Bio-Inspired Robots and Structures toward Fostering the Modernization of Agriculture

Abstract

Biomimetics is the interdisciplinary cooperation of biology and technology that offers solutions to practical problems by analyzing biological systems and transferring their principles into applications. This review article focused on biomimetic innovations, including bio-inspired soft robots and swarm robots that could serve multiple functions, including the harvesting of fruits, pest control, and crop management. The research demonstrated commercially available biomimetic innovations, including robot bees by Arugga AI Farming and the Robotriks Traction Unit (RTU) precision farming equipment. Additionally, soft robotic systems have made it possible to mitigate the risk of surface bruises, rupture, the crushing destruction of plant tissue, and plastic deformation in the harvesting of fruits with a soft rind such as apples, cherries, pears, stone fruits, kiwifruit, mandarins, cucumbers, peaches, and pome. Even though the smart farming technologies, which were developed to mimic nature, could help prevent climate change and enhance the intensification of agriculture, there are concerns about long-term ecological impact, cost, and their inability to complement natural processes such as pollination. Despite the problems, the market for bio-inspired technologies with potential agricultural applications to modernize farming and solve the abovementioned challenges has increased exponentially. Future research and development should lead to low-cost FEA robotic grippers and FEA-tendon-driven grippers for crop harvesting. In brief, soft robots and swarm robotics have immense potential in agriculture.

Full text

Citation: Kondoyanni, M.;
Loukatos, D.; Maraveas, C.;
Drosos, C.; Arvanitis, K.G.
Bio-Inspired Robots and Structures
toward Fostering the Modernization
of Agriculture. Biomimetics 2022,7, 69.
https://doi.org/10.3390/
biomimetics7020069
Academic Editor: Stanislav N. Gorb
Received: 4 May 2022
Accepted: 25 May 2022
Published: 29 May 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
biomimetics
Review
Bio-Inspired Robots and Structures toward Fostering the
Modernization of Agriculture
Maria Kondoyanni 1, Dimitrios Loukatos 1, * , Chrysanthos Maraveas 1, Christos Drosos 2
and Konstantinos G. Arvanitis 1
1Department of Natural Resources Management and Agricultural Engineering,
Agricultural University of Athens, 75 Iera Odos Str., Botanikos, 11855 Athens, Greece;
mkondoyanni@aua.gr (M.K.); maraveas@aua.gr (C.M.); karvan@aua.gr (K.G.A.)
2Department of Industrial Design and Production Engineering, University of West Attica,
250 Thivon & P. Ralli Str., 12241 Egaleo, Greece; drososx@uniwa.gr
*Correspondence: dlouka@aua.gr; Tel.: +30-210-5294-109
Abstract:
Biomimetics is the interdisciplinary cooperation of biology and technology that offers
solutions to practical problems by analyzing biological systems and transferring their principles
into applications. This review article focused on biomimetic innovations, including bio-inspired
soft robots and swarm robots that could serve multiple functions, including the harvesting of fruits,
pest control, and crop management. The research demonstrated commercially available biomimetic
innovations, including robot bees by Arugga AI Farming and the Robotriks Traction Unit (RTU)
precision farming equipment. Additionally, soft robotic systems have made it possible to mitigate the
risk of surface bruises, rupture, the crushing destruction of plant tissue, and plastic deformation in the
harvesting of fruits with a soft rind such as apples, cherries, pears, stone fruits, kiwifruit, mandarins,
cucumbers, peaches, and pome. Even though the smart farming technologies, which were developed
to mimic nature, could help prevent climate change and enhance the intensification of agriculture,
there are concerns about long-term ecological impact, cost, and their inability to complement natural
processes such as pollination. Despite the problems, the market for bio-inspired technologies with
potential agricultural applications to modernize farming and solve the abovementioned challenges
has increased exponentially. Future research and development should lead to low-cost FEA robotic
grippers and FEA-tendon-driven grippers for crop harvesting. In brief, soft robots and swarm robotics
have immense potential in agriculture.
Keywords:
biomimetic; agriculture 4.0; intelligent materials; bio-inspired; IoT; machine learning; robotics
1. Introduction
The term biomimetic in agriculture denotes the unique ability to interlock artificial and
natural systems without altering the function and ecosystems of wild species by mimicking
nature [1]
; this is achieved through the adoption of an interdisciplinary approach in the
development of machines, systems, and materials that are inspired by biological processes
for scientific, engineering, and medical applications [
2
]. Even though research and develop-
ment in biomimicry are new, the history of biomimicry can be traced back to Leonardo da
Vinci’s flying machine, whose design was inspired by birds. Such inspirations led to the
development of the first commercial aircraft, bionic car concepts, animal-shaped robots,
and architectural techniques adapted to nature [
2
]. That the recent biomimetic synthesis of
biomaterials for agriculture remains one of the most critical applications of biomimetics
in agriculture is a case in point [
3
,
4
]. The biomaterials have a wide array of potential
applications that range from hydrogels for water storage, carbon capture, biodegradable
materials for greenhouses, antimicrobial packaging for fruits and vegetables [
5
–
7
], and
sustainable agriculture. This review focuses on soft robotics and swarm robotics, which
hold great promise in harvesting, plant management, and seeding [
8
–
11
]. The emphasis on
Biomimetics 2022,7, 69. https://doi.org/10.3390/biomimetics7020069 https://www.mdpi.com/journal/biomimetics

Biomimetics 2022,7, 69 2 of 31
soft robotics and robot-animal artifacts is expected to translate to a nuanced understanding
of the benefits, technological challenges, and prospects. Such information would help guide
decision-making among smallholder and large commercial farmers. Consumer demand
would be a catalyst for industrial research and development.
Biomimetic-related innovations hold great promise in ecological system-design
models [1]
for the intelligent mitigation of soil degradation, the conservation of biological systems,
and the conceptualization of smart farming technologies [
12
]. Smart farming technologies
have been proven to reduce production costs and improve yields through the intelligent
regulation of humidity, irrigation, frost, greenhouse microclimate, pesticide, and fertilizer
applications [7,13,14]
. Beyond production, biomimetics has practical applications in elec-
tronics (which mimic light-reflective butterfly wing structures), eco-friendly packaging, and
the production of laboratory-grown meat [
15
]. The list is not exhaustive considering new
applications for biomimetic-related applications are emerging with advances in research.
Recent reports suggested that the biomimetic agriculture industry would grow expo-
nentially and contribute to global economic development. On average, biomimetic-related
innovations would contribute at least $1.5 trillion to the global domestic product [
15
].
However, the latter contribution would have a lesser adverse effect on local ecosystems
because the biomimetic revolution learns and mimics nature; this is in contrast to the 19th-
century industrial revolution, which exploited nature [
1
,
12
,
15
]. The unique contributions
of biomimetics to agriculture are depicted in Figure 1. In light of the diverse potential
contributions of biomimetics to agriculture, the review article focused on applications in
commercial agriculture, particularly smart and intelligent IoT-mediated farming in Europe,
the Middle East, Asia, and North America.
Biomimetics 2022, 7, x FOR PEER REVIEW 2 of 33
ture, biodegradable materials for greenhouses, antimicrobial packaging for fruits and veg-
etables [5–7], and sustainable agriculture. This review focuses on soft robotics and swarm
robotics, which hold great promise in harvesting, plant management, and seeding [8–11].
The emphasis on soft robotics and robot-animal artifacts is expected to translate to a nu-
anced understanding of the benefits, technological challenges, and prospects. Such infor-
mation would help guide decision-making among smallholder and large commercial
farmers. Consumer demand would be a catalyst for industrial research and development.
Biomimetic-related innovations hold great promise in ecological system-design mod-
els [1] for the intelligent mitigation of soil degradation, the conservation of biological sys-
tems, and the conceptualization of smart farming technologies [12]. Smart farming tech-
nologies have been proven to reduce production costs and improve yields through the
intelligent regulation of humidity, irrigation, frost, greenhouse microclimate, pesticide,
and fertilizer applications [7,13,14]. Beyond production, biomimetics has practical appli-
cations in electronics (which mimic light-reflective butterfly wing structures), eco-friendly
packaging, and the production of laboratory-grown meat [15]. The list is not exhaustive
considering new applications for biomimetic-related applications are emerging with ad-
vances in research.
Recent reports suggested that the biomimetic agriculture industry would grow ex-
ponentially and contribute to global economic development. On average, biomimetic-re-
lated innovations would contribute at least $1.5 trillion to the global domestic product
[15]. However, the latter contribution would have a lesser adverse effect on local ecosys-
tems because the biomimetic revolution learns and mimics nature; this is in contrast to the
19th-century industrial revolution, which exploited nature [1,12,15]. The unique contribu-
tions of biomimetics to agriculture are depicted in Figure 1. In light of the diverse potential
contributions of biomimetics to agriculture, the review article focused on applications in
commercial agriculture, particularly smart and intelligent IoT-mediated farming in Eu-
rope, the Middle East, Asia, and North America.
Figure 1. Unique attributes of biomimetics and contributions to agriculture and nature conservation
[12].
Biomimetic research and development by academia and industry would have a tan-
gible impact on global food security. The focus on innovation to address global food se-
curity was justified by the scale of the challenge. According to the WHO, 12% of the global
Biomimetic
Appreciates
limits of
human
monitoring Self-
regulation,
self-
healing/self-
repairing and
adaptability
Self-
perseveranc
e as self-
constitution
No immitation
of aesthetic
forms
Identity of
natural
entities
Open to new
configurations
Figure 1.
Unique attributes of biomimetics and contributions to agriculture and nature
conservation [12]
.
Biomimetic research and development by academia and industry would have a tangi-
ble impact on global food security. The focus on innovation to address global food security
was justified by the scale of the challenge. According to the WHO, 12% of the global
population is food insecure, and about one in three persons (2.37 billion) lacks access to
adequate food [
16
]. Future projections indicate that 660 million persons will experience
hunger by 2030 [
17
]. The projected food insecurity must be addressed because the global
population will increase by 10% [
18
]. Higher demand for agricultural produce translates

Biomimetics 2022,7, 69 3 of 31
to a concomitant increase in price and inflation, which might translate to further food
insecurity [18]
. Drawing from the current state of food insecurity post-COVID-19 [
19
]
and future projections, concerted measures are necessary to reverse the current trend of
food insecurity.
Practical interventions include agriculture 4.0, IoT, and biomimetic agriculture. This
review focuses on the potential of the latter, given the former was extensively reviewed
by [20–28]
. The link between global food security and biomimetic research is grounded
on the progress made using biomimetic approaches to address water scarcity, using in-
expensive and scalable Warka and water cone water from the
atmosphere [28]
. Addi-
tionally, biomimetic approaches have led to the development of biomimetic phosphate
scavengers via quantum chemical studies of phosphate anions on small, intrinsically disor-
dered
peptides [29]
. The latter study demonstrated that it was possible to improve global
food security using phosphate scavengers while minimizing the negative effect of phos-
phate compounds on the environment. The biomimetic-related innovations highlighted by
Othmani et al. [28]
and
Gruber et al. [29]
were but a microcosm of the various innovations
that could transform the future of agriculture.
The current research focuses on two interrelated biomimetic systems, namely swarm and
soft robotics [
30
,
31
]. On the one hand, soft robotics was designed to achieve specific functions
such as harvesting fragile fruits and vegetables, seeding, and crop
management [30,32–35]
.
On the other hand, swarm intelligence is the collective behavior of undistributed, self-
organizing physical or artificial systems. Applying swarm principles to robots is called
swarm robotics, where robots aim to mimic natural swarms, such as ants and birds, to form
a scalable, flexible, and robust system. Flocks may be considered as a type that can adapt to
the environment’s changes and follows specific behavior [
2
,
36
,
37
], such as the fulfillment of
objectives, aggregation or dispersing, communicating, and memorizing. Similarly, swarm
robots exhibit autonomy, cooperation, and coordination, which are necessary for long-
term applications.
1.1. Global Agricultural Challenges and Need for Biomimetic Innovations
Global agriculture has been constrained by many factors, including biophysical and
socioeconomic issues, including climate change [
38
,
39
]. Climate change is a catalyst for
desertification and diminished crop yields attributed to the depletion of vital nutrients
in agricultural lands [
38
,
39
]. The United Nations Convention to Combat Desertification
(UNCCD) estimated that 1.6–3.3 million ha of farmland would be lost annually due to
urbanization. The pattern is anticipated to persist between 2000 and 2030 [
40
]. Other
studies estimated that about 23 ha are lost per minute due to climate change-induced
desertification [
41
]. Empirical evidence suggests that traditional interventions could not
suffice because they had not helped to address the problem from the onset. Traditionally,
commercial farms and smallholder farmers had sought to address global food insecurity
through the intensification of farming. For example, the global production of essential
foods such as cereals has surged by >200% compared to the 1960s [
42
]. From an economic
perspective, there were practical challenges with this intervention. First, the intensification
of agriculture did not translate to greater adoption of sustainable measures such as irrigation
to save water; IPCC’s special report corroborated these claims. As of 2015, only 2% of
global croplands were irrigated [
42
]. The limited effectiveness of the traditional measures
reinforced the need for climate-smart agriculture and biomimetic innovations.
Despite the compelling evidence supporting the transition, critics have argued that
developing countries, including those in Sub-Sahara Africa, are less equipped for the
transition to agriculture 4.0 [
43
]. Additionally, new research questions the development and
rollout of robot bees that mimic natural insect/bee pollinators. In place of creating robotic
free-flying bees and insects to facilitate pollination, it would be much more appropriate to
restore natural ecosystems and “create environments that are friendly to bees and exploring
the use of other species for pollination and bio-control” [
10
]. The observation represents one
of many ethical dilemmas agricultural experts, scientists, and engineers face as they seek

Biomimetics 2022,7, 69 4 of 31
to optimize crop yields. The essence of equipping developing countries for the transition
remains unclear, especially in the case of soft robotics. The argument is premised on the
fact that soft robotics and seeding/planting equipment seek to address labor shortages
inherent in Europe [35].
In contrast, developing nations in Africa have a critical mass of unemployed
youth [44–48]
.
Drawing from the employment dynamics in the global north and south, labor shortage
is not a sufficient incentive for the transition to soft robots and robot insects. However,
efficiency in cultivation and harvesting is compelling.
The transition to agriculture 4.0 is necessary for developing nations because it offers
unlimited potential by adopting robotic arms for harvesting and seeding, precision tractors,
drones for the detection and location of fruits using UAV images [
47
], land tilling, fertilizer,
and pesticide applications [
49
–
51
], and decision support systems based on machine learning
techniques for tree health monitoring and fruit diseases classification [
52
–
54
]. The use of
precision tractors serves multiple functions. First, it provides a practical solution to the
labor shortage. Second, it reduces greenhouse gas emissions [
49
,
50
]. Third, the precision
tractors are integral to intelligent and precision planting using tractors fitted with soft
robots and guided by LIDAR (light/laser detection and ranging) or a global navigation
satellite system [
34
,
55
]. The Robotics Traction Unit (RTU), which retails at £ 7000, is a
case in point [
56
]. The RTU serves multiple functions, including harvesting and crop
monitoring. Even small autonomous robotic systems can assist the harvesting process
and be combined with recognition systems using deep learning for the fast and precise
detection and collection of fruits [
57
]. The extent of preparedness is reviewed in the next
section, emphasizing soft robotics and swarm robotics.
1.2. Review Framework
A rigorous review framework was adopted in the preparation of this review paper. The
review process was aligned with the PRISMA guidelines for systematic reviews and meta-
analyses (see Figure 2). The majority of data were sourced from published articles. More
specifically, the peer-reviewed data were sourced from the following primary databases:
MDPI, Elsevier, Springer, Wiley, and Taylor and Francis. Secondary sources were also
taken into account, such as government reports published by the USDA and European
Commission and industry stakeholders, including Boston Dynamics, Arugga AI, and Bird
Gard Australia. The literature focuses on recent developments in biomimetics research and
potential applications in agriculture. The articles were selected using keywords such as
bio-inspired, biomimetics, biomimicry, agriculture, smart agriculture, solar panels, tropism,
soft robotics, swarm robotic structures, and materials. The research was mainly focused on
English-published papers to avoid translation delays. The publication period was between
2005 and 2022. The search window was justified considering it was necessary to understand
how biomimicry has evolved over the years and how it can be applied in agriculture. The
inclusion and exclusion criteria were characterized by a title and abstract screening followed
by a full-text screening process, which focused on the relevance of the subject. Part of the
grey literature was excluded because it did not satisfy the inclusion criteria.
The reviewed articles were grouped into three categories: firstly, biomimetic inno-
vations, including soft robots, swarm robotics, and other intelligent systems with broad
applications in agriculture. The second and third classifications included biomimetic mate-
rials and resource management. The categorization followed a logical order of increasing
complexity, starting from the natural resources necessary for the crops, continuing to
the materials that are important for the construction of various farming uses, and finally
mentioning more complex mechanisms that facilitate the processes. The bio-inspired tech-
nologies applied in resources management were divided into sub-categories, including
solar energy harvest and water preservation.

Biomimetics 2022,7, 69 5 of 31
Biomimetics 2022, 7, x FOR PEER REVIEW 5 of 33
Figure 2. Diagram of the literature source selection process.
The reviewed articles were grouped into three categories: firstly, biomimetic innova-
tions, including soft robots, swarm robotics, and other intelligent systems with broad ap-
plications in agriculture. The second and third classifications included biomimetic mate-
rials and resource management. The categorization followed a logical order of increasing
complexity, starting from the natural resources necessary for the crops, continuing to the
materials that are important for the construction of various farming uses, and finally men-
tioning more complex mechanisms that facilitate the processes. The bio-inspired technol-
ogies applied in resources management were divided into sub-categories, including solar
energy harvest and water preservation.
2. Biomimetic Innovations and Climate-Smart Agriculture
2.1. Intelligent Systems Connectivity and Cost
The introduction of IoT infrastructures greatly assists in making agricultural pro-
cesses more efficient and accurate. These systems may utilize technologies from conven-
tional cellular and Wi-Fi to long-range and low-rate radio transceivers [58]. The selection
of components depends directly on the type of application and the information needed to
be transferred and should be addressed meticulously. Even though soft robotics and
swarm intelligence offer unique advantages compared to traditional systems, integration
in farms is often impacted by cost and the lack of intelligent system connectivity [59,60].
Even though new research and development projects have attempted to address the prob-
lem, the issue has persisted. The problem is multidimensional, considering that intelligent
systems are energy-intensive. A recent study estimated that the energy expenditure for
large farms could be 65,891.5–151,220.6$ per year [61]. Such costs are unsustainable, con-
sidering farmers expect to achieve net savings of about $500/acre [62]. Based on the high
costs of operating intelligent systems on farms, smallholder farmers are disadvantaged
compared to large farms, which enjoy better economies of scale.
The current research and development projects show that the technology and cost-
related barriers to the adoption of biomimetic technologies could be addressed with time
[2,12,28,29,63,64]. If existing R&D projects are successfully commercialized, swarm robot-
ics could be employed on a broader scale to solve pressing challenges on farms. For ex-
ample, efficiency, cost reduction, and the optimization of crop production have become a
priority [13,65–67]. However, big data in decision-making remains a challenge [36]. The
Primary sources: MDPI,
Elsevier, Springer, ACS,
Wiley, Taylor & Francis
(n=315)
Secondary sources:
Government reports,
Industry, HAL Archives-
Ouvertes (n=120)
Number of publications after sorting and
removing duplicates (n=430)
Number of articles after title and abstract
screening (n=430)
Number of articles excluded after
full-text screening and further
criteria (n=219)
Peer reviewed articles sited
(n=211)
Figure 2. Diagram of the literature source selection process.
2. Biomimetic Innovations and Climate-Smart Agriculture
2.1. Intelligent Systems Connectivity and Cost
The introduction of IoT infrastructures greatly assists in making agricultural processes
more efficient and accurate. These systems may utilize technologies from conventional
cellular and Wi-Fi to long-range and low-rate radio transceivers [
58
]. The selection of
components depends directly on the type of application and the information needed to be
transferred and should be addressed meticulously. Even though soft robotics and swarm
intelligence offer unique advantages compared to traditional systems, integration in farms
is often impacted by cost and the lack of intelligent system connectivity [
59
,
60
]. Even
though new research and development projects have attempted to address the problem, the
issue has persisted. The problem is multidimensional, considering that intelligent systems
are energy-intensive. A recent study estimated that the energy expenditure for large
farms could be 65,891.5–151,220.6$ per year [
61
]. Such costs are unsustainable, considering
farmers expect to achieve net savings of about $500/acre [
62
]. Based on the high costs of
operating intelligent systems on farms, smallholder farmers are disadvantaged compared
to large farms, which enjoy better economies of scale.
The current research and development projects show that the technology and cost-
related barriers to the adoption of biomimetic technologies could be addressed with
time [
2
,
12
,
28
,
29
,
63
,
64
]. If existing R&D projects are successfully commercialized, swarm
robotics could be employed on a broader scale to solve pressing challenges on farms. For
example, efficiency, cost reduction, and the optimization of crop production have become a
priority [
13
,
65
–
67
]. However, big data in decision-making remains a challenge [
36
]. The
type of data that each farmer needs depends on the type of production. Water consump-
tion, soil fertilizer levels, weather conditions, and crop growth are some useful data for
a farmer. Combining all these data with artificial intelligence, farmers receive practical
knowledge that gives them real-time updates. However, the use of many sensors is ham-
pered by several factors related to connectivity. Commercial farms in developing countries
and remote regions have inadequate access to the internet and infrastructure required
to support intelligent machines; this explains why most innovations are concentrated in
developed countries [
66
–
72
]. Wired sensors are less efficient than wireless sensors, which
can automatically relay signals remotely. Refs. [
73
–
76
]. Mobile-based connectivity to the
sensor network is less sustainable considering the high-power consumption and initial cost
of the infrastructure.
Biomimetic innovations in the agricultural sector cannot be considered outside the
context of support technologies such as precision tractors [50,55], IoT [75–79], and LIDAR

Biomimetics 2022,7, 69 6 of 31
(light/laser detection and ranging) or global navigation satellite system [
34
,
55
]. The multi-
dimensional view is grounded in the fact that soft robots and swarm intelligence cannot
operate autonomously without the supporting infrastructure [
80
]. The worldviews ad-
vanced by Duckett et al. [
80
] were in agreement with Rial-Lovera [
35
], who claimed that
real-time kinematics, GPS, actuators, specialized sensors, and interfaces were enablers
for automation systems, particularly robots in agriculture. The integration of IoT and AI
was evident in developing the first soft robot by researchers from Harvard University
working in collaboration with Defense Advanced Research Projects Agency (DARPA) [
33
].
Drawing from past trends, future advances in soft robotics, swarm robotics, and biomimetic
innovations, in general, would be predicted by developments in IoT and cloud computing,
data storage, and big data, as illustrated in Figure 3[
81
]. Beyond the disruptive technology,
consumer attitudes and the ability to rapidly deploy the innovations would predict the rate
at which soft robotic grippers replaced handpicking and mechanical harvesters.
Biomimetics 2022, 7, x FOR PEER REVIEW 6 of 33
type of data that each farmer needs depends on the type of production. Water consump-
tion, soil fertilizer levels, weather conditions, and crop growth are some useful data for a
farmer. Combining all these data with artificial intelligence, farmers receive practical
knowledge that gives them real-time updates. However, the use of many sensors is ham-
pered by several factors related to connectivity. Commercial farms in developing coun-
tries and remote regions have inadequate access to the internet and infrastructure re-
quired to support intelligent machines; this explains why most innovations are concen-
trated in developed countries [66–72]. Wired sensors are less efficient than wireless sen-
sors, which can automatically relay signals remotely. Refs. [73–76]. Mobile-based connec-
tivity to the sensor network is less sustainable considering the high-power consumption
and initial cost of the infrastructure.
Biomimetic innovations in the agricultural sector cannot be considered outside the
context of support technologies such as precision tractors [50,55], IoT [75–79], and LIDAR
(light/laser detection and ranging) or global navigation satellite system [34,55]. The mul-
tidimensional view is grounded in the fact that soft robots and swarm intelligence cannot
operate autonomously without the supporting infrastructure [80]. The worldviews ad-
vanced by Duckett et al. [80] were in agreement with Rial-Lovera [35], who claimed that
real-time kinematics, GPS, actuators, specialized sensors, and interfaces were enablers for
automation systems, particularly robots in agriculture. The integration of IoT and AI was
evident in developing the first soft robot by researchers from Harvard University working
in collaboration with Defense Advanced Research Projects Agency (DARPA) [33]. Draw-
ing from past trends, future advances in soft robotics, swarm robotics, and biomimetic
innovations, in general, would be predicted by developments in IoT and cloud compu-
ting, data storage, and big data, as illustrated in Figure 3 [81]. Beyond the disruptive tech-
nology, consumer attitudes and the ability to rapidly deploy the innovations would pre-
dict the rate at which soft robotic grippers replaced handpicking and mechanical harvest-
ers.
Figure 3. Relationship between disruptive technology drivers in the agricultural domain [81].
Figure 3. Relationship between disruptive technology drivers in the agricultural domain [81].
The first subsection explores the cost benefits of soft robotic systems in agriculture.
Based on the current body of knowledge, biomimetic innovations are a prerequisite for
large-scale sustainable agriculture [
34
,
35
,
49
,
55
,
80
]; this has been demonstrated in advanced
economies such as Australia, where commercial agriculture had led to optimal land-use—
about 20% more land compared to traditional tractors. A study conducted by the University
of New South Wales [
55
] observed that precision tractors replaced the traditional manual
tractors, which were expensive and less effective considering they compacted the soil
and created crop lines and large paddocks, which made 20% of the land unusable and
contributed to the degradation of the land in the long-term; this problem can be significantly
reduced using precision tractors.
2.2. Soft Robotics in Commercial Harvesting
Soft robotics is an emerging class of robots that easily adapt their form and shape
to external obstacles and constraints and can easily deform (see Figure 4) [
8
]. In contrast
to traditional rigid machines, soft robots have unique soft-touch capabilities, which are

Biomimetics 2022,7, 69 7 of 31
desirable for weeding, irrigation, fruit picking, and seeding [
34
,
80
,
82
]. The soft gripper
robotic arm designed for a robotic agricultural harvester by researchers in Spain was
comparable to a soft robotic arm developed by Chinese researchers. In the latter case, the
soft robotic gripper mimicked the human hand—it relied on a simple control scheme with
infinite degrees of freedom that enabled shape and size changes that matched the load in a
wide range [
83
]. Despite the intelligent use of materials and systems, soft robotics is not
immune to the mechanical damage of fruits and vegetables. The observation was in line
with [84], who documented fruit damage at high grasp force and pressure.
Biomimetics 2022, 7, x FOR PEER REVIEW 7 of 33
The first subsection explores the cost benefits of soft robotic systems in agriculture.
Based on the current body of knowledge, biomimetic innovations are a prerequisite for
large-scale sustainable agriculture [34,35,49,55,80]; this has been demonstrated in ad-
vanced economies such as Australia, where commercial agriculture had led to optimal
land-use—about 20% more land compared to traditional tractors. A study conducted by
the University of New South Wales [55] observed that precision tractors replaced the tra-
ditional manual tractors, which were expensive and less effective considering they com-
pacted the soil and created crop lines and large paddocks, which made 20% of the land
unusable and contributed to the degradation of the land in the long-term; this problem
can be significantly reduced using precision tractors.
2.2. Soft Robotics in Commercial Harvesting
Soft robotics is an emerging class of robots that easily adapt their form and shape to
external obstacles and constraints and can easily deform (see Figure 4) [8]. In contrast to
traditional rigid machines, soft robots have unique soft-touch capabilities, which are de-
sirable for weeding, irrigation, fruit picking, and seeding [34,80,82]. The soft gripper ro-
botic arm designed for a robotic agricultural harvester by researchers in Spain was com-
parable to a soft robotic arm developed by Chinese researchers. In the latter case, the soft
robotic gripper mimicked the human hand—it relied on a simple control scheme with
infinite degrees of freedom that enabled shape and size changes that matched the load in
a wide range [83]. Despite the intelligent use of materials and systems, soft robotics is not
immune to the mechanical damage of fruits and vegetables. The observation was in line
with [84], who documented fruit damage at high grasp force and pressure.
Figure 4. Soft gripper robotic arm designed in Spain for robotic agricultural harvesters [8].
Navas et al. [8] and Yan et al. [83] concurred that soft robotic arms were better than
traditional mechanical robots, vegetable and fruit grippers with rigid or underactuated
grippers, which lacked precision control and often damaged the fruits. Mechanical dam-
age to fruits and vegetables assumed different forms, such as surface bruises, rupture,
crushing, plant tissue destruction, and plastic deformation. The probability of mechanical
damage is higher in apples, cherries, pears, stone fruits, kiwifruit, mandarins, cucumbers,
peaches, pome, and other fruits with a soft rind [84,85]. Integrating a bio-inspired robotic
Figure 4. Soft gripper robotic arm designed in Spain for robotic agricultural harvesters [8].
Navas et al. [
8
] and Yan et al. [
83
] concurred that soft robotic arms were better than
traditional mechanical robots, vegetable and fruit grippers with rigid or underactuated
grippers, which lacked precision control and often damaged the fruits. Mechanical damage
to fruits and vegetables assumed different forms, such as surface bruises, rupture, crushing,
plant tissue destruction, and plastic deformation. The probability of mechanical damage is
higher in apples, cherries, pears, stone fruits, kiwifruit, mandarins, cucumbers, peaches,
pome, and other fruits with a soft rind [
84
,
85
]. Integrating a bio-inspired robotic arm with
high precision control mitigates the risk of fruit and vegetable damage during harvesting.
Drawing from the evidence presented in Table 1, soft grippers are either fluidic
elastomer actuators (FEAs) or FEA-tendon, primarily because these two technologies are
best suited for agriculture because they rely on soft actuators to regulate the grip force.
From an abstract perspective, the FEA is a suitable alternative considering it relies on
affordable materials, which are easy and simple to manufacture. In addition, the grip
strength of the FEA is appropriate for different types of fruits and vegetables, as shown in
Table 1. Similarly, the FEA-tendon-driven soft gripper has a desirable payload to weight
ratio of 7 kg and can lift a weight of 27 kg [
86
]. The constraints associated with FEA robotic
grippers could be addressed by optimizing the gripper type, size, lifting ratio, scalability
and controllability, response time, and surface conditions, among other parameters [
86
].
Considering the technology was nascent, optimized soft gripper systems are expensive and
out of reach for smallholder farmers; this remains one of the key limitations of biomimetic
innovations [
87
]. Other challenges include the prospecting of a suitable biomimicry pattern,
relevance to the problem, information accessibility, and implementation on a broader scale.

Biomimetics 2022,7, 69 8 of 31
Table 1.
Soft gripper technology, gripper type, size, lifting ratio, scalability and controllability,
response time, and surface conditions [86].
Soft
Technology
Grasped
Object
Object Size
or Weight
Gripper
Type Gripper Size Lifting
Ratio
Controllability
/Scalability
Response
Time
Surface
Condition
FEAs
Lettuce 250 ×250 mm
Two pneumatic
actuators and a
blade
8000 g,
450 ×450 ×300 mm -
Close-loop
with force
sensor
feedback/Yes
31.7 s -
Apple - Three soft finger
design
Two fingers length:
95.25 mm
One Finger
length: 152.4 mm
- Open-loop/- 7.3 s -
Mushroom -
Three soft
chambers in
circular shell
Chamber height:
20 mm
Chamber arc
angle: 60o
30 -/Yes - Any
surface
Apple, Tomato,
Carrot,
Strawberry
69 mm, 5–150 g Magnetorheological
gripper - - PID/- 0.46 s Any
surface
Cupcake liners
filled
with peanuts
34–64 g Three soft finger
design
Finger size:
82 ×16 ×15 mm -FE
Analysis/Yes - -
Cupcake liners
filled
with red beans,
higiki, ohitashi
75.2 g Soft fingers Finger length:
97 mm 1805% Open-
Loop/Yes
10 s pick
and place
(total
procedure)
-
Defrosted
broccoli
33.54 ×23.94 mm,
3.8–7.0 g Two soft fingers Actuator size:
50 ×20 mm - -/- 3 s for
inflation -
Granular
kernel corn,
Chopped green
onion,
Boiled hijiki
0.77–26.6 g Four soft fingers Finger size:
43 ×61.5 mm -Open-
Loop/Yes -Any
surface
Orange 1000 g Soft fingers Finger size:
95 ×20 ×18 mm -Open-
Loop/Yes -Any
surface
Tomato,
Kiwifruit,
Strawberry
45–76 mm
Four soft
chambers
in circular shell
Internal diameter:
46 mm
Height: 30 mm
-Open-
Loop/Yes 2–5 s Any
surface
Tendon-
driven
Tomato 500 g Three soft finger
design - -
Preprogrammed
rotation of
motors /Yes
- -
Tomato,
Cucumber
(slices) Avocado
(Strips)
Cherry Tomato,
Olives,
Pineapples
cubes,
Broccoli
-Quad-Spatula
design - - -/Yes - Flat
surfaces
FEA-
Tendon-
driven
Banana, Apple,
Grapes 2700 g
Three soft finger
design with a
suction cup
389.69 g 7.06 Teleoperation
Control/Yes
0.094 s
(Rise time)
Any
surface
Topology
optimized
soft
actuators
Apple,
Grapefruit,
Guava, Orange,
Kiwifruit
1499 g Two compliant
fingers - - Open-loop
(Arduino)/Yes - -
Conservative estimates suggest that mechanical damage reduces the orange fruit qual-
ity; this is evident from the 5–11% lower ascorbic acid, titratable acidity, and soluble solids
levels compared to undamaged fruits [
88
]. The post-harvest losses linked to mechanical
damage have serious economic implications. For example, in the UK and the US, the cost of
harvesting and post-harvest losses are between 18 and 24 billion pounds [
89
]. The volume
of post-harvest losses could reduce with time, considering that the UK is making com-
mendable investments in soft robotics and autonomous systems [
80
], making it a pioneer
in intelligent technologies to reduce the demand for human labor and enhance production
efficiency. For example, deploying the RTU eliminated the need for expensive human labor
while minimizing the risk of errors [
56
]. On the downside, investing in soft robotics in
isolation might not be a long-term solution because of other contributing factors. Reducing
the risk of fruit and vegetable damage must consider that damage during harvesting re-
mains a challenge considering there are other contributing factors, including the storage
temperature, microbial infections, high temperatures, and relative humidity [
85
,
88
]. In

Biomimetics 2022,7, 69 9 of 31
light of the latter challenges, one can argue that the intelligent control of the grip force and
pressure in robotic grippers alone could not eliminate the risk of mechanical damage. The
storage and transportation conditions must satisfy the requirements.
Fruit and vegetable picking grippers are an important technology to achieve rapid and
labor-saving harvest. However, most of the existing fruit and vegetable picking grippers
still use traditional rigid or underactuated grippers, which often cause fruit and vegetable
damage by the heavy mass and lack of high-precision control, and have poor compliance
in the operation process [
55
,
82
–
85
]. In recent years, inspired by soft creatures’ tentacles,
soft robotic grippers have appeared and been used in robotics due to the emergence of
soft robots. The soft robotic gripper is made of flexible material integrated into a smart
autonomous system that regulates the grip force and pressure. The risk of mechanical
damage using mechanical harvesters versus soft robots is illustrated in Table 2. Following
the comparative analysis of mechanical harvesters, handpicking and robotic systems, there
was a lower risk of fruit damage in the latter.
Table 2.
Comparative analysis of fruit damage using mechanical harvesters, robotic grippers, and
handpicking [84].
Cultivar Harvest Method Key Observations
Blueberry Commercial mechanical
harvester
Three out of four mechanically harvested blueberries were severely bruised and
damaged by the commercial mechanical harvester.
Handpicking Nearly one in four hand-harvested blueberries had noticeable bruise damage.
Apple Shake-and-catch
harvesting system At least eight percent of the three cultivars led to fruit bruises.
Robotic picking using
a three-finger gripper
If the robotic finger gripper’s grasping pressure and force are properly
programmed, the risk of mechanical damage is reduced. Significant bruising of
apples (46–60% of the harvest) was observed at higher grasping forces (14.5 to
15.9 N) 46.7% and grasping pressure (0.28 and 0.29 MPa). Based on the data,
proper adjustment of the pressure and force is essential to minimize
fruit damage
.
Handpicking
The risk of severe bruise damage on plants was mitigated if the average grasping
force (5.05 N) and grasping pressure (0.24 MPa) were maintained at 5 N and
0.24 MPa. However, it is challenging for human hands to exert constant pressure
and force during the entire harvesting process; bruise damage is unavoidable in
handpicking fruits and vegetables.
Table olive Manual picking Manual picking by hand was responsible for 17.5–51% of the severe
bruise damage.
Trunk shaking harvester There was a 62–77% % risk of damage if the farmers used mechanical
trunk shakers.
Grape straddle harvester The risk of bruising damage was the highest, at between 91% and 100%.
Prune
Straddle mechanical harvester
Less than 10% of the prunes harvested using mechanical techniques showed signs
of bruise damage.
Handpicking ∼50% bruise damage
Plum
Straddle mechanical harvester
∼18% of the plums showed some bruise damage.
Preliminary research conducted by Chowdhary et al. (2019) showed that soft robotics
(small bots with soft arms) could perform a wide range of complex tasks on farms. The
commercial rollout of the soft robots is feasible, considering the robots are affordable and
effective compared to the traditional hard robots [
8
]. Despite the huge potential of soft
robotics in agriculture, the level of adoption remains low, and most projects are in the pilot
phase, such as the swarm robotics for agricultural applications (SAGA) project [
90
] and
Soft Robotics LLC’s SoftAI
™
powered robotic solution, which combines AI, 3D vision, and
IP69K-rated soft grasping technology [
91
]. According to Soft Robotics LLC, the robots have

Biomimetics 2022,7, 69 10 of 31
comparable hand-eye coordination to humans [
91
]. On the downside, there is minimal
data from commercial companies that have adopted the technology.
The inadequate commercialization of the technology could be linked to the newness
of the technology. As of 2021, Soft Robotics LLC sought Series B funding from investors to
enhance operations. From a theoretical point of view, the limited uptake of soft robots could
mirror earlier reservations about the adoption of precision agriculture across
Europe [49]
.
During the 1990s, the adoption of ICTs, particularly precision agriculture, was confined
to Canada and the US, primarily because there were large smallholder farms with prop-
erly organized extension services by university research teams and government extension
officers [49]
. In addition, the investments in precision agriculture at the time were medi-
ated by consumers’ willingness to explore new technologies, higher incomes, access to
financial resources and investments, and subsidies. On the downside, the positive growth
in precision agriculture decreased in the early 2000s due to many challenges. First, certain
technology systems were incompatible across brands, and consumers expressed reserva-
tions about the ease of use and maintenance, fuel use, and projected productivity. The
demand for precision agriculture has resurged with the demand for sustainable agriculture
and the judicious use of fertilizers, pesticides, and water.
The problems experienced in the transition toward precision agriculture are compara-
ble to the current scenario. Leading soft robotic system manufacturers have experienced
significant challenges transitioning from laboratory to commercial adoption [
30
]. The clo-
sure of Empire Robotics—a market leader in soft robotics—is a case in point. Despite spend-
ing significant resources on R&D and releasing a revolutionary product, VERSABALL
®
,
the company was unable to achieve a sustainable business model [
30
]. The case is but a
microcosm of the challenges faced by companies in soft robotic systems. The challenge
could be attributed to unique market dynamics, such as a new value chain mediated
by data analytics, robotic machines, aerial imagery, and disruptive technology-mediated
agrifood-technology e-business models [
81
,
92
]. Business theories developed to delineate
the antecedents and barriers to new technology adoption offer a nuanced understanding
of why certain consumers (commercial farms) were hesitant. One of the plausible expla-
nations is that the new technology did not provide sufficient relative advantages or did
not elicit pleasure and arousal based on the perceived ease of use and usefulness. These
variables predict positive or negative attitudes toward new technologies and adoption
intentions [
93
]. Based on the theory of technology adoption, there is a need for customized
soft robotics and swarm robotic solutions that address the unique needs of commercial
farms. The view is further supported by the fact that the harvesting of cherries, pears,
stone fruits, kiwifruit, mandarins, cucumbers, peaches, pome fruits, and tomatoes requires
different grip force and pressure and biomimetic designs based on the shape and size of the
produce [
84
,
85
]. The challenge of developing efficient pickers for fruit collection, demands
systems that, apart from having the suitable electromechanical characteristics, should be
equipped with fast and accurate fruit identification algorithms exploiting machine vision
and other
techniques [47]
, such as the one destined for
peaches [94]
or the one for banana
recognition and
cutting [57]
. Machine vision imitates the ability of most animals to see,
while stereo vision is inspired by the calculation of depth information from the views of
different points in space, which was thought to be confined only to primates and mam-
mals but has also been demonstrated in other animals, such as birds and amphibians [
95
].
These artificial vision mechanisms can be used to enhance the actual systems serving
agricultural tasks [
96
], including maturity detection for ripe fruit harvesting [
57
,
97
] or for
vegetables [
98
] and similar classification actions [
99
,
100
]. Pest and disease detection is also
a process typically based on visual techniques [
52
,
101
,
102
] and has a direct impact on the
quality and quantity of the fruits being collected. Furthermore, automatic navigation and
obstacle detection based on visual data [
103
,
104
] are important functions for supporting
the system carrying the gripper effectors or performing the fruit transportation or plant
treatment tasks. For these reasons, apart from the soft robotic mechanisms for harvesting,
the current state of the adoption of swarm robotics is reviewed in the next sections.

Biomimetics 2022,7, 69 11 of 31
2.3. Swarm Robotics and Robot Bees
The term “swarm robotics” refers to a combination of different micro-robotic systems,
simple and homogeneous or heterogeneous agents, coordinated in a decentralized and dis-
tributed
manner [105]
. The development of swarm robot bees is a case in
point [105–107]
.
Swarm robotics is unique because they lack a centralized control and act according to
simple and local behavior, and this behavior is capable of solving complex tasks. The
fundamental characteristics of swarm robotics include autonomy and the ability to coop-
erate to solve different tasks. The autonomy of robotic systems is important because it
predicts the commercial application of swarm robotics. Recent industry data estimated
that the cumulative annual growth rate would be about 9.9% and market capitalization
would exceed $81 billion [
108
]. The projections made by Bluewave Consulting [
108
] were
in line with other market reports, which forecasted a CAGR of 19%. However, the market
value in the latter case was estimated at $11 billion [
109
]. The current state of the swarm
robotics global market is illustrated in Figure 5. According to Figure 5, market growth
was reinforced by investments in robotic systems, the consumer adoption of real-time
multimodal systems, and agricultural automation.
Biomimetics 2022, 7, x FOR PEER REVIEW 12 of 33
The growing adoption of robots in agriculture would inadvertently lead to complex
robot–animal interactions and animal–robot mixed societies. Recent studies have focused
on human–computer interaction (HCI) to mitigate the adverse effects of human–robot in-
teractions [110]. The preliminary evidence from published scholarly studies demonstrated
it was practical to build micro-robots that could fit into the cockroach ecosystem [110]; this
was a gateway to the higher deployment of micro-robots in pest control and soil health
monitoring. Such findings could pave the way for the greater adoption of micro-robots
that live harmoniously with bees and other insects that influence crop pollination [111].
The development of precision robots for agriculture is anticipated to increase with the
development of micro-robots, which can serve unique functions. Researchers at the Uni-
versity of Exeter had successfully developed bio-inspired bio-hybrid micro-robots that
combined biological and synthetic components, which enabled them to perform tailored
biochemical operations with precision at the nanoscale [112]. Such microbe-mimicking ro-
bots could have unique functions in agriculture, such as monitoring plant health and elim-
inating pests and disease/pest control.
From an R&D perspective, the optimal potential of swarm robotics has not been fully
exploited, considering there is growing interest in mimicking nature by learning from
bacterial colonies, which continuously benefit from multicellular cooperation, facilitating
cell division and the deployment of effective defense mechanisms [113]. Moreover, ants,
bee colonies, and bird crowds offer useful insights into creating bio-inspired swarm ro-
botic systems [113]. The limited exploitation of the full market potential might partly ex-
plain why industry growth is concentrated in the west while developing countries lag
behind in the assimilation of swarm intelligence [108,109]. The delayed investment into
swarm robotics can be regarded as a non-issue since developing countries in Africa and
South Asia have vast colonies of honey bees, which are less endangered than those in
Europe. A report by the IUCN noted that 9% of European bees were threatened with ex-
tinction [114]. The threat to natural bees underscored the urgency to invest in robot bees.
The region-specific variations in the development and adoption of swarm robotics and
other innovations in agriculture could be resolved with advances in new technologies.
Figure 5. The current state of the global swarm robotics market [109].
The main advantages of swarms are adaptability, robustness, and scalability. These
robots can operate without any central entity to control them, and the communication
between the robots can either be direct (robot-to-robot) or indirect (robot-to-environment)
[109,115]. Swarm robotics has a variety of applications, from simple household tasks to
military missions. In a swarm, multiple robots, which can be characterized as homogene-
ous or heterogeneous, are interconnected to form a swarm of robots, which serve a variety
Figure 5. The current state of the global swarm robotics market [109].
The growing adoption of robots in agriculture would inadvertently lead to complex
robot–animal interactions and animal–robot mixed societies. Recent studies have focused
on human–computer interaction (HCI) to mitigate the adverse effects of human–robot
interactions [110]
. The preliminary evidence from published scholarly studies demonstrated
it was practical to build micro-robots that could fit into the cockroach
ecosystem [110]
; this
was a gateway to the higher deployment of micro-robots in pest control and soil health
monitoring. Such findings could pave the way for the greater adoption of micro-robots that
live harmoniously with bees and other insects that influence crop
pollination [111]
. The
development of precision robots for agriculture is anticipated to increase with the devel-
opment of micro-robots, which can serve unique functions. Researchers at the University
of Exeter had successfully developed bio-inspired bio-hybrid micro-robots that combined
biological and synthetic components, which enabled them to perform tailored biochemical
operations with precision at the nanoscale [
112
]. Such microbe-mimicking robots could
have unique functions in agriculture, such as monitoring plant health and eliminating pests
and disease/pest control.
From an R&D perspective, the optimal potential of swarm robotics has not been fully
exploited, considering there is growing interest in mimicking nature by learning from
bacterial colonies, which continuously benefit from multicellular cooperation, facilitating
cell division and the deployment of effective defense mechanisms [
113
]. Moreover, ants,

Biomimetics 2022,7, 69 12 of 31
bee colonies, and bird crowds offer useful insights into creating bio-inspired swarm robotic
systems [
113
]. The limited exploitation of the full market potential might partly explain
why industry growth is concentrated in the west while developing countries lag behind
in the assimilation of swarm intelligence [
108
,
109
]. The delayed investment into swarm
robotics can be regarded as a non-issue since developing countries in Africa and South Asia
have vast colonies of honey bees, which are less endangered than those in Europe. A report
by the IUCN noted that 9% of European bees were threatened with extinction [
114
]. The
threat to natural bees underscored the urgency to invest in robot bees. The region-specific
variations in the development and adoption of swarm robotics and other innovations in
agriculture could be resolved with advances in new technologies.
The main advantages of swarms are adaptability, robustness, and scalability. These
robots can operate without any central entity to control them, and the communication between
the robots can either be direct (robot-to-robot) or indirect (robot-to-
environment) [109,115]
.
Swarm robotics has a variety of applications, from simple household tasks to military
missions. In a swarm, multiple robots, which can be characterized as homogeneous or
heterogeneous, are interconnected to form a swarm of robots, which serve a variety of
applications in the field of smart agriculture, such as sowing/seeding, the diagnosis of
soil and plants, and irrigation systems [
9
,
90
]. Since individual robots have processing,
communication, and sensing capabilities locally on board, they can interact with each other
and react to the environment autonomously. The unique capabilities make it possible for
swarm robots to replicate the reactions and functions of various swarms in nature (birds,
fish, and insects) to carry out a specific goal. The perceived advantages and disadvantages of
swarm robotic systems are largely contingent on the desired functions, including navigation,
miscellaneous (self-healing, self-reproduction, and human swarm interaction), and spatial
organization (see Figure 6) [
116
]. Considering that each sub-class of swarm robots offers
unique benefits in agriculture, the adoption should be guided by local needs, technology
adaptability, cost, and sustainability.
Biomimetics 2022, 7, x FOR PEER REVIEW 13 of 33
of applications in the field of smart agriculture, such as sowing/seeding, the diagnosis of
soil and plants, and irrigation systems [9,90]. Since individual robots have processing,
communication, and sensing capabilities locally on board, they can interact with each
other and react to the environment autonomously. The unique capabilities make it possi-
ble for swarm robots to replicate the reactions and functions of various swarms in nature
(birds, fish, and insects) to carry out a specific goal. The perceived advantages and disad-
vantages of swarm robotic systems are largely contingent on the desired functions, in-
cluding navigation, miscellaneous (self-healing, self-reproduction, and human swarm in-
teraction), and spatial organization (see Figure 6) [116]. Considering that each sub-class of
swarm robots offers unique benefits in agriculture, the adoption should be guided by local
needs, technology adaptability, cost, and sustainability.
Figure 6. The function of different robotic systems [116].
A comparison of different R&D projects and commercial applications of robotic sys-
tems in agriculture demonstrated that the swarm behaviors and availability predicted use.
For example, swarm behaviors such as human–swarm interactions, partial self-healing,
collective perception, task allocation, coordinated motion, collective exploration, and ag-
gregation were associated with swarm robotics for terrestrial applications. On the con-
trary, swarm robotic self-assembly was best suited for aerial applications [116]. The ob-
servations made by Schranz et al. [116] were in agreement with other scholarly studies,
which investigated swarm behaviors and intelligence [36,37]. Table 3 summarizes the link
between different swarm behaviors, application and adoption, and environment. How-
ever, a fundamental constraint relates to the high costs of swarm intelligence systems and
the lack of consensus about the ecological impact. As noted in the preceding sections, crit-
ics expressed reservations about the ecological impact of swarm robots [10]. However, the
SAGA project suggests otherwise [90]. Despite the lack of consensus among scholars and
industry stakeholders, swarm robotics have been employed in the field to facilitate auto-
mation in agriculture and another sector [36,37,113,115,116]. Based on the current market
trends, it could be argued that ecological concerns were not a critical impediment to the
adoption of swarm robotic systems.
Figure 6. The function of different robotic systems [116].
A comparison of different R&D projects and commercial applications of robotic sys-
tems in agriculture demonstrated that the swarm behaviors and availability predicted use.
For example, swarm behaviors such as human–swarm interactions, partial self-healing,

Biomimetics 2022,7, 69 13 of 31
collective perception, task allocation, coordinated motion, collective exploration, and ag-
gregation were associated with swarm robotics for terrestrial applications. On the contrary,
swarm robotic self-assembly was best suited for aerial applications [
116
]. The observations
made by Schranz et al. [
116
] were in agreement with other scholarly studies, which inves-
tigated swarm behaviors and intelligence [
36
,
37
]. Table 3summarizes the link between
different swarm behaviors, application and adoption, and environment. However, a fun-
damental constraint relates to the high costs of swarm intelligence systems and the lack
of consensus about the ecological impact. As noted in the preceding sections, critics ex-
pressed reservations about the ecological impact of swarm robots [
10
]. However, the SAGA
project suggests otherwise [
90
]. Despite the lack of consensus among scholars and industry
stakeholders, swarm robotics have been employed in the field to facilitate automation in
agriculture and another sector [
36
,
37
,
113
,
115
,
116
]. Based on the current market trends, it
could be argued that ecological concerns were not a critical impediment to the adoption of
swarm robotic systems.
Table 3.
The link between different swarm behaviors, application and adoption, and
environment [116]
.
Environment Project/Product Name Basic Swarm Behaviors Availability
Aerial
Distributed Flight Array Self-assembly, coordinated motion n.a.
Crazyflie 2.1 Aggregation, collective exploration, coordinated
motion, collective localization, collective perception
Open-source, commercial
Finken-III n.a.
Aquatic
CoCoRo
Aggregation, collective exploration, collective
localization, task allocation
n.a
Monsun
CORATAM Open-source
Outer Space
Swarmers Collective exploration, collective localization
n.a
Marsbee Collective exploration, coordinated motion,
task allocation
Current research and development projects coupled with the scheduled projects might po-
tentially enhance and reduce the costs associated with swarm
agriculture [36,37,113,115,116]
.
The utility of biomimetic innovations would be augmented by advances in UAVs for
agricultural applications. UAVs have been extensively used to provide aerial monitoring
capabilities on farms, crop monitoring, and the spraying of fertilizers [
43
,
109
,
117
]. Despite
the inadequate distribution of technological innovations, the progress would have a tangi-
ble and positive impact on farming in the agriculture 4.0 phase, given the development of
light UAVs, which are quicker, affordable, and capable of providing real-time information
of all sections in a farm. On the downside, developing countries with inadequate access to
resources and intelligent systems would lag behind compared to developed countries.
2.3.1. Case Studies of Commercial Adoption
The SAGA project was one of the most successful swarm robotics adoption case
studies [
90
,
116
]. Beyond SAGA, researchers have developed an abstract model for weed
monitoring [
36
]. The system aims to take over the monitoring task, generate task maps for
future autonomous weeding robots, tell them which areas to work and plan their paths,
and provide monitoring and mapping systems by swarms of UAVs. The system functions
by exploiting a simple random walk strategy that constitutes a baseline. The baseline
monitoring strategy against a disparate weed distribution is efficient, behaves well with
low rates of weed detection, and presents good scalability with the group size [
36
]. The
observations made by Albani et al. [
36
] were in agreement with Carbone et al., who noted
that swarm robots were ideal for crop monitoring in precision agriculture [
118
]. Based
on the preliminary positive evidence, UAVs can help farmers take aerial images of the
crops, which can be processed to extract information about the state of the crops; this
was demonstrated in the Mobile Agricultural Robot Swarms (MARS) project for farming

Biomimetics 2022,7, 69 14 of 31
operations [
9
]. MARS’s path planning, optimization, and the supervision of the robotic
swarms are coordinated by a centralized entity called OptiVisor, which can supervise the
seeding process, avoid collision between the robots, and react to robot failures.
The SAGA and MARS projects affirm that swarm-forming robots can improve crops and
reduce environmental impact. Low-input robots offer potential such as performing tasks in the
field that originally required the precise work attributed to human
presence [36,37,113,115,116]
.
However, as they will potentially become increasingly integrated into human society, new
regulations should be developed to govern the operations of swarm robotic systems. At
present, there are no regulations, given the industry is still
nascent [111,112,119,120]
. Addi-
tionally, the development of advanced robotic systems is not confined to the agricultural
sector in isolation; there are micro-robots for medical, industrial, and agricultural appli-
cations. Considering each sector has unique requirements, industry-specific regulations
are needed.
In 2021, researchers successfully developed micro-robots that successfully mimicked
bees and spiders. Of particular importance was the development of robot bees by Arugga
AI Farming (see Figure 7), which exploit deep learning to facilitate cross-pollination in
plants [
120
]. Even though the technology has not been widely adopted in the agricultural
sector, preliminary data indicates that the robots could be more effective than natural bees,
hand pollination, and flowers to attract bees [
119
]. The case for robotic bees advanced
by Arugga AI Farming [
119
] was corroborated by Boffey [
121
], who suggested that the
robot bees could complement the activities of natural bees, which are threatened by anthro-
pogenic activities. There are valid economic grounds for the technology. For example, bee
pollination contributed about $29 billion to the agricultural industry [
122
]. However, the
economic contribution of honey bees is threatened by the pesticide poisoning of colonies.
Biomimetics 2022, 7, x FOR PEER REVIEW 15 of 33
demonstrated in the Mobile Agricultural Robot Swarms (MARS) project for farming op-
erations [9]. MARS’s path planning, optimization, and the supervision of the robotic
swarms are coordinated by a centralized entity called OptiVisor, which can supervise the
seeding process, avoid collision between the robots, and react to robot failures.
The SAGA and MARS projects affirm that swarm-forming robots can improve crops
and reduce environmental impact. Low-input robots offer potential such as performing
tasks in the field that originally required the precise work attributed to human presence
[36,37,113,115,116]. However, as they will potentially become increasingly integrated into
human society, new regulations should be developed to govern the operations of swarm
robotic systems. At present, there are no regulations, given the industry is still nascent
[111,112,119,120]. Additionally, the development of advanced robotic systems is not con-
fined to the agricultural sector in isolation; there are micro-robots for medical, industrial,
and agricultural applications. Considering each sector has unique requirements, industry-
specific regulations are needed.
In 2021, researchers successfully developed micro-robots that successfully mimicked
bees and spiders. Of particular importance was the development of robot bees by Arugga
AI Farming (see Figure 7), which exploit deep learning to facilitate cross-pollination in
plants [120]. Even though the technology has not been widely adopted in the agricultural
sector, preliminary data indicates that the robots could be more effective than natural
bees, hand pollination, and flowers to attract bees [119]. The case for robotic bees ad-
vanced by Arugga AI Farming [119] was corroborated by Boffey [121], who suggested
that the robot bees could complement the activities of natural bees, which are threatened
by anthropogenic activities. There are valid economic grounds for the technology. For ex-
ample, bee pollination contributed about $29 billion to the agricultural industry [122].
However, the economic contribution of honey bees is threatened by the pesticide poison-
ing of colonies.
Figure 7. Robot bees developed by Arugga AI Farming Israel for cross-pollination.
Despite the strong support for robot bees in agriculture, critics raised concerns about
the suitability of robot bees. One school of thought claims that robot bees could not replace
natural biodiversity [123]. This worldview is grounded in the possibility that the robot
bees could become an invasive species. Additionally, it is not practical for robot bees to
entirely replace natural bees, considering mass production would have a negative ecolog-
ical impact, and they cannot satisfy the cultural and intrinsic worth [123]. For example,
bee-mediated pollination is essential to the proper function of the natural environment.
Potts et al.’s [123] criticism was further reinforced by evidence collected by the Uni-
versity of Sussex Gould’s laboratory researchers, who claimed that it was highly improb-
able for humans to develop robot bees that are as effective as natural bees because bees
Figure 7. Robot bees developed by Arugga AI Farming Israel for cross-pollination.
Despite the strong support for robot bees in agriculture, critics raised concerns about
the suitability of robot bees. One school of thought claims that robot bees could not replace
natural biodiversity [
123
]. This worldview is grounded in the possibility that the robot bees
could become an invasive species. Additionally, it is not practical for robot bees to entirely
replace natural bees, considering mass production would have a negative ecological impact,
and they cannot satisfy the cultural and intrinsic worth [
123
]. For example, bee-mediated
pollination is essential to the proper function of the natural environment.
Potts et al.’s [
123
] criticism was further reinforced by evidence collected by the Univer-
sity of Sussex Gould’s laboratory researchers, who claimed that it was highly improbable
for humans to develop robot bees that are as effective as natural bees because bees had
been “pollinating flowers for more than 120 million years; they have evolved to become
very good at it. It is remarkable hubris to think we can improve on that” [
124
]. Economic
considerations also justified the case against robot bees.

Biomimetics 2022,7, 69 15 of 31
As of 2020, there were about 3.2 trillion bees, which are self-sufficient and independent.
If humans opted to replace the bees with robot bees, it would cost 32 billion assuming
that each bee costs one penny (which is highly unlikely). The sustainability of the robot
bees would be problematic, given robots cannot reproduce and often malfunction. In
brief, the costs associated with replacing natural bees with robot bees are unsustainable.
The negative economic and ecological arguments could be addressed with advances in
technology, given companies are exploring the use of ecologically benign materials that are
light, cheap, and biodegradable.
2.3.2. Swarm Robotic Systems for Intelligent Pesticide Application
Beyond the robot bees, researchers have developed swarm robotic systems for pest
control [
107
]. The case for intelligent pest control is further augmented by the long-term and
adverse cost implications associated with pesticide use. On average, 360 million kilograms
of pesticides are sprayed on crops each year [
107
]. Despite the drawbacks mentioned above,
the continued use of pesticides is encouraged due to the high returns on investment and
lack of practical alternatives. Still, there are critical drawbacks linked to traditional pesticide
application, given the method is imprecise, expensive, and inaccurate. It is not suitable for
small farms and cannot be applied on farms with a mountainous terrain [
125
]. Moreover,
large UAVs in pesticide application exhibit adjuvant-specific drift rates of 30–74% [
126
].
The higher the drift, the lower the precision and the higher the volume of pesticide wasted.
There is a growing interest in delivering spraying robots with additional features, like
thermal imagery capabilities for plant inspection and early disease detection [
127
], thus
improving their autonomous characteristics and usability. Recently, researchers discovered
that it was possible to develop swarm robots for pesticide application [
107
,
128
,
129
]. In
particular, Skyx swarm technology has developed software for “autonomous and modular
spraying robots” [
130
], while Greenfield Robotics has developed the hardware components
for the wide-scale adoption of swarm robotic systems for pesticide applications [
131
]. The
sustainability of traditional fertilizer application methods is questionable, considering
the deleterious effect on the environment and human health. For example, pesticides
have endangered nearly 10% of European bees, placed on the IUCN red list [
114
]. A
primary concern is the inadequate data concerning applying the technology in real-life
situations (smallholder and large farms). The observations made in the literature were
drawn from pilot studies and experiments. In the absence of conclusive data comparable
to robot bees, the use of swarm robotic systems in pesticide application would remain
limited. The view was further reinforced by the fact that mobility and adaptability are
impacted by unexpected changes in atmospheric conditions and obstacles, common in
farms situated in the mountainous regions of South Korea, Japan, and China [
126
]. Swarm
robotics might exhibit an undesirable phase transition between two macroscopic states [
132
].
The weather-dependent technical constraints should be addressed in future research and
development projects.
2.3.3. Robot-Animal Artifacts
The advances in swarm and soft robotics have led to the adoption of different robot-
animal systems, such as self-assembly systems [
113
]. The artifact mimics the different
natural processes or structures of microorganisms and exhibits similar behaviors. Although
such constructions seem impressive, they raise many questions about ethics, viability, and
ecological impact [
10
]. Pollination is an essential part of plant reproduction and is mainly
performed by bees [
10
]. Pollinating animals travel from plant to plant, carrying pollen
on their bodies and transferring the genetic material, which is a vital interaction for the
reproduction of most flowers. Robo-bees are small, autonomous aerial vehicles, weighing
up to 10 g and sized just a few centimeters, which can replace the pollination activities of
insects. These UAVs resemble bees and are designed to perform similar functions. However,
the use of robo-bees raises ethical, economic, and ecological issues as noted in the preceding

Biomimetics 2022,7, 69 16 of 31
sections [
2
,
12
,
29
,
64
,
87
]. Nonetheless, there is strong support for bio-inspired innovations
in agriculture to boost efficiency and optimize crop yields.
Fruits and vegetables depend on animal pollinators such as bees [
10
,
106
,
120
,
133
]. The
replacement of natural bees with robot bees might disrupt natural ecosystems [
86
,
113
,
134
].
The production of robo-bees involves the assembly of electronic components made of
metals, plastics, silicon, liquid crystal elastomers, stretchy polymer networks, and lithium,
among other elements [
135
,
136
]. The processing of these elements from natural ores
and the production of lithium polymer batteries to power the soft robots and swarm
robotic systems releases toxic substances into the environment. As of 2019, the production
(including mining, refining, and cell assembly) and operation of Li-Po batteries released
about 70–110 kg CO2e/kWh [
137
]. Beyond production, there are other risks associated
with the non-renewable and toxic materials in robots; for example, the swarm robots
can fail or malfunction during operation [
37
,
105
,
113
,
116
,
118
]; this might result in the
contamination of crops with toxic metals and elements. Even though there are legitimate
concerns about ecological impact, the potential impact on production efficiency catalyzed
widespread adoption.
Market growth should not negate the adoption of standards to guide the responsible
use of soft robotics and swarm robotics; there is a profound risk of the contamination
of fruits and vegetables if the swarm robotics fall and release toxins. The risk remains
significant regardless of whether the robot bees fail during operation, are damaged by
weather, or fail to return to base [
10
]. Beyond weather related-issues, there are concerns
about the life-cycle analysis of the innovations, considering most are made using non-
renewable materials—hence the call to balance the use of materials in swarm robotics [
138
].
On the contrary, the swarm and soft robotics proponents argue that natural birds and
pests equally damage fruits and vegetables. One study estimated that birds were directly
responsible for $189 million in damages to crops and vegetables [
139
]. The estimates
provided by Anderson et al. [
139
] were comparable to USDA’s Specialty Crop Research
Initiative report, which established that wine grapes, blueberries, cherries, tart, and sweet
cherries were highly susceptible to bird damage, which accounted for 13–67% of the losses
(see Table 4) [
140
]. Adopting advanced crop management methods has not prevented bird
damage to crops. Since natural and eco-friendly birds had a negative impact on high-value
commercial crops, the potential risk of swarm robot system failure should not be a barrier
to the widespread use of advanced technologies on farms; this is because bio-inspired
management strategies have proven effective.
Table 4. Apples, cherries, and grapes losses linked to bird damage [140].
Crop
Yield
per
Acre
Annual Bird
Management
Costs
Current
Percent Lost to Bird Damage
No Management
(Low Estimate)
No Management
(High Estimate)
Wine Grapes 5.11 $1570 6% 36% 39%
Blueberries 5191 $404 12% 52% 54%
Tart Cherries 7260 $510 9% 43% 47%
Sweet Cherries 3.40 $692 31% 60% 67%
HC Apples 679 $249 5% 13% 15%
The USDA recommends the following bird damage prevention strategies: the mod-
ification of agricultural practices, better habitat management, decoy crops, the use of
frightening physical devices, and chemical repellents [
140
,
141
]. Each of these strategies
has been proven effective in preventing bird damage. On the downside, farmers are less
incentivized to engage in bird management strategies unless the damage is severe due to
the cost implications. Considering the cost implications associated with the management
of birds, bio-inspired solutions have been proven to be practical alternatives.
Muller et al. reported the successful development of a bio-inspired autonomous
aircraft for bird management [
142
]. However, the commercial rollout was constrained

Biomimetics 2022,7, 69 17 of 31
by the trade-off between the use of lightweight materials, structural strength, moment of
inertia, and center of gravity [
132
,
136
,
142
]. Further improvements in design were necessary
before commercial deployment. In the meantime, researchers have reported positive
outcomes with the deployment of Bird Gard in Australia [
143
]. The Bird Gard system
features a bio-inspired sonic and ultrasonic microchip technology, which is non-harmful,
non-toxic, environmentally friendly, and humane. The system is ranked among the leading
bird deterrence and pest control systems [143].
In contrast to the traditional methods employed to prevent bird damage, the system is
affordable. Additionally, the miniaturized electronics and the high-energy-density lithium
batteries make using small UAVs for biomimetic bird damage control in agriculture possible.
The biomimetic systems mimic flapping wings, natural sounds, and behaviors, apart from
exploiting ultrasonics. Recently, researchers have proposed developing a system that
integrates all four capabilities in a natural predator-like design. In parallel, scientists
concentrate their efforts in order to provide optimized path planning solutions for the
UAVs destined for bird monitoring and repelling purposes [144].
Beyond the bio-inspired bird management systems, various farm activities can provide
the basis for experimentation with biomimetic systems in agriculture, such as human- and
path-following robotic mules or shepherd robots [
145
–
149
]. These systems may include
monocular, stereo, or thermal vision combined with various assistive components, such
as voice recognizers or neural network operation accelerators. Figure 8shows details of
the core engine of the robot presented in [
145
], where the machine vision process on a
raspberry pi unit is assisted by an Intel
®
Neural Compute Stick 2 accelerator chip, while
details of the electromechanical actuators and controllers are also depicted. Figure 9shows
a Luxonis OAK-D stereovision camera that incorporates the same chip with the Intel
®
Neural Compute Stick 2 for faster processing, thus leaving enough processing power on
the hosting raspberry unit available for serving complementary tasks.
Biomimetics 2022, 7, x FOR PEER REVIEW 18 of 33
The USDA recommends the following bird damage prevention strategies: the modi-
fication of agricultural practices, better habitat management, decoy crops, the use of
frightening physical devices, and chemical repellents [140,141]. Each of these strategies
has been proven effective in preventing bird damage. On the downside, farmers are less
incentivized to engage in bird management strategies unless the damage is severe due to
the cost implications. Considering the cost implications associated with the management
of birds, bio-inspired solutions have been proven to be practical alternatives.
Muller et al. reported the successful development of a bio-inspired autonomous air-
craft for bird management [142]. However, the commercial rollout was constrained by the
trade-off between the use of lightweight materials, structural strength, moment of inertia,
and center of gravity [132,136,142]. Further improvements in design were necessary be-
fore commercial deployment. In the meantime, researchers have reported positive out-
comes with the deployment of Bird Gard in Australia [143]. The Bird Gard system features
a bio-inspired sonic and ultrasonic microchip technology, which is non-harmful, non-
toxic, environmentally friendly, and humane. The system is ranked among the leading
bird deterrence and pest control systems [143].
In contrast to the traditional methods employed to prevent bird damage, the system
is affordable. Additionally, the miniaturized electronics and the high-energy-density lith-
ium batteries make using small UAVs for biomimetic bird damage control in agriculture
possible. The biomimetic systems mimic flapping wings, natural sounds, and behaviors,
apart from exploiting ultrasonics. Recently, researchers have proposed developing a sys-
tem that integrates all four capabilities in a natural predator-like design. In parallel, scien-
tists concentrate their efforts in order to provide optimized path planning solutions for
the UAVs destined for bird monitoring and repelling purposes [144].
Beyond the bio-inspired bird management systems, various farm activities can pro-
vide the basis for experimentation with biomimetic systems in agriculture, such as hu-
man- and path-following robotic mules or shepherd robots [145–149]. These systems may
include monocular, stereo, or thermal vision combined with various assistive compo-
nents, such as voice recognizers or neural network operation accelerators. Figure 8 shows
details of the core engine of the robot presented in [145], where the machine vision process
on a raspberry pi unit is assisted by an Intel® Neural Compute Stick 2 accelerator chip,
while details of the electromechanical actuators and controllers are also depicted. Figure
9 shows a Luxonis OAK-D stereovision camera that incorporates the same chip with the
Intel® Neural Compute Stick 2 for faster processing, thus leaving enough processing
power on the hosting raspberry unit available for serving complementary tasks.
Figure 8. Details of the core engine of the robot presented in [145].
Figure 8. Details of the core engine of the robot presented in [145].
In a more industrialized layout, Rocos and Boston Dynamics developed a remote-
control system for robots that can herd sheep, assist in harvesting, inspect crop yields,
and create real-time terrain maps. Apart from the advanced robots developed by Boston
Dynamics, Australia’s SwagBot affords similar capabilities. The robot features a camera
and a drone, which can be launched and work together in a two-bot team [
148
,
149
]. The
shepherd robot can herd and interact with animals, traverse obstacles, drive over rough
terrain and shallow water, and climb hills.
In contrast to the Boston Dynamics robot, the SwagBot was released in 2016, and fur-
ther improvements have been made with the release of new and advanced
models [148,149]
.

Biomimetics 2022,7, 69 18 of 31
On the downside, there is no consensus on whether wheeled or legged robots should be
deployed on farms. The legged robots were better at avoiding obstacles but performed tasks
at lower speeds. In contrast, the wheeled robotic systems were fast but unable to overcome
obstacles in their paths [
150
]. Field experiments conducted on the above robotic artifacts
and similar ones show that they need improvement in terms of energy
efficiency [150,151]
.
Beyond the energy requirements of the robotic systems, the broad adoption of robots in the
agriculture sector raises economic, ethical, and environmental issues, as noted earlier.
Biomimetics 2022, 7, 69 19 of 32
Figure 9. A Luxonis OAK-D stereovision camera that incorporates neural compute functionality, as
an implementation variant enhancing the robot presented in [145].
In a more industrialized layout, Rocos and Boston Dynamics developed a remote-
control system for robots that can herd sheep, assist in harvesting, inspect crop yields, and
create real-time terrain maps. Apart from the advanced robots developed by Boston Dy-
namics, Australia’s SwagBot affords similar capabilities. The robot features a camera and
a drone, which can be launched and work together in a two-bot team [148,149]. The shep-
herd robot can herd and interact with animals, traverse obstacles, drive over rough terrain
and shallow water, and climb hills.
In contrast to the Boston Dynamics robot, the SwagBot was released in 2016, and
further improvements have been made with the release of new and advanced models
[148,149]. On the downside, there is no consensus on whether wheeled or legged robots
should be deployed on farms. The legged robots were better at avoiding obstacles but
performed tasks at lower speeds. In contrast, the wheeled robotic systems were fast but
unable to overcome obstacles in their paths [150]. Field experiments conducted on the
above robotic artifacts and similar ones show that they need improvement in terms of
energy efficiency [150,151]. Beyond the energy requirements of the robotic systems, the
broad adoption of robots in the agriculture sector raises economic, ethical, and environ-
mental issues, as noted earlier.
Future research and development should include establishing the policy choices nec-
essary to meet the ethical challenges and maximize the benefits of the utilization of robots
in agriculture. Other concerns raised by stakeholders include the absence of an effective
robotic herding algorithm that can function optimally with a large herd of animals and
nature [151]. Farm animals are not familiar with robotic systems, and it would take time
to build robot-to-animal interactions and a suitable robotic herding platform [151]. The
concerns raised by Li et al. [151] were in line with Fue et al.’s [150] research, which af-
firmed that bio-inspired robotic systems could not outperform humans in the short term.
In brief, bio-inspired robotic systems can complement human labor.
3. Biomimetic Materials, Structures, and Resource Management
The choice of suitable materials for agricultural structures predicts productivity, op-
erational costs, durability, and the sustainability of operations; this is because materials
predict heat losses, insulation, and the absorption of UV radiation, humidity, and temper-
ature [61,152–154]. Significant attention has been directed toward selecting suitable mate-
rials for livestock structures, farming equipment, silos for seeds and crops, as well as for
greenhouses and their frames and covers [13,128,155,156]. Traditional farm buildings
were designed as industrial buildings, using stone, brick, steel, or even timber; however,
Figure 9.
A Luxonis OAK-D stereovision camera that incorporates neural compute functionality, as
an implementation variant enhancing the robot presented in [145].
3. Biomimetic Materials, Structures, and Resource Management
Future research and development should include establishing the policy choices
necessary to meet the ethical challenges and maximize the benefits of the utilization of
robots in agriculture. Other concerns raised by stakeholders include the absence of an
effective robotic herding algorithm that can function optimally with a large herd of animals
and nature [
151
]. Farm animals are not familiar with robotic systems, and it would take
time to build robot-to-animal interactions and a suitable robotic herding platform [
151
].
The concerns raised by Li et al. [
151
] were in line with Fue et al.’s [
150
] research, which
affirmed that bio-inspired robotic systems could not outperform humans in the short term.
In brief, bio-inspired robotic systems can complement human labor.
The choice of suitable materials for agricultural structures predicts productivity, op-
erational costs, durability, and the sustainability of operations; this is because materi-
als predict heat losses, insulation, and the absorption of UV radiation, humidity, and
temperature [61,152–154]
. Significant attention has been directed toward selecting suit-
able materials for livestock structures, farming equipment, silos for seeds and crops, as
well as for greenhouses and their frames and covers [
13
,
128
,
155
,
156
]. Traditional farm
buildings were designed as industrial buildings, using stone, brick, steel, or even timber;
however, new structures are fitted with sensors and IoT systems for better regulation of
the microclimate [
24
,
61
,
153
,
154
,
157
]. However, the risk of corrosion and material failure
catalyzed the demand for bio-inspired materials that can withstand severe environmental
conditions [
158
]. A wide array of biomimetic materials have been developed that can
adapt and respond to their environment due to their unique deformable, elastic, and rhe-
ological properties [
158
]. The progress made in agricultural materials engineering could
facilitate the creation of a wide range of smart materials and structures that can self-sense
and self-repair without external intervention, such as microcapsules, calcite-precipitating
bacteria, and shape memory polymers for fluidic elastomer actuators (FEAs) [
86
,
159
,
160
].
Preliminary data drawn from trials undertaken using self-healing systems/materials are
promising [
2
,
161
]. The advantages include lower costs, better reinforcement, a lower

Biomimetics 2022,7, 69 19 of 31
risk of mechanical damage, and durability [
12
,
116
,
158
]. Specific examples of bio-inspired
materials are reviewed under Section 3.1.
3.1. Biomimetic Materials
The growth of bio-inspired innovations is contingent on suitable materials to limit
the adverse ecological impact and carbon footprint [
159
,
162
]. Progressive research and
development have led to novel, lightweight and durable materials with bio-inspired relax-
ation and dynamic, multi-functionality, and hierarchical properties. The ideal materials
include polymers and composites, and surfactants [
87
,
159
,
162
], biological materials, gels,
and elastomers with easily deformable, elastic, and rheological properties [
158
]. The impact
resistance of biological materials is predicted by the presence or absence of hierarchical
structures, composites, porous structures, and interfaces. Other requirements include soft
properties, especially for soft robotic grippers used to harvest fruits and vegetables. The
soft properties are comparable to soft matter found in nature, such as collagen in human
bones [158]
. The soft body properties mitigate mechanical damage. However, the unique
properties have substantial cost implications. The bio-inspired soft materials are expensive
because they embody artificial intelligence in contrast to rigid-bodied robots [
87
,
159
,
162
].
For example, soft robots are integrated with robot operating systems (ROS) [
160
], deep
learning systems, and RGB-D cameras with GPU computers [
163
]. Recent developments
have demonstrated that the materials can be customized to achieve the desired function
in agriculture.
Conductive polymer materials are suitable for robot bees because they are light and
durable. In addition, the polymers have superconductive properties and are
elastic [159,164,165]
.
Other materials that have been developed for robots include shape-memory alloys (SMA)
and magnetic shape memory (MSM) alloys or ferromagnetic shape memory alloys (FSMA)
for micro-positioning applications [
166
]. Despite the progress made in developing ad-
vanced materials for robots, the cost of the materials remains a challenge, and this part
helps explain the high cost of robotic materials.
Another application is bio-inspired by biological compositions. An attempt to improve
the mechanical properties of low-carbon steel with biomimetic units was made using a laser
re-melting process and augmented by computer-aided design [
167
]. The unique benefits
associated with the process were ascribed to the combined effects of the microstructural
characteristics and stress redistribution. The net effect was enhancing the strength and flex-
ibility of materials, the pure composition, controllable parameters, and simple procedures
without altering the special properties of the substrate materials [
167
], [
168
]. Hydrophobic
materials commonly exist on the surfaces of plants and anti-wetting animals, such as plant
leaves covered with epicuticular wax or other substances, which are quite waterproof.
A biomimetic super-hydrophobic surface has been fabricated by directly spraying a self-
assembled nanomaterial inspired by hydrophobic properties found in nature [
167
,
168
]. A
nano-silica gel with low surface energy was used to modify the multi-scale microstructure
and surface chemical properties. Following the completion of the process, the roughness
and self-breathing properties of the modified surface were significantly improved [
167
,
168
].
Based on the experimental data, the approach could be employed to modify various sub-
strates. The water repellent is beneficial for simplicity in fabrication, high adaptation to a
concrete-based substrate, cost-effectiveness, and self-breathability.
Innovative biopolymers from plant biomass and by-products from fishery waste have
been developed for eco-friendly food-package films to reduce pollution and protect the
environment by substituting the use of fossil fuel-derived compounds [
167
,
168
]. Marine
organisms such as mussels can produce viscoelastic materials made from polymeric protein
polysaccharides, which inspired research groups to produce novel materials [
169
]. Drawing
from past successes, it is recommended that the future design and development of new
materials be bio-inspired to enhance the ecological compatibility of farming structures,
equipment, and systems. The proposal is justified considering bio-inspired materials have
been developed to extract lanthanides and actinides and remove dyes and ingredients in

Biomimetics 2022,7, 69 20 of 31
nutraceuticals [
170
–
172
]. However, the transition from traditional to bio-inspired materials
creates challenges and opportunities. Researchers have a role to play in maximizing the
benefits and limiting the negative effects.
3.2. Biomimetic Structures
Biomimetic innovations and materials have an integral role in the performance of
agricultural structures such as greenhouses, storage facilities, and silos [
173
,
174
]. In contrast
to conventional structures, agricultural structures must satisfy certain requirements such
as adequate ventilation, the regulation of solar radiation, and thermal losses [
175
–
177
].
The design of greenhouse structures and ventilation is important to control the internal
micro-climate, especially the temperature and the humidity, among other parameters, that
predict crop growth and yield [
155
,
178
–
180
]. Bio-inspired methods of complex branching,
metaheuristics, and streamlining have been described to optimize ventilation and sunlight
in a greenhouse [
181
]. Anthill mounds have been examined to design a natural ventilation
system for greenhouses and bricks [
173
]. Termite colonies are responsive to environmental
changes and can survive as one system. As the research on termite mounds and geometry
indicates, the natural ventilation is driven by pressure differences on the surface through the
boundary layer of the wind. For this reason, digital simulation and experimentation have
been applied to define and develop forms for natural branched ventilation in buildings [
182
].
The broad scope of the application demonstrates how biomimetic innovations, digital
techniques, and complex branching morphology can be applied for natural ventilation and
solar optimization in agricultural structures.
3.3. Resources Management—Solar Energy Harvesting
Agriculture relies heavily on energy; thus, the use of sustainable energy technolo-
gies in climate change is an urgent need. Renewable energy such as solar energy can
reduce a farm’s electricity and heating
costs [183]
. The efficient use of photovoltaics (solar
electric panels) can power farm operations, livestock buildings, greenhouses, and water
pumps [184–186]
. Intelligent solar tracking systems mimic the plants’ response to the
sun’s movement, a process called phototropism, to maximize the energy collection from
the sun. A solar tracking system is used to track the sun’s position across the sky to
keep the photovoltaic system in the best position [
187
]. A novel solar tracking genera-
tion system was designed and tested to generate power for lighting in greenhouses. The
experimental results indicated that the system could provide nearly 100% of the green-
house power
requirements [188–191]
. However, the realization of optimal performance
was contingent on optimizing the systems using IoT-based artificial neural networks to
control solar tracking and precision agriculture systems [
192
,
193
]. An increase in the effi-
ciency of solar systems can be achieved using hybrid techniques, neural network principles,
and new artificial intelligence techniques [
194
]. A notable example is a solar tracking
system based on computer vision combined with control algorithms developed in Math-
ematica and Simulink and the implementation of this method in a real system, based
specifically on deep Convolutional Neural Networks (CNNs) for object localization and
detection, inspired by biological processes where the connectivity pattern between neu-
rons resembles the organization of the visual cortex [
195
]. Other phototropic systems can
autonomously and instantaneously detect and track light in three-dimensional space at
ambient temperatures very accurately and fast, without auxiliary supply or human inter-
vention, such as the SunBOT (sunflower-like biomimetic omnidirectional tracker) [
196
,
197
].
This phototropic material mimics the asymmetric biological growth of plants and the el-
egant agility of living systems, which could provide a solution to energy harvesting and
could lead to self-sustained, autonomously capable learning soft robots for performing
tasks in several environments.

Biomimetics 2022,7, 69 21 of 31
3.4. Water Resource Management
Commercial agriculture is key to the sustenance of modern civilizations, but re-
source use remains a challenge. At present, global food security is threatened by poor
soil nutrition, unsustainable agricultural practices, and the excessive use of water in
irrigation [16,180,198,199]
. Conservative estimates suggest that agricultural activities ac-
count for 70% of global water use [
200
]. If the current rates are sustained, water scarcity
will be experienced in most agriculturally productive regions due to climate change and
global warming [
42
,
201
,
202
]. Considering excessive water use contributes to global water
stress, agriculture institutions should collaborate across sectors to mitigate the challenge.
The imbalance between the supply and demand for freshwater (water scarcity) and in-
adequate rainfall patterns (water shortages) already affects every continent and must be
seriously considered.
Runoff water can help complement alternative water sources for irrigation. A pilot
project used surface runoff to offset the negative effects of a dry spell on cowpea farming
in Nigeria’s savannah belt [
203
]. Similar techniques were employed in micro-catchment
water harvesting for agriculture. A study conducted by researchers at the Freie Universität
Berlin affirmed the suitability of surface runoff micro-catchment in reducing water stress in
arid and semi-arid to sub-humid zones [
204
]. The capture and storage of surface runoff
help to meet the water needs during the dry season or mitigate the impact of dry spells.
Novel methods for effective water harvesting have been developed based on nature-
inspired designs, including the biomimicking spider webs to harvest fog water with
electrospun polymer fibers [
205
]. The use of fog water for agriculture has not been widely
explored except for the Warka tower project [
206
]. The spider web is nature’s example of
how fibers can collect water droplets from fog. Mimicking this natural process to create
nano- and micro-polymer webs with unique fiber structures opens new paths in fog-water
collection. The Warka Tower is inspired by Namib bugs and lotus flower leaves, it is about
9 m tall, consisting of locally sourced materials such as bamboo, and its efficiency can
be increased in the desert between sunset and sunrise, where the temperature range is
huge [
207
]. However, the existing meshes used in collectors need improvement, as they
show inadequacies in the surface of microstructure and property design [
205
]. Several
species can directly harvest fog or dew, such as beetles, frogs, lizards, spiders, and plants.
The design of the building of Namibia University Water Science Center was inspired by the
Namib Desert darkling beetles (Tenebrionidae) [
208
]. The building is located behind a curved
nylon wall that faces the ocean and catches the moisture from the ocean air. The wall’s net
surfaces are shaped like the bumpy structure of the beetle’s shell, and the water is stored in
underground water reservoirs. This design mimics the desert beetles, which can live in the
harshest desert climate because they can harness water from the ambient air, using their
bodies as “fog collectors assuming a characteristic fog-basking stance” [
208
]. Scanning
electron microscopy studies of the beetles confirmed the presence of hydrophilic sites,
which were instrumental in the collection of fog water. The analysis of the bio-inspired
desert beetle water collection capabilities by Nørgaard and Dacke [
208
] was in agreement
with Zhai et al. [
209
]. In the latter case, the researchers developed bio-inspired surfaces with
super-hydrophilicity properties; this was achieved through the customized deposition of
multilayer films on the hydrophilic patterns [
209
]. The films were made of poly(allylamine
hydrochloride), poly(acrylic acid), and silica nanoparticle/semi-fluoro silane, which has a
strong affinity for water molecules. Such milestones could help inform the development
of new greenhouse-covering materials that harvest water using similar techniques as the
Namib Desert darkling beetles. Various methods for preserving water have been developed,
although those that are environmentally friendly and inexpensive should be applied. The
main concern is that the bio-inspired design for fog and rainwater harvesting has not been
employed on a wider scale. The documented pilot projects focused on communal benefits
such as access to potable water rather than the exploitation of the bio-inspired designs for
intelligent agricultural purposes [
210
,
211
]. However, there is immense potential for the

Biomimetics 2022,7, 69 22 of 31
Namib Desert darkling beetle’s bio-inspired designs for water harvesting for agricultural
activities in water-scarce regions.
4. Future Prospects
Despite concerns about costs and ecological impact, there are future prospects for
utilizing robot bees/swarm robotics and soft robotics [
56
,
119
,
121
,
123
,
124
]. The positive
outlook is reinforced by the current realities in the agricultural sector. The pollination of
plants with natural bees has been compromised by anthropogenic pollution [121,123,124],
especially the use of synthetic pesticides [
122
]. The intrinsic value of the robotic innovations
is further reinforced by the projected market value of soft robotics. The sector recorded
a 10% yearly growth, translating to $81 billion in market value [
108
]. The cumulative
value of the industry would increase exponentially with innovations. For example, the
current models of robot bees rely on a specially applied gel to facilitate crop pollination,
and scientists have not coordinated large swarms of bees, despite concerted efforts to mimic
bees. The autonomous or remote coordination of the bees would facilitate the large-scale
employment of the robot bees. It would be easier to find pollen sites and pollinate the
target plants while minimizing the risk of robot bees’ collisions [
122
]. The refinement of
robot bees’ technology would translate to wide-scale adoption and better yields through
robotic pollination.
Beyond swarm and bee robotics, soft robotics has promising applications in fruit har-
vesting, considering the grip force could be adjusted to prevent the damage of fruits with a
soft rind [
84
]. The refinement of the soft robotic technology would lead to the replacement
of nearly all mechanical harvesters. The transition from mechanical harvesters and harvest-
ing to soft robotic grippers would reduce harvest and post-harvest losses associated with
the bacterial damage of mechanically damaged fruit, surface bruises, rupture, and crushing,
the destruction of plant tissue, and plastic deformation. The process would also enable
farmers to control the harvesting process precisely and achieve better yields. So far, FEA
robotic grippers and FEA-tendon-driven grippers have been proven useful in harvesting
tomatoes, cucumbers, bananas, apples, grapes, olives, and broccoli, among others [
86
]. On
the downside, technology-related advances have not resolved concerns about information
accessibility and implementation on a broader scale, the prospecting of suitable biomimicry
patterns, and bio-inspired technologies’ relevance to the problem.
5. Conclusions
The growth of biomimetic innovations has been motivated by population growth,
labor shortages on farms, and the desire to reduce the negative ecological impact on the
environment. The projected surge in the global population is expected to introduce new
challenges including food scarcity, water shortage, and higher emissions. Biomimetic
solutions have been explored to enhance agricultural productivity through adaptation to
nature. Sustainable agricultural production can meet the global food requirements through
an efficient, inclusive, and resilient system. The integration of bio-inspired innovations
enables large farms to achieve better efficiency and productivity while minimizing the
adverse environmental impact; this was demonstrated with soft robotics for fruit and
vegetable harvesting and swarm robotics. First, the soft robotic systems minimized the risk
of mechanical damage, which is a common problem with traditional mechanical harvesters
and hand-picking. The mitigation of mechanical damage (surface bruises, rupture, the
crushing destruction of plant tissue, and plastic deformation) has practical benefits for
farmers because post-harvest losses have a negative effect on profitability. Second, the
swarm robotic systems could enhance pollination by complementing natural bees, which
are critically endangered. Alternatively, the swarm robots could perform other important
functions on farms including spraying pesticides and monitoring crop growth. Based on
existing studies, it is clear that farmers have not yet exploited the full potential of soft
and swarm robotics. The use of FEA robotic grippers and FEA-tendon-driven grippers in
harvesting tomatoes, cucumbers, grapes, olives, and apples is inadequate.

Biomimetics 2022,7, 69 23 of 31
The inability to exploit the full potential of the swarm and soft robotics could be partly
attributed to a lack of consensus and phases of innovation. The soft and swarm robot
systems developed by Arugga AI Farming, Bird Gard Australia, Boston Dynamics, and the
Robotics Traction Unit (RTU) are not widely available. Various hypotheses were advanced
to explain the phenomena including the cost of the technology, consumer attitudes toward
the unknown, ecological concerns, and technical constraints. For example, Boston Dynamics
had not created an effective robotic herding algorithm that can function optimally with a
large herd of animals and nature and build robot-to-animal interactions. Other concerns
relate to the practicality of swarm robotics. Critics noted that it is illogical to assume that
humans could replicate the unique behavior of bees, which have been refined through
evolution over 120 million years; this means it is not practical to replace natural bees with
robots. However, other bio-inspired innovations have found broad acceptance including
the water harvesting from fog, inspired by the Namib Desert darkling beetles (Tenebrionidae),
and biomimetic materials, which exhibit unique relaxation, dynamic, multi-functional, and
hierarchical properties for lightweight and durable applications.
Despite the concerns against soft and swarm robots, their role in agriculture cannot
be disregarded considering the adverse effect of population growth and climate change
on agriculture. The literature reviewed affirmed that biomimetic innovations are integral
to climate-smart agriculture, which seeks to boost food security while minimizing carbon
footprints. However, there is no consensus among researchers on achieving the goals.
Conversely, pro-innovation researchers advocate for the development and deployment
of robot bees to complement natural bees in agriculture. On the other hand, critics argue
that the robot bees would negatively affect nature, given they are an invasive species.
Additionally, the cost and practicality of robot bee projects remain questionable.
Despite the challenges, the modernization of agriculture and the transition to agri-
culture 4.0 will continue, given it is necessary to increase economic growth and boost
productivity. Presently, significant progress has been made in achieving interoperation
among machine learning, robotics, and big data entities and using materials that are more
sustainable and resilient. Based on conservative projections and the progress made by
industry pioneers such as Arugga AI Farming, Bird Gard Australia, Boston Dynamics, and
the Robotics Traction Unit (RTU), soft and swarm robotics will complement existing smart
agricultural practices, leading to sustainable production. Nonetheless, the concerns raised
by scholars about the life-cycle of man-made equipment in the natural environment cannot
be disregarded and should be resolved through research and development.
Author Contributions:
Conceptualization, D.L.; methodology, M.K., D.L. and C.M.; validation, M.K.,
D.L., C.M., C.D. and K.G.A.; investigation, M.K., D.L., C.M. and C.D.; data curation, M.K., D.L. and
C.M.; writing—original draft preparation, M.K., D.L., CM. and C.D.; writing—review and editing,
M.K., D.L. and C.M.; visualization, M.K. and C.M.; supervision and project administration, K.G.A.
All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments:
The authors would like to thank the personnel of the Dept. of Natural Resources
Management and Agricultural Engineering of the Agricultural University of Athens, Greece, for their
technical assistance in the investigation process.
Conflicts of Interest: The authors declare no conflict of interest.

Biomimetics 2022,7, 69 24 of 31
References
1.
Stojanovic, M. Biomimicry in Agriculture: Is the Ecological System-Design Model the Future Agricultural Paradigm? J. Agric.
Environ. Ethics 2017,32, 789–804. [CrossRef]
2.
Hwang, J.; Jeong, Y.; Park, J.M.; Lee, K.H.; Hong, J.W.; Choi, J. Biomimetics: Forecasting the future of science, engineering, and
medicine. Int. J. Nanomed. 2015,10, 5701–5713.
3.
Bounakoff, C.; Hayward, V.; Genest, J.; Michaud, F.; Beauvais, J. Artificial fast-adapting mechanoreceptor based on carbon
nanotube percolating network. Sci. Rep. 2022,12, 2818. [CrossRef] [PubMed]
4.
Kang, D.H.; Han, W.B.; Il Ryu, H.; Kim, N.H.; Kim, T.Y.; Choi, N.; Kim, T.S. Tunable and scalable fabrication of block copolymer-
based 3D polymorphic artificial cell membrane array. Nat. Commun. 2022,13, 1261. [CrossRef]
5.
Lukic, I.; Vulic, J.; Ivanovic, J. Antioxidant activity of PLA/PCL films loaded with thymol and/or carvacrol using scCO
2
for active
food packaging. Food Packag. Shelf Life 2020,26, 100578. [CrossRef]
6.
González-Henríquez, C.M.; Sarabia-Vallejos, M.A.; Rodriguez-Hernandez, J. Progress in Polymer Science Polymers for additive
manufacturing and 4D-printing: Materials, methodologies, and biomedical applications. Prog. Polym. Sci.
2019
,94, 57–116.
[CrossRef]
7.
Baruah, S.; Dutta, Æ.J. Nanotechnology applications in pollution sensing and degradation in agriculture: A review. Environ.
Chem. Lett. 2009,7, 191–204. [CrossRef]
8.
Navas, E.; Fernández, R.; Sepúlveda, D.; Armada, M.; Gonzalez-de-Santos, P. Soft Gripper for Robotic Harvesting in Precision
Agriculture Applications. In Proceedings of the 2021 IEEE International Conference on Autonomous Robot Systems and
Competitions (ICARSC), Santa Maria da Feira, Portugal, 28–29 April 2021; pp. 167–172.
9.
Blender, T.; Buchner, T.; Fernandez, B.; Pichlmaier, B.; Schlegel, C. Managing a Mobile Agricultural Robot Swarm for a seeding
task. In Proceedings of the IECON 2016—42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy,
23–26 October 2016; pp. 6879–6886.
10.
Gleadow, R.; Hanan, J.; Dorin, A. Averting robo-bees: Why free-flying robotic bees are a bad idea. Emerg. Top. Life Sci.
2019
,3,
723–729.
11.
Vidwath, S.M.G.; Rohith, P.; Dikshithaa, R.; Nrusimha Suraj, N.; Chittawadigi, R.G.; Sambandham, M. Soft Robotic Gripper for
Agricultural Harvesting BT. In Machines, Mechanism and Robotics; Springer: Singapore, 2022; pp. 1347–1353.
12.
Blok, V.; Gremmen, B. Agricultural technologies as living machines: Toward a biomimetic conceptualization of smart farming
technologies. Ethics Policy Environ. 2018,21, 246–263. [CrossRef]
13.
Shamshiri, R.R.; Kalantari, F.; Ting, K.C.; Thorp, K.R.; Hameed, I.A.; Weltzien, C.; Shad, Z.M. Advances in greenhouse automation
and controlled environment agriculture: A transition to plant factories and urban agriculture. Int. J. Agric. Biol. Eng. Open 2018,
11, 1–22. [CrossRef]
14.
Raj, M.; Gupta, S.; Chamola, V.; Elhence, A.; Garg, T.; Atiquzzaman, M.; Niyato, D. A survey on the role of Internet of Things for
adopting and promoting agriculture 4.0. J. Netw. Comput. Appl. 2021,187, 103107. [CrossRef]
15.
Wong, K. Biomimicry: Using Nature’s Designs to Transform Agriculture. The Guardian. Available online: https://www.
theguardian.com/sustainable-business/2015/oct/30/biomimicry-institute-sxsw-eco-agriculture-bioinspired (accessed on 20
April 2022). .
16.
FAO-IFAD-UNICEF. The State of Food Security and Nutrition in the World 2021: Transforming Food Systems for Food Security,
Improved Nutrition and Affordable Healthy Diet for All. 2021. Available online: https://reliefweb.int/report/world/brief-state-
food-security-and-nutrition-world-2021-transforming-food-systems-food (accessed on 20 April 2022).
17.
United Nations. Global Issues: Food. 2021. Available online: https://wyss.harvard.edu/technology/robobees-autonomous-
flying-microrobots/ (accessed on 20 April 2022).
18.
Food and Agricultural Organization of the United Nations. The Future of Food and Agriculture: Trends and Challenges; FAO: Rome,
Italy, 2017.
19.
Pereira, M.; Oliveira, A.M. Poverty and food insecurity may increase as the threat of COVID-19 spreads. Public Heal. Nutr.
2020
,
23, 3236–3240. [CrossRef] [PubMed]
20.
Galanakis, C.M. Environment and Climate-Smart Food Production; Springer International Publishing AG: Berlin/Heilderberg,
Germany, 2021. [CrossRef]
21.
Gaspar, P.D.; Soares, V.N.G.J.; Caldeira, J.M.L.P.; Andrade, L.P.; Soares, C.D. Technological modernization and innovation of
traditional agri-food companies based on ICT solutions—The Portuguese case study. J. Food Process. Preserv. 2019. [CrossRef]
22.
Alibabaei, K.; Gaspar, P.D.; Lima, T.M. Crop Yield Estimation Using Deep Learning Based on Climate Big Data and Irrigation
Scheduling. Energies 2021,14, 3004. [CrossRef]
23.
Alibabaei, K.; Gaspar, P.D.; Lima, T.M.; Campos, R.M.; Girão, I.; Monteiro, J.; Lopes, C.M. A Review of the Challenges of Using
Deep Learning Algorithms to Support Decision-Making in Agricultural Activities. Remote Sens. 2022,14, 638. [CrossRef]
24.
Maraveas, C. Durability Issues and Corrosion of Structural Materials and Systems in Farm Environment. Appl. Sci.
2020
,10, 990.
[CrossRef]
25.
Maraveas, C.; Bartnazas, T. Application of internet of things for optimized greenhouse environments. AgriEngineering
2021
,3,
954–970. [CrossRef]
26.
Maraveas, C.; Bayer, I.S.; Bartzanas, T. 4D printing: Perspectives for the production of sustainable plastics for agriculture.
Biotechnol. Adv. 2021,54, 107785. [CrossRef]

Biomimetics 2022,7, 69 25 of 31
27.
Maraveas, C. Production of Sustainable and Biodegradable Polymers from Agricultural Waste. Polymers
2020
,12, 1127. [CrossRef]
28.
Othmani, N.; Sahak, N.M.; Yunos, M.Y.M. Biomimicry in agrotechnology: Future solution of water problem for the agriculture
industry? IOP Conf. Series Earth Environ. Sci. 2021,756. [CrossRef]
29.
Gruber, M.F.; Wood, E.; Truelsen, S.; Østergaard, T.; Hélix-Nielsen, C. Computational Design of Biomimetic Phosphate Scavengers.
Environ. Sci. Technol. 2015,49, 9469–9478. [CrossRef] [PubMed]
30.
Amend, J.; Cheng, N.; Fakhouri, S.; Culley, B. Soft Robotics Commercialization: Jamming Grippers from Research to Product. Soft
Robot. 2016,3, 213–222. [CrossRef] [PubMed]
31.
Chowdhary, G.; Gazzola, M.; Krishnan, G.; Soman, C.; Lovell, S. Soft Robotics as an Enabling Technology for Agroforestry Practice
and Research. Sustainability 2019,11, 6751. [CrossRef]
32.
Kim, S.; Laschi, C.; Trimmer, B. Soft robotics: A bioinspired evolution in robotics. Trends Biotechnol.
2013
,31, 287–294. [CrossRef]
33.
Vause, C. This Robot Has Soft Hands. It Could Be the Future of Sustainable Production. 2018. Available online: https:
//www.weforum.org/agenda/2018/06/robot-soft-hands- sustainable-production-soft-robotics/ (accessed on 25 April 2022).
34.
Gonzalez-de-Santos, P.; Fernández, R.; Sepúlveda, D.; Navas, E.; Emmi, L.; Armada, M. Field robots for intelligent farms—inhering
features from industry. Agronomy 2020,10, 1638. [CrossRef]
35.
Rial-Lovera, R. Agricultural Robots: Drivers, barriers and opportunities for adoption. In Proceedings of the 14th International
Conference on Precision Agriculture, Montreal, QC, Canada, 24–27 June 2018; pp. 24–27.
36.
Albani, D.; Manoni, T.; Arik, A.; Nardi, D.; Trianni, V. Field Coverage for Weed Mapping: Toward Experiments with a UAV
Swarm. In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST);
Springer: Cham, Switzerland, 2019; Volume 289, pp. 132–146. [CrossRef]
37.
Brambilla, M.; Ferrante, E.; Birattari, M.; Dorigo, M. Swarm Intell Swarm robotics: A review from the swarm engineering
perspective. Swarm Intell. 2013,7, 1–41. [CrossRef]
38.
Gornall, J.; Betts, R.; Burke, E.; Clark, R.; Camp, J.; Willett, K.; Wiltshire, A. Implications of climate change for agricultural
productivity in the early twenty-first century. Philos. Trans. R. Soc. B Biol. Sci. 2010,365, 2973–2989. [CrossRef]
39. Sivakumar, M.V.K. Interactions between climate and desertification. Agric. For. Meteorol. 2007,142, 143–155. [CrossRef]
40.
UNCCD. Urbanisation to Cause Huge Loss of Prime Farmland. 2019. Available online: https://www.downtoearth.org.in/news/
agriculture/urbanisation-to-cause-huge-loss-of-prime-farmland-unccd-66562 (accessed on 20 April 2022).
41. OCHA. The High Price of Desertification: 23 Hectares of Land a Minute; OCHA: Geneva, Switzerland, 2017.
42.
Shukla, P.R.; Skea, J.; Buendia, E.C.; Masson-Delmotte, V.; Pörtner, H.-O.; Roberts, D.C.; Zhai, P.; Slade, R.; Connors, S.;
van Diemen, R.; et al.
(Eds.) Intergovernmental Panel on Climate Change. In Climate Change and Land: An IPCC Special Report on
Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial
Ecosystems; 2019. Available online: https://www.ipcc.ch/srccl/download/ (accessed on 25 April 2022).
43. Jellason, N.; Robinson, E.; Ogbaga, C. Agriculture 4.0: Is Sub-Saharan Africa Ready? Appl. Sci. 2021,11, 5750. [CrossRef]
44.
Mehmood, A. Effects of Rent Seeking and Its Three Forms (Corruption, Bribery and Lobbying) on Entrepreneurship; Umea University:
Umea, Sweden, 2014.
45.
Ovadia, J.S. The Nigerian “One Percent” and the Management of National Oil Wealth Through Nigerian Content. Sci. Soc.
2013
,
77, 315–341. [CrossRef]
46. Uwakonye, M.N.; Osho, G.S.; Anucha, H. The Nigerian Economy: A Rural Sector. Int. Bus. Econ. Res. J. 2006,5, 61–78.
47.
Li, D.; Sun, X.; Elkhouchlaa, H.; Jia, Y.; Yao, Z.; Lin, P.; Li, J.; Lu, H. Fast detection and location of longan fruits using UAV images.
Comput. Electron. Agric. 2021,190, 106465. [CrossRef]
48.
Ahimbisibwe, F. Uganda and the refugee problem: Challenges and opportunities. Afr. J. Polit. Sci. Int. Relat.
2019
,13, 62–72.
[CrossRef]
49.
Balafoutis, A.; Beck, B.; Fountas, S.; Vangeyte, J.; Van Der Wal, T.; Soto, I.; Gómez-Barbero, M.; Barnes, A.; Eory, V. Precision
Agriculture Technologies Positively Contributing to GHG Emissions Mitigation, Farm Productivity and Economics. Sustainability
2017,9, 1339. [CrossRef]
50.
Gonzalez-De-Soto, M.; Emmi, L.; Benavides, C.; Garcia, I.; Gonzalez-De-Santos, P. Reducing air pollution with hybrid-powered
robotic tractors for precision agriculture. Biosyst. Eng. 2016,143, 79–94. [CrossRef]
51. Chen, C.-J.; Huang, Y.-Y.; Li, Y.-S.; Chen, Y.-C.; Chang, C.-Y.; Huang, Y.-M. Identification of Fruit Tree Pests With Deep Learning
on Embedded Drone to Achieve Accurate Pesticide Spraying. IEEE Access 2021,9, 21986–21997. [CrossRef]
52.
Cunha, J.; Gaspar, P.D.; Assunção, E.; Mesquita, R. Prediction of the Vigor and Health of Peach Tree Orchard. In Proceedings of the
International Conference on Computational Science and Its Applications, Cagliary, Italy, 5–8 July 2021; Springer: Cham, Switzerland,
2021; pp. 541–551. [CrossRef]
53.
Assuncao, E.; Diniz, C.; Gaspar, P.D.; Proenca, H. Decision-making support system for fruit diseases classification using Deep
Learning. In Proceedings of the International Conference on Decision Aid Sciences and Application (DASA), Sakheer, Bahrain,
8–9 November 2020; pp. 652–656. [CrossRef]
54.
Chen, Z.; Wu, R.; Lin, Y.; Li, C.; Chen, S.; Yuan, Z.; Chen, S.; Zou, X. Plant Disease Recognition Model Based on Improved
YOLOv5. Agronomy 2022,12, 365. [CrossRef]
55. University of New South Wales. Robotic tractor to deliver precision planting. Phys. Org. 2013.
56.
Williams, A. Cutting-Edge Agricultural Robots Offers a Low-Cost Lifeline to UK Farmers; University of Plymouth: Plymouth,
UK, 2020
.

Biomimetics 2022,7, 69 26 of 31
57.
Wu, F.; Duan, J.; Chen, S.; Ye, Y.; Ai, P.; Yang, Z. Multi-Target Recognition of Bananas and Automatic Positioning for the
Inflorescence Axis Cutting Point. Front. Plant Sci. 2021,12, 705021. [CrossRef]
58.
Varandas, L.; Faria, J.; Gaspar, P.D.; Aguiar, M.L. Low-Cost IoT Remote Sensor Mesh for Large-Scale Orchard Monitorization.
J. Sens. Actuator Netw. 2020,9, 44. [CrossRef]
59.
Dube, E.E. Wireless Farming: A Mobile and Wireless Sensor Network Based Application to Create Farm Field Monitoring and Plant
Protection for Sustainable Crop Production and Poverty Reduction Reduction; Malmö University: Malmö, Swedem, 2013.
60.
Rajasekaran, T.; Anandamurugan, S. Challenges and Applications of Wireless Sensor Networks in Smart Farming—A Survey; Springer:
Singapore, 2018; Volume 750, pp. 353–361. [CrossRef]
61.
Canakci, M.; Emekli, N.Y.; Bilgin, S.; Caglayan, N. Heating requirement and its costs in greenhouse structures: A case study for
Mediterranean region of Turkey. Renew. Sustain. Energy Rev. 2013,24, 483–490. [CrossRef]
62.
Antony, A.P.; Leith, K.; Jolley, C.; Lu, J.; Sweeney, D.J. A Review of Practice and Implementation of the Internet of Things (IoT) for
Smallholder Agriculture. Sustainability 2020,12, 3750. [CrossRef]
63.
Zhang, K.; Lv, H.; Zheng, Y.; Yao, Y.; Li, X.; Yu, J.; Ding, B. Nanofibrous hydrogels embedded with phase-change materials:
Temperature-responsive dressings for accelerating skin wound healing. Compos. Commun. 2021,25, 100752. [CrossRef]
64.
Allakhverdiev, S.I. Photosynthetic and biomimetic hydrogen production. Int. J. Hydrogen Energy
2012
,37, 8744–8752. [CrossRef]
65.
Kishore, H. Economic and Environmental Impact of AI and IOT Based Precision Agriculture on Small and Mid-Scale Farmers Economic
and Environmental Impact of AI and IOT Based Precision Agriculture On Small and Mid-Scale Farmers; Bournemouth University:
Bournemouth, UK, 2020; pp. 1–4.
66.
Sonneveld, P.; Swinkels, G.; Campen, J.; van Tuijl, B.; Janssen, H.; Bot, G. Performance results of a solar greenhouse combining
electrical and thermal energy production. Biosyst. Eng. 2010,106, 48–57. [CrossRef]
67.
Alsalam, B.H.Y.; Morton, K.; Campbell, D.; Gonzalez, F. Autonomous UAV with vision based on-board decision making for remote
sensing and precision agriculture. In Proceedings of the 2017 IEEE Aerospace Conference, Big Sky, MT, USA,
4–11 March 2017
;
pp. 1–12.
68.
Foresti, R.; Rossi, S.; Magnani, M.; Bianco, C.G.L.; Delmonte, N. Smart Society and Artificial Intelligence: Big Data Scheduling
and the Global Standard Method Applied to Smart Maintenance. Engineering 2020,6, 835–846. [CrossRef]
69.
Patil, V.C.; Al-Gaadi, K.A.; Biradar, D.P.; Rangaswamy, M. Internet of Things (Iot) and Cloud Computing for Agriculture: An
Overview. In Proceedings of the Agro-Informatics and Precision Agriculture (AIPA 2012), Hyderabad, India, 1 August 2012;
pp. 292–296.
70.
Hernández-morales, C.A.; Luna-rivera, J.M.; Perez-jimenez, R. Design and deployment of a practical IoT-based monitoring
system for protected cultivations. Comput. Commun. 2022,186, 51–64. [CrossRef]
71.
Sagheer, A.; Mohammed, M.; Riad, K.; Alhajhoj, M. A Cloud-Based IoT Platform for Precision Control of Soilless Greenhouse
Cultivation. Sensors 2020,21, 223. [CrossRef]
72. Rojas, A. Smart Agriculture IoT with Cloud Computing. Rev. Hist. América 2015,29, 37–66.
73.
Park, J.H.; Lee, S.; Yun, S.; Kim, H.; Kim, W.-T. Dependable Fire Detection System with Multifunctional Artificial Intelligence
Framework. Sensors 2019,19, 2025. [CrossRef] [PubMed]
74.
Lemayian, J.P.; Hamamreh, J.M. Drone-Based Smart Rescue System for Critical Situation Management in Future Wireless
Networks. RS Open J. Innov. Commun. Technol. 2020,1, 1–12.
75.
Bo, Y.; Wang, H. The Application of Cloud Computing and the Internet of Things in Agriculture and Forestry. In Proceedings of
the 2011 International Joint Conference on Service Sciences, Taipei, Taiwan, 25–27 May 2011; pp. 168–172. [CrossRef]
76.
He, M.; Shi, J. Circulation traceability system of Chinese herbal medicine supply chain based on internet of things agricultural
sensor. Sustain. Comput. Inform. Syst. 2021,30, 100518. [CrossRef]
77.
Singh, R.K.; Berkvens, R.; Weyn, M. Energy Efficient Wireless Communication for IoT Enabled Greenhouses. In Proceedings of
the 2020 International Conference on Communication Systems & NETworkS (COMSNETS), Bengaluru, India, 7–11 January 2020;
pp. 885–887. [CrossRef]
78.
Madushanki, R.; Wirasagoda, H.; Halgamuge, M.; Syed, A. Adoption of the Internet of Things (IoT) in agriculture and smart
farming towards urban greening: A review. Int. J. Adv. Comput. Sci. Appl. 2019,10, 11–28. [CrossRef]
79.
Agrawal, H.; Prieto, J.; Ramos, C.; Corchado, J.M. Smart feeding in farming through IoT in silos. Adv. Intell. Syst. Comput.
2016
,
530, 355–366. [CrossRef]
80.
Duckett, T.; Pearson, S.; Blackmore, S.; Grieve, B. Agricultural Robotics: The Future of Robotic Agriculture. arXiv
2018
,
arXiv:1806.06762.
81.
Vlachopoulou, M.; Ziakis, C.; Vergidis, K.; Madas, M. Analyzing AgriFood-Tech e-Business Models. Sustainability
2021
,
13, 5516
.
[CrossRef]
82.
Wang, T.; Xu, X.; Wang, C.; Li, Z.; Li, D. From Smart Farming towards Unmanned Farms: A New Mode of Agricultural Production.
Agriculture 2021,11, 145. [CrossRef]
83.
Peng, Y.; Liu, Y.; Yang, Y.; Yang, Y.; Liu, N.; Sun, Y. Research progress on application of soft robotic gripper in fruit and vegetable
picking. Trans. Chin. Soc. Agric. Eng. 2018,34, 11–20.
84.
Hussein, Z.; Fawole, O.; Opara, U.L. Harvest and Postharvest Factors Affecting Bruise Damage of Fresh Fruits. Hortic. Plant J.
2019,6, 1–13. [CrossRef]

Biomimetics 2022,7, 69 27 of 31
85.
Martinez-Romero, D.; Serrano, M.; Carbonell, A.; Castillo, S.; Riquelme, F.; Valero, D. Mechanical Damage During Fruit Post-
Harvest Handling: Technical and Physiological Implications BT—Production Practices and Quality Assessment of Food Crops:
Quality Handling and Evaluation. In Production Practices and Quality Assessment of Food Crops; Dris, R., Jain, M.S., Eds.; Springer:
Dordrecht, The Netherlands, 2004; pp. 233–252.
86.
Navas, E.; Fernández, R.; Sepúlveda, D.; Armada, M.; Gonzalez-de-Santos, P. Soft grippers for automatic crop harvesting:
A review. Sensors 2021,21, 2689. [CrossRef] [PubMed]
87.
Biomimicry New England, Biomimetic Innovations Are Hindered by Challenges to Implementation, Not Inspiration.
2022.
Available
online: https://www.biomimicryne.org/blog/2016/5/10/biomimetic-innovations-are-hindered-by-their-
implementation-not-their-inspiration (accessed on 20 April 2022).
88.
Miranda, M.; Spricigo, P.C.; Ferreira, M.D. Mechanical damage during harvest and loading affect orange postharvest quality. Eng.
Agric. 2015,35, 154–162. [CrossRef]
89.
Porat, R.; Lichter, A.; Terry, L.A.; Harker, R.; Buzby, J. Postharvest losses of fruit and vegetables during retail and in consumers ’
homes: Quantifications, causes, and means of prevention. Postharvest Biol. Technol. 2018,139, 135–149. [CrossRef]
90.
Swarm Robotics for Agricultural Applications, SAGA Experiment. 2022. Available online: https://echord.eu/saga.html (accessed
on 25 April 2022).
91. James, A. Food and Farming Technology. Food Farming Technol. 2021.
92.
Dent, M.; Wang, Y. Agricultural Robotics Market 2022–2032. 2022. Available online: https://www.idtechex.com/zh/research-
report/agricultural-robotics-market- 2022-2032/837 (accessed on 22 April 2022).
93.
Zhang, E.M. Understanding the Acceptance of Mobile SMS Advertising among Young Chinese Consumers. Psychol. Mark.
2010
,
30, 461–469. [CrossRef]
94.
Assunção, E.T.; Gaspar, P.D.; Mesquita, R.J.M.; Simões, M.P.; Ramos, A.; Proença, H.; Inacio, P.R.M. Peaches Detection Using a
Deep Learning Technique—A Contribution to Yield Estimation, Resources Management, and Circular Economy. Climate
2022
,
10, 11. [CrossRef]
95.
Nityananda, V.; Read, J. Stereopsis in animals: Evolution, function and mechanisms. J. Exp. Biol.
2017
,220, 2502–2512. [CrossRef]
96.
Vázquez-Arellano, M.; Griepentrog, H.W.; Reiser, D.; Paraforos, D.S. 3-D Imaging Systems for Agricultural Applications—A
Review. Sensors 2016,16, 618. [CrossRef]
97.
Chu, X.; Miao, P.; Zhang, K.; Wei, H.; Fu, H.; Liu, H.; Jiang, H.; Ma, Z. Green Banana Maturity Classification and Quality
Evaluation Using Hyperspectral Imaging. Agriculture 2022,12, 530. [CrossRef]
98.
Psiroukis, V.; Espejo-Garcia, B.; Chitos, A.; Dedousis, A.; Karantzalos, K.; Fountas, S. Assessment of Different Object Detectors for
the Maturity Level Classification of Broccoli Crops Using UAV Imagery. Remote Sens. 2022,14, 731. [CrossRef]
99.
Trieu, N.M.; Thinh, N.T. Quality Classification of Dragon Fruits Based on External Performance Using a Convolutional Neural
Network. Appl. Sci. 2021,11, 10558. [CrossRef]
100.
Zhang, G.; Fu, Q.; Fu, Z.; Li, X.; Mateti´c, M.; Bakaric, M.B.; Jemri´c, T. A Comprehensive Peach Fruit Quality Evaluation Method
for Grading and Consumption. Appl. Sci. 2020,10, 1348. [CrossRef]
101.
Abbaspour-Gilandeh, Y.; Aghabara, A.; Davari, M.; Maja, J.M. Feasibility of Using Computer Vision and Artificial Intelligence
Techniques in Detection of Some Apple Pests and Diseases. Appl. Sci. 2022,12, 906. [CrossRef]
102.
Rasheed, S.U.; Muhammad, W.; Qaiser, I.; Irshad, M.J. A Multispectral Pest-Detection Algorithm for Precision Agriculture. Eng.
Proc. 2021,12, 46. [CrossRef]
103.
Kragh, M.F.; Christiansen, P.; Laursen, M.; Steen, K.A.; Green, O.; Karstoft, H.; Jørgensen, R.N. FieldSAFE: Dataset for Obstacle
Detection in Agriculture. Sensors 2017,17, 2579. [CrossRef]
104.
Yun, C.; Kim, H.-J.; Jeon, C.-W.; Gang, M.; Lee, W.S.; Han, J.G. Stereovision-based ridge-furrow detection and tracking for
auto-guided cultivator. Comput. Electron. Agric. 2021,191, 106490. [CrossRef]
105. Navarro, I.; Matía, F. An Introduction to Swarm Robotics. ISRN Robot. 2013,2013, 608164. [CrossRef]
106.
Jevti´c, A.; Gazi, P.; Andina, D.; Jamshidi, M.O. Building a swarm of robotic bees. In Proceedings of the 2010 World Automation
Congress, Kobe, Japan, 19–23 September 2010; pp. 1–6.
107.
Gonzalez-De-Santos, P.; Ribeiro, A.; Fernandez-Quintanilla, C.; Lopez-Granados, F.; Brandstoetter, M.; Tomic, S.; Pedrazzi, S.;
Peruzzi, A.; Pajares, G.; Kaplanis, G.; et al. Fleets of robots for environmentally-safe pest control in agriculture. Precis. Agric.
2016
,
18, 574–614. [CrossRef]
108.
BlueWeave Consulting, Global Agricultural Robots Market to Surpass 81 Billion by 2028, at a CAGR of 9.9% during the
Forecast
Period
. 2022. Available online: https://www.globenewswire.com/news-release/2022/03/21/2406862/0/en/
Global-Agricultural-Robots-Market-to-Surpass-81-Billion-by-2028-at-a-CAGR-of-9-9-during-the-forecast-period-BlueWeave-
Consulting.html (accessed on 22 April 2022).
109.
Markets and Markets, Agricultural Robots Market by Type (Milking Robots, UAVs/Drones, Automated Harvesting Systems,
Driverless Tractors), Farm Produce, Farming Environment (Indoor, Outdoor), Application, and Geography—Global Forecast to
2026. 2022. Available online: https://www.marketsandmarkets.com/Market-Reports/agricultural-robot-market- 173601759.html
(accessed on 22 April 2022).
110.
Caprari, G.; Colot, A.; Siegwart, R.; Halloy, J.; Deneubourg, J.-L. Animal and robot mixed societies—Building cooperation between
microrobots and cockroaches. IEEE Robot. Autom. Mag. 2005,12, 58–65. [CrossRef]

Biomimetics 2022,7, 69 28 of 31
111.
Hansjörg Wyss Institute for Biologically Inspired Engineering, RoboBees: Autonomous Flying Microrobots. 2022. Available
online: https://wyss.harvard.edu/technology/robobees-autonomous-flying- microrobots/ (accessed on 20 April 2022).
112.
University of Exeter, Biohybrid Microrobots inspired by Microbes. 2022. Available online: https://www.exeter.ac.uk/research/
livingsystems/research/biohybrid-microrobots/ (accessed on 21 April 2022).
113. Tan, Y.; Zheng, Z.-Y. Research Advance in Swarm Robotics. Def. Technol. 2013,9, 18–39. [CrossRef]
114.
International Union for Conservation of Nature, Nearly One in 10 Wild Bee Species Face Extinction in Europe While the Status of
More Than Half Remains unknown. 2015. Available online: https://www.iucn.org/content/nearly-one-10-wild-bee-species-
face-extinction-europe-while-status-more-half- remains-unknown-iucn-report (accessed on 23 April 2022).
115.
Jefferson, N.; Raspe, P.; Tran, N. Agricultural Swarm Robotics with Distributed Sensing. Major Qualification Project for Bachelor
Degree, Worcester Polytechnic Institute 2017. Available online: https://digital.wpi.edu/downloads/w3763840s (accessed on
21 April 2022).
116.
Schranz, M.; Umlauft, M.; Sende, M.; Elmenreich, W. Swarm Robotic Behaviors and Current Applications. Front. Robot. AI
2020
,
7, 36. [CrossRef] [PubMed]
117.
Elijah, O.; Rahman, T.A.; Orikumhi, I.; Leow, C.Y.; Hindia, M.N. An Overview of Internet of Things (IoT) and Data Analytics in
Agriculture: Benefits and Challenges. IEEE Internet Things J. 2018,5, 3758–3773. [CrossRef]
118.
Carbone, C.; Garibaldi, O.; Kurt, Z. Swarm Robotics as a Solution to Crops Inspection for Precision Agriculture. KnE Eng.
2018
,3,
552–562. [CrossRef]
119.
Arugga AI Farming. Robots Treating Every Plant. 2022. Available online: https://www.arugga.com/ (accessed on 20 April 2022).
120.
Cherney, M. Buzz Off, Bees. Pollination Robots Are Here. Wallstreet Journal, 2021. Available online: https://www.wsj.com/
articles/buzz-off-bees-pollination-robots-are-here-11625673660(accessed on 20 April 2022).
121. Boffey, D. Robotic Bees Could Pollinate Plants in Case of Insect Apocalypse. Guardian, 2018.
122.
US Environmental Protection Agency. Understanding How Pesticide Exposure Affects Honey Bee Colonies. 2018. Available
online: https://www.epa.gov/sciencematters/understanding-how-pesticide-exposure-affects-honey-bee- colonies (accessed
on 20 April 2022).
123.
Potts, S.G.; Neumann, P.; Vaissière, B.; Vereecken, N.J. Robotic bees for crop pollination: Why drones cannot replace biodiversity.
Sci. Total Environ. 2018,642, 665–667. [CrossRef] [PubMed]
124.
University of Sussex. Goulson Lab: Are Robotic Bees the Future? 2022. Available online: http://www.sussex.ac.uk/lifesci/
goulsonlab/blog/robotic-bees (accessed on 22 April 2022).
125.
Weiss, T. Skyx: Precise Agriculture Spraying by a Swarm of Drones. 2017. Available online: https://blog.startupnationcentral.
org/agritech/skyx-agriculture-swarm- drones/ (accessed on 28 April 2022).
126.
Wang, S.; Li, X.; Zeng, A.; Song, J.; Xu, T.; Lv, X.; He, X. Effects of Adjuvants on Spraying Characteristics and Control Efficacy in
Unmanned Aerial Application. Agriculture 2022,12, 138. [CrossRef]
127.
Loukatos, D.; Templalexis, C.; Lentzou, D.; Xanthopoulos, G.; Arvanitis, K.G. Enhancing a flexible robotic spraying platform for
distant plant inspection via high-quality thermal imagery data. Comput. Electron. Agric. 2021,190, 106462. [CrossRef]
128.
Sammons, P.J.; Furukawa, T.; Bulgin, A. Autonomous pesticide spraying robot for use in a greenhouse, in Proceedings of the 2005
Australasian Conference on Robotics and Automation, ACRA 2005. Syndey, Australia, 5–7 December 2005; pp. 1–9.
129. Jian-Sheng, P. An Intelligent Robot System for Spraying Pesticides. Open Appl. Inform. J. 2014,8, 435–444. [CrossRef]
130.
European Commission. Agricultural-robotics technology company enabling a modular swarm of autonomous drones for
spraying. 2022. Available online: https://cordis.europa.eu/project/id/836621 (accessed on 20 April 2022).
131.
Borrás, J. Swarm of Tiny Robots Could Help Eliminate Pesticides, eTransport News. 7 August 2020. Available online: https:
//etransportnews.com/2020/08/07/swarm-of-tiny-robots-could-help-eliminate-pesticides/ (accessed on 26 April 2022).
132. Dorigo, M.; Theraulaz, G.; Trianni, V. Reflections on the future of swarm robotics. Sci. Robot. 2020,5. [CrossRef]
133.
Harvard University, Better Bees: Progress Towards Robotic Pollinators. 2022. Available online: https://sitn.hms.harvard.edu/
flash/2017/bees-better-progress-made-towards-robotic-pollinators/ (accessed on 21 April 2022).
134.
Carolan, M. Automated agrifood futures: Robotics, labor and the distributive politics of digital agriculture. J. Peasant Stud.
2019
,
47, 184–207. [CrossRef]
135. Labios, L. These New Soft Actuators Could Make Soft Robots Less Bulky; UC San Diego: La Jolla, CA, USA, 2019.
136.
Chen, H.-Y.; Diteesawat, R.S.; Haynes, A.; Partridge, A.J.; Simons, M.F.; Werner, E.; Garrad, M.; Rossiter, J.; Conn, A.T. RUBIC:
An Untethered Soft Robot with Discrete Path Following. Front. Robot. AI 2019,6, 52. [CrossRef]
137. Melin, H.E. Analysis of the Climate Impact of Lithium-Ion Batteries and How to Measure It. Circular Energy Storage- Research
and Consulting. 2019, pp. 1–17. Available online: https://www.transportenvironment.org/sites/te/files/publications/2019_11_
Analysis_CO2_footprint_lithium-ion_batteries.pdf (accessed on 25 April 2022).
138.
Miletitch, R.; Dorigo, M.; Trianni, V. Balancing exploitation of renewable resources by a robot swarm. Swarm Intell.
2018
,12,
307–326. [CrossRef]
139.
Anderson, A.; Lindell, C.; Moxcey, K.; Siemer, W.; Linz, G.; Curtis, P.; Carroll, J.; Burrows, C.; Boulanger, J.; Steensma, K.; et al.
Bird damage to select fruit crops: The cost of damage and the benefits of control in five states. Crop. Prot.
2013
,52, 103–109.
[CrossRef]
140.
USDA’s Specialty Crop Research Initiative. The Economic Impact of Bird Damage to Select Fruit Crops in New York; USDA: Washington,
DC, USA, 2014; pp. 1–7.

Biomimetics 2022,7, 69 29 of 31
141.
USDA. Reducing Bird Damage to Agriculture Contact; USDA—National Wildlife Research Center: Washington, DC, USA, 2019; p. 1.
142.
Muller, B.; Clothier, R.; Watkins, S.; Fisher, A. Design of Bio-Inspired Autonomous Aircra for Bird Management Design of
Bio-Inspired Autonomous Aircraft for Bird Management. In Proceedings of the AIAC16: 16th Australian International Aerospace
Congress, Melbourne, Australia, 23–24 February 2015; pp. 23–24.
143.
Bird Gard. Australias #1 Selling Bird Deterrents & Pest Control Systems. 2022. Available online: https://www.birdgard.com.au/
(accessed on 20 April 2022).
144.
Mesquita, R.; Gaspar, P.D. A Novel Path Planning Optimization Algorithm Based on Particle Swarm Optimization for UAVs for
Bird Monitoring and Repelling. Processes 2022,10, 62. [CrossRef]
145.
Loukatos, D.; Petrongonas, E.; Manes, K.; Kyrtopoulos, I.-V.; Dimou, V.; Arvanitis, K.G. A Synergy of Innovative Technologies
towards Implementing an Autonomous DIY Electric Vehicle for Harvester-Assisting Purposes. Machines 2021,9, 82. [CrossRef]
146.
Boston Dynamics, Changing Your Idea of What Robots Can Do. 2022. Available online: https://www.bostondynamics.com/
(accessed on 20 April 2022).
147.
Nield, D. Video Shows Boston Dynamics’ Robot Dog Herding Sheep and Checking Crops, Science Alert. 2020. Available online:
https://www.sciencealert.com/spot-the-robot-dog-is-now-herding-sheep-in- new-zealand (accessed on 20 April 2022).
148.
Hayward, A. Meet SwagBot, the Robot Cowboy That Can Herd and Monitor Cattle on Its Own, Smithsonian, modern farmer.
18 July 2016. Available online: https://modernfarmer.com/2016/07/swagbot/ (accessed on 20 April 2022).
149.
Kleen, A. Cattle-herding robot Swagbot makes debut on Australian farms. 2016. Available online: https://www.newscientist.
com/article/2097004-cattle-herding-robot-swagbot- makes-debut- on-australian- farms/ (accessed on 20 April 2022).
150.
Fue, K.G.; Porter, W.M.; Barnes, E.M.; Rains, G.C. An Extensive Review of Mobile Agricultural Robotics for Field Operations:
Focus on Cotton Harvesting. AgriEngineering 2020,2, 150–174. [CrossRef]
151.
Li, X.; Huang, H.; Savkin, A.V.; Zhang, J. Robotic Herding of Farm Animals Using a Network of Barking Aerial Drones. Drones
2022,6, 29. [CrossRef]
152.
Cong, P.; Cheng, Y. Advances in geopolymer materials: A comprehensive review. J. Traffic Transp. Eng. (Engl. Ed.)
2021
,8, 283–314.
[CrossRef]
153. Maraveas, C.; Bartzanas, T. Sensors for structural health monitoring of agricultural structures. Sensors 2021,21, 314. [CrossRef]
154.
De Belie, N.; Sonck, B.; Braam, C.R.; Lenehan, J.J.; Svennerstedt, B.; Richardson, M. Durability of Building Materials and
Components in the Agricultural Environment, Part II: Metal Structures. J. Agric. Eng. Res. 2000,75, 333–347. [CrossRef]
155.
Baeza, E.J.; Van Breugel, A.J.B.; Hemming, S.; Stanghellini, C. Smart greenhouse covers: A look into the future. Acta Hortic.
2020
,
213–224. [CrossRef]
156.
Ou, C.; Yang, J.; Du, Z.; Liu, Y.; Feng, Q.; Zhu, D. Long-Term Mapping of a Greenhouse in a Typical Protected Agricultural Region
Using Landsat Imagery and the Google Earth Engine. Remote Sens. 2019,12, 55. [CrossRef]
157.
Li, C.; Wang, H.; Miao, H.; Ye, B. The economic and social performance of integrated photovoltaic and agricultural greenhouses
systems: Case study in China. Appl. Energy 2017,190, 204–212. [CrossRef]
158.
Lazarus, B.S.; Velasco-Hogan, A.; Río, T.G.-D.; Meyers, M.A.; Jasiuk, I. A review of impact resistant biological and bioinspired
materials and structures. J. Mater. Res. Technol. 2020,9, 15705–15738. [CrossRef]
159.
Calvert, P.; Azhari, A. Biomimetic Materials: Properties and Processing. In Reference Module in Materials Science and Materials
Engineering; Elsevier: Amsterdam, The Netherlands, 2016.
160.
McKenzie, R.M.; Barraclough, T.W.; Stokes, A.A. Integrating Soft Robotics with the Robot Operating System: A Hybrid Pick and
Place Arm. Front. Robot. AI 2017,4, 39. [CrossRef]
161.
Athanassopoulos, C.; Kohlman, A.; Henderson, M.; Kaul, J.; Boschuk, J. Permeability, puncture, and shear strength testing of
composite liner systems under high normal loads. In Proceedings of the Thirteenth International Conference on Tailings and
Mine Waste, Banff, AB, Canada, 1–4 November 2009, pp. 105–118.
162. Vincent, J.F.V. Biomimetic Material. In Materials Experience; Butterworth-Heinemann: Oxford, UK, 2014.
163.
Kootstra, G.; Wang, X.; Blok, P.M.; Hemming, J.; Van Henten, E. Selective Harvesting Robotics: Current Research, Trends, and
Future Directions. Curr. Robot. Rep. 2021,2, 95–104. [CrossRef]
164. Benson, T. Researchers have Developed a Durable Robot Bee. 2019. Available online: https://www.inverse.com/article/60697-
robot-bee-black-mirror-research-disaster (accessed on 20 April 2022).
165.
Helmer, J. The Race to Build a Better Bee. JSTOR. 21 August 2018. Available online: https://daily.jstor.org/the-race-to-build-a-
better-bee/ (accessed on 25 April 2022).
166.
Fundazioa, E. Precision Control of Movement in Robots. 2008. Available online: https://phys.org/news/2008-05-precision-
movement-robots.html (accessed on 20 April 2022).
167.
Dobrza´nski, L.A.; Dobrza ´nska-Danikiewicz, A.D.; Gawel, T. Computer-aided design and selective laser melting of porous
biomimetic materials. Adv. Mater. Process. Technol. 2016,3, 70–82. [CrossRef]
168.
Hu, K.; Lin, K.; Gu, D.; Yang, J.; Wang, H.; Yuan, L. Mechanical properties and deformation behavior under compressive loading
of selective laser melting processed bio-inspired sandwich structures. Mater. Sci. Eng. A 2019,762, 138089. [CrossRef]
169. Li, J.; Yu, X.; Martinez, E.E.; Zhu, J.; Wang, T.; Shi, S.; Guo, C. Emerging Biopolymer-Based Bioadhesives. Macromol. Biosci. 2022,
22, 2100340. [CrossRef]
170.
Bhattacharya, T.; Rather, G.; Akter, R.; Kabir, T.; Rauf, A.; Rahman, H. Nutraceuticals and Bio-inspired Materials from Microalgae
and their Future Perspectives. Curr. Top. Med. Chem. 2021,21, 1037–1051. [CrossRef]

Biomimetics 2022,7, 69 30 of 31
171.
Mattocks, J.A.; Cotruvo, J.A. Biological, biomolecular, and bio-inspired strategies for detection, extraction, and separations of
lanthanides and actinides. Chem. Soc. Rev. 2020,49, 8315–8334. [CrossRef] [PubMed]
172.
Wei, X.; Huang, T.; Nie, J.; Yang, J.-H.; Qi, X.-D.; Zhou, Z.-W.; Wang, Y. Bio-inspired functionalization of microcrystalline cellulose
aerogel with high adsorption performance toward dyes. Carbohydr. Polym. 2018,198, 546–555. [CrossRef] [PubMed]
173.
Akinyemi, B.A.; Orogbade, B.O.; Ogheneyome, A.; Abeer, M.A.; Khan, A.; Mahmoud, A.H.; Asiri, A. Influence of alkali activators
on thermo-physical properties of ecofriendly unfired clay bricks from anthill mounds. Eur. J. Environ. Civ. Eng.
2021
, 1–13.
[CrossRef]
174.
Prasad, R.; Ali, M.; Kwan, P.; Khan, H. Designing a multi-stage multivariate empirical mode decomposition coupled with ant
colony optimization and random forest model to forecast monthly solar radiation. Appl. Energy 2018,236, 778–792. [CrossRef]
175.
Huang, L.; Deng, L.; Li, A.; Gao, R.; Zhang, L.; Lei, W. Analytical model for solar radiation transmitting the curved transparent
surface of solar greenhouse. J. Build. Eng. 2020,32, 101785. [CrossRef]
176.
Li, B.; Shi, B.J.; Yao, Z.Z.; Shukla, M.K.; Du, T.S. Energy partitioning and microclimate of solar greenhouse under drip and furrow
irrigation systems. Agric. Water Manag. 2020,234, 106096. [CrossRef]
177.
Piromalis, D.; Arvanitis, K. SensoTube: A Scalable Hardware Design Architecture for Wireless Sensors and Actuators Networks
Nodes in the Agricultural Domain. Sensors 2016,16, 1227. [CrossRef]
178.
Singh, R.; Aernouts, M.; De Meyer, M.; Weyn, M.; Berkvens, R. Leveraging LoRaWAN Technology for Precision Agriculture in
Greenhouses. Sensors 2020,20, 1827. [CrossRef]
179.
Sofo, A.; Mininni, A.N.; Ricciuti, P. Soil Macrofauna: A key Factor for Increasing Soil Fertility and Promoting Sustainable Soil Use
in Fruit Orchard Agrosystems. Agronomy 2020,10, 456. [CrossRef]
180.
Papasolomontos, A.; Baudoin, W.; Lutaladio, N. Good Agricultural Practices for Greenhouse Vegetable Crops: Principles for Mediter-
ranean Climate Areas; FAO: Rome, Italy, 2013.
181.
Oliveira, P.M.; Pires, E.S.; Boaventura-Cunha, J.; Pinho, T.M. Review of nature and biologically inspired metaheuristics for
greenhouse environment control. Trans. Inst. Meas. Control 2020,42, 2338–2358. [CrossRef]
182.
Colmenares-Quintero, R.F.; Góez-Sánchez, G.D.; Colmenares-Quintero, J.C.; Latorre-Noguera, L.F.; Kasperczyk, D. Application of
a simulation tool based on a bio-inspired algorithm for optimisation of distributed power generation systems. Cogent Eng.
2021
,
8, 1909791. [CrossRef]
183.
The Pennsylvania State University. Things a Greenhouse Grower Can Do to Improve Energy Efficiency; The Pennsylvania State
University: State College, PA, USA, 2016.
184.
Trypanagnostopoulos, G.; Kavga, A.; Souliotis, M. Greenhouse performance results for roof installed photovoltaics. Renew. Energy
2017,111, 724–731. [CrossRef]
185.
La Notte, L.; Giordano, L.; Calabrò, E.; Bedini, R.; Colla, G.; Puglisi, G.; Reale, A. Hybrid and organic photovoltaics for greenhouse
applications. Appl. Energy 2020,278, 115582. [CrossRef]
186.
Zisis, C.; Pechlivani, E.; Tsimikli, S.; Mekeridis, E.; Laskarakis, A.; Logothetidis, S. Organic Photovoltaics on Greenhouse Rooftops:
Effects on Plant Growth. Mater. Today Proc. 2019,19, 65–72. [CrossRef]
187.
Chaysaz, A.; Seyedi, S.R.M.; Motevali, A. Effects of different greenhouse coverings on energy parameters of a photovoltaic–thermal
solar system. Sol. Energy 2019,194, 519–529. [CrossRef]
188.
Huang, L.; Deng, L.; Li, A.; Gao, R.; Zhang, L.; Lei, W. A novel approach for solar greenhouse air temperature and heating load
prediction based on Laplace transform. J. Build. Eng. 2021,44, 102682. [CrossRef]
189.
Srinivasan, G.; Muthukumar, P. A review on solar greenhouse dryer: Design, thermal modelling, energy, economic and
environmental aspects. Sol. Energy 2021,229, 3–21. [CrossRef]
190.
Korecko, J.; Jirka, V.; Sourek, B.; Cerveny, J. Module greenhouse with high efficiency of transformation of solar energy, utilizing
active and passive glass optical rasters. Sol. Energy 2010,84, 1794–1808. [CrossRef]
191.
Wang, D.; Liu, H.; Li, Y.; Zhou, G.; Zhan, L.; Zhu, H.; Lu, X.; Chen, H.; Li, C.-Z. High-performance and eco-friendly semitransparent
organic solar cells for greenhouse applications. Joule 2021,5, 945–957. [CrossRef]
192.
Shankar, A.; Vijayakumar, K.; Babu, B.C. Energy saving potential through artificial lighting system in PV integrated smart
buildings. J. Build. Eng. 2021,43, 103080. [CrossRef]
193.
Capraro, F.; Patiño, D.; Tosetti, S.; Schugurensky, C. Neural network-based irrigation control for precision agriculture. In
Proceedings
of the 2008 IEEE International Conference on Networking, Sensing and Control, Sanya, China, 6–8 April 2008;
pp. 357–362.
194.
Pérez-Alonso, J.; Pérez-García, M.; Pasamontes-Romera, M.; Callejón-Ferre, A.J. Performance analysis and neural modelling of a
greenhouse integrated photovoltaic system. Renew. Sustain. Energy Rev. 2012,16, 4675–4685. [CrossRef]
195.
Carballo, J.A.; Bonilla, J.; Roca, L.; Berenguel, M. New low-cost solar tracking system based on open source hardware for
educational purposes. Sol. Energy 2018,174, 826–836. [CrossRef]
196.
Qian, X.; Zhao, Y.; Alsaid, Y.; Wang, X.; Hua, M.; Galy, T.; Gopalakrishna, H.; Yang, Y.; Cui, J.; Liu, N.; et al. Artificial phototropism
for omnidirectional tracking and harvesting of light. Nat. Nanotechnol. 2019,14, 1048–1055. [CrossRef] [PubMed]
197.
Yang, M.; Xu, Y.; Zhang, X.; Bisoyi, H.K.; Xue, P.; Yang, Y.; Yang, X.; Valenzuela, C.; Chen, Y.; Wang, L.; et al. Bioinspired
Phototropic MXene-Reinforced Soft Tubular Actuators for Omnidirectional Light-Tracking and Adaptive Photovoltaics. Adv.
Funct. Mater. 2022. [CrossRef]

Biomimetics 2022,7, 69 31 of 31
198.
Syers, J.K. Efficiency of soil and fertilizer phosphorus use: Reconciling changing concepts of soil phosphorus behaviour with
agronomic information. FAO Fertil. Plant Nutr. Bull. 2018,18, 1–108.
199. FAO. The Impact of Disasters and Crises on Agriculture and Food Security; FAO: Rome, Italy, 2017; pp. 1–168.
200. OECD. Water and Agriculture; OECD: Paris, France, 2022.
201.
Bao, J.; Lu, W.-H.; Zhao, J.; Bi, X.T. Greenhouses for CO2 sequestration from atmosphere. Carbon Resour. Convers.
2018
,1, 183–190.
[CrossRef]
202.
Singh, G.G.; Hilmi, N.; Bernhardt, J.R.; Montemayor, A.M.C.; Cashion, M.; Ota, Y.; Acar, S.; Brown, J.M.; Cottrell, R.; Djoundourian,
S.; et al. Climate impacts on the ocean are making the Sustainable Development Goals a moving target travelling away from us.
People Nat. 2019,1, 317–330. [CrossRef]
203.
Adekalu, K.; Balogun, J.; Aluko, O.; Okunade, D.A.; Gowing, J.W.; Faborode, M. Runoff water harvesting for dry spell mitigation
for cowpea in the savannah belt of Nigeria. Agric. Water Manag. 2009,96, 1502–1508. [CrossRef]
204.
Freie Universität Berlin. Macrocatchment water Harvesting. 2022. Available online: https://www.geo.fu-berlin.de/en/v/
iwrm/Implementation/technical_measures/Water-harvesting-techniques/macrocatchment_harvesting/index.html (accessed on
23 April 2022).
205.
Knapczyk-Korczak, J.; Stachewicz, U. Biomimicking spider webs for effective fog water harvesting with electrospun polymer
fibers. Nanoscale 2021,13, 16034–16051. [CrossRef]
206. Warka Water. Warka Water: Every Drop Counts. Available online: https://www.warkawater.org/ (accessed on 23 April 2022).
207.
Lovechio, N. Warka Water Tower: How to Collect Water from the Atmosphere. Available online: https://buildingcue.it/warka-
water-tower-how- to-collect- water-from-the-atmosphere/6097/ (accessed on 24 April 2022).
208.
Nørgaard, T.; Dacke, M. Fog-basking behaviour and water collection efficiency in Namib Desert Darkling beetles. Front. Zoöl.
2010,7, 23. [CrossRef]
209.
Zhai, L.; Berg, M.C.; Cebeci, F.; Kim, Y.; Milwid, J.M.; Rubner, M.F.; Cohen, R.E. Patterned Superhydrophobic Surfaces: Toward a
Synthetic Mimic of the Namib Desert Beetle. Nano Lett. 2006,6, 1213–1217. [CrossRef] [PubMed]
210.
Fenyvesi, K.; Park, H.G.; Choi, T.; Song, K.; Ahn, S. Modelling Environmental Problem-Solving through STEAM Activities: 4D
frames Warka Water Workshop. In Proceedings of the Bridges 2016: Mathematics, Music, Art, Architecture, Education, Culture,
Bridges, Jyväskylä, Finland, 9–13 August 2016; pp. 601–608.
211.
Aslan, D.K.; Selçuk, S.A. A Biomimetic Approach to Rainwater Harvesting Strategies Through the Use of Buildings. Eurasian J.
Civ. Eng. Archit. 2018,2, 27S–39S.