Drone and sensor technology for sustainable weed management: a review

Abstract

Weeds are amongst the most impacting abiotic factors in agriculture, causing important yield loss worldwide. Integrated Weed Management coupled with the use of Unmanned Aerial Vehicles (drones), allows for Site-Specific Weed Management, which is a highly efficient methodology as well as beneficial to the environment. The identification of weed patches in a cultivated field can be achieved by combining image acquisition by drones and further processing by machine learning techniques. Specific algorithms can be trained to manage weeds removal by Autonomous Weeding Robot systems via herbicide spray or mechanical procedures. However, scientific and technical understanding of the specific goals and available technology is necessary to rapidly advance in this field. In this review, we provide an overview of precision weed control with a focus on the potential and practical use of the most advanced sensors available in the market. Much effort is needed to fully understand weed population dynamics and their competition with crops so as to implement this approach in real agricultural contexts.

Full text

Espositoetal. Chem. Biol. Technol. Agric. (2021) 8:18
https://doi.org/10.1186/s40538-021-00217-8
REVIEW
Drone andsensor technology forsustainable
weed management: areview
Marco Esposito†, Mariano Crimaldi†, Valerio Cirillo* , Fabrizio Sarghini and Albino Maggio
Abstract
Weeds are amongst the most impacting abiotic factors in agriculture, causing important yield loss worldwide. Inte-
grated Weed Management coupled with the use of Unmanned Aerial Vehicles (drones), allows for Site-Specific Weed
Management, which is a highly efficient methodology as well as beneficial to the environment. The identification of
weed patches in a cultivated field can be achieved by combining image acquisition by drones and further process-
ing by machine learning techniques. Specific algorithms can be trained to manage weeds removal by Autonomous
Weeding Robot systems via herbicide spray or mechanical procedures. However, scientific and technical under-
standing of the specific goals and available technology is necessary to rapidly advance in this field. In this review, we
provide an overview of precision weed control with a focus on the potential and practical use of the most advanced
sensors available in the market. Much effort is needed to fully understand weed population dynamics and their com-
petition with crops so as to implement this approach in real agricultural contexts.
Keywords: Site-specific weed management, UAVs, Precision agriculture, Weed detection, Crop–weed interaction
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Introduction
Biotic threats such as insects, weeds, fungi, viruses, and
bacteria can broadly affect crop yield and quality. Among
these, weeds are the most impacting problem causing
remarkable yield loss worldwide [1]. e most char-
acterized effect of weeds is competition for resources
such as light [2], water [3], space [4], and nutrients [5].
In addition, specific chemical signals and/or toxic mol-
ecules produced by weeds may interfere with a normal
crop development [6]. A distinctive trait of wild species,
including weeds, is their high physiological, morphologi-
cal, and anatomical plasticity which makes them more
tolerant than crop species to environmental stressors [7–
10]. Moreover, weeds interact with other biological com-
ponents of the environment, acting as refuge for plant
pests such as insects, fungi, and bacteria that can harm
close in crops [11–13]. For example, wild oats (Avena
fatua L.) can harbor the etiological agents of the powdery
mildew in crops such as wheat (Triticum aestivum L.),
oats, and barley (Hordeum vulgare L.) [14]; altamisa (Par-
thenium hysterophorus L.) can be a secondary host of the
common hairy caterpillar (Diacrisia obliqua Walk.) [15,
16]; Cyperus rotundus can host the root-knot Meloido-
gyne graminicola and, therefore, can contribute to their
spreading in the field [17]. Finally, weed infestation may
affect fresh and processed products quality such as beer,
wine, forage [18, 19]. In this respect, weed residuals may
cause accumulation of off-flavors products [20, 21], or in
some cases, can make them harmful to humans and ani-
mals [22, 23]. Weeds may also contain high levels of aller-
gens and/or toxic metabolites that, if ingested, can cause
asthma, skin rash, and other reactions [24, 25].
Most weed research aims at developing strategies that
can reduce the deleterious impact of the interspecific
competition between crops and weeds and recent tech-
nological advances may further contribute to this scope,
while improving the sustainability of weed control [26–
28]. Worldwide, weed competition causes severe yield
reduction in all major crops, such as wheat (23%), soy-
bean (37%), rice (37%), maize (40%), cotton (36%), and
potato (30%) [1]. Yearly, weeds cause 50% yield losses of
Open Access
*Correspondence: valerio.cirillo@unina.it
†Marco Esposito and Mariano Crimaldi contributed equally to this work
Department of Agricultural Sciences, University of Naples Federico II, Via
Università 100, 80055 Portici, NA, Italy
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Espositoetal. Chem. Biol. Technol. Agric. (2021) 8:18
corn and soybean productivity in North America. For
corn, this equates to a loss of 148 million tons for an
economical loss of over $26.7 billion [29]. In Australia,
yield loss due to weeds accounts for 2.76 million tons of
grain from different plants, including wheat, barley, oats,
canola, sorghum, and pulses [30]. e annual global eco-
nomic loss caused by weeds has been estimated to be
more than $100 billion U.S. dollars [31], despite world-
wide annual herbicide sales in the range of $25 billion
[32]. In Europe, herbicides are the second most-sold pes-
ticides. ey accounted for 35% of all pesticide sales in
2018, overcoming insecticides and acaricides (Fig.1) [33].
Weed management requires anintegrated approach
In 2050, the world population will quadruplicate, reach-
ing 9.15 billion people [34]. However, the predicted
increase in food demand will be hardly met by the cur-
rent production system [35]. Also, climate change will be
an additional challenge for the human food supply in the
near future [36]. Among all the processes affecting crop
productivity, weed management will be one of the hard-
est challenges [37]. Mechanical and chemical weed con-
trol has disadvantages that probably will impede them
to be effective for future weed management [38–43].
Mechanical methods are scarcely efficient, and herbi-
cides have a high ecological impact. An approach that
minimizes the drawbacks of mechanical and chemical
weed control is Integrated Weed Management (IWM).
IWM combines chemical, biological, mechanical, and/
or crop management methods, and represents a model
to improve the efficiency and sustainability of weed con-
trol [3, 44]. In contrast to traditional methods, IWM
integrates several agro-ecological aspects such as the role
of conservation tillage and crop rotation on weeds seed
bank dynamics [10], the ability to forecast the critical
period of weed interference and their competition with
crops [45, 46], and the specific critical levels of crops/
weeds interaction [47]. erefore, an effective IWM must
rely on a thorough knowledge of crop-weeds competition
dynamics, which currently represents one of the most
active research areas in weed science [48, 49].
New technologies forsite‑specic weed management
Precision agriculture relies on technologies that combine
sensors, information systems, and informed management
to optimize crop productivity and to reduce the environ-
mental impact [50]. Nowadays, precision agriculture has
a broad range of applications and it is employed in dif-
ferent agricultural contexts including pests control [51],
fertilization, irrigation [52, 53], sowing [54] and harvest-
ing [55]. Precision agriculture can be effectively applied
to IWM also. In the last decade, precision agriculture
has rapidly advanced because of technological innova-
tions in the areas of sensors [56], computer hardware
[57], nanotechnology [58], unmanned vehicles systems
and robots [59] that may allow for specific identification
of weeds that are present in the field [47]. Unmanned
aerial vehicles (UAV) are one of the most successful tech-
nologies applied in precision agriculture [60]. Unmanned
Vehicles systems are mobile Aerial (UAV) or Terrestrial
(UTV) platforms that provide numerous advantages for
the execution and monitoring of farming activities [61].
UAVs can be highly valuable since they allow for Site-
Specific Weed Management (SSWM) (Fig.2). SSWM is
Fig. 1 Percentage (of total volume in kilograms) of pesticide sales by category in Europe in 2018 [33]
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an improved weed management approach for highly effi-
cient and environmentally safe control of weed popula-
tions [28], enabling precise and continuous monitoring
and mapping of weed infestation. SSWM consents to
optimize weed treatments for each specific agronomical
situation [62]. e combination of UAVs with advanced
cameras and sensors, able to discern specific weeds [63],
and GPS technologies, that provide geographical infor-
mation for field mapping, can help in precisely monitor-
ing large areas in a few minutes. anks to more accurate
planning of weed management that can increase mechan-
ical methods effectiveness and/or reduce herbicide
spread [64], the potential agro-ecological and economic
implications of SSWM are remarkable, yielding lower
production costs, reducing the onset of weed resistance,
improving biodiversity, and containing environmental
impacts [65]. e application of UAVs to weed control
can, therefore, contribute to improve the sustainability of
future agricultural production systems that must comply
with an increasing world population [34, 35].
UAVs remote sensing techniques andsensors
UAVs have become a common tool in precision agricul-
ture [66, 67]. anks to their affordability, user-friendli-
ness and versatility, UAVs are often the primary choice
for fast and precise in situ remote sensing or survey
operations. Despite their versatility, these systems may
be used for different purposes, depending on the sen-
sors they carry on. Ongoing research is looking at the
best solutions to integrate data collected from sensors on
UAVs, ground sensors and other data sources for better
management of punctual operations in the field, with a
particular focus on smart agriculture and big data man-
agement [68, 69].
Although UAVs systems do not offer the same territo-
rial coverage as satellites, they offer a spatial and temporal
resolution that other systems do not [70, 71]. From an
economic point of view, the use of drones requires the
investment to buy a UAV system with at least a 0.1cm/px
resolution RGB camera, a trained pilot for flight manage-
ment and post-processing software capabilities. e ini-
tial UAV investment is compensated by the repeatability
of flights, which increases the frequency of datasets deliv-
ered, and the higher resolution compared to other sys-
tems [72, 73]. UAVs systems also have further advantages:
(1) the possibility to collect easily deployable data in real
time (excluding post-processing); (2) they can be used to
survey areas with high level of hazard and/or difficult to
reach; (3) they allow operators to collect data even with
unfavorable weather conditions, such as in very cloudy or
foggy days, under which satellite detection systems fail or
produce very altered datasets [71]. e most important
sensors available as payload are mainly categorized into
three classes depending on the spectral length and num-
ber they can record:
• RGB (Red, Green, Blue) or VIS (Visible) sensors
• Multispectral sensors
• Hyperspectral sensors
RGB/VIS sensors
e RGB or VIS sensors are the most common and
largely available commercial cameras (Table 1). eir
possible applications have been the focus of most
research for years due to their potential and low-cost
operational requirements [74, 75].
ese sensors are used to calculate vegetation indices
such as the Green/Red Vegetation Index (GRVI), Green-
ness Index (GI) and Excessive Greenness (ExG) with
acceptable or high levels of accuracy [76, 77]. Also, RGB
sensors have been increasingly used for machine learn-
ing techniques in object recognition, phenology, pathol-
ogies, and similar purposes. e typical workflow of
processing RGB images from UAVs for remote sensing is:
1. pre-flight planning, 2. flight and image acquisition, 3.
post-processing and indexes or dataset extrapolation [71].
Phase 1 is critical and essential to collect data of useful
quality for the purpose. In the pre-flight planning phase,
the parameters to consider are the definition of the study
area, the flight altitude, site topography, weather fore-
cast and local regulations for unmanned flights. In phase
2, it is recommended to keep the data flow sufficient to
store data and to check if the acquisition platform can
acquire the amount of data required. It could be possi-
ble to encounter I/O errors due to the inadequacy of the
platform with consequent loss of information or abor-
tion of the mission. In phase 3, for RGB sensors, there is
no need to perform radiometric calibration, which is the
Fig. 2 Site-specific weed management (SSWM) scheme realized by
drones and its economical and agro-ecological implications
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case when using multispectral and hyperspectral sensors.
RGB data can be used per se or to create a georeferenced
orthomosaic. In this case, the individual images are recti-
fied, georeferenced using GPS data and stitched together
to form a single image (orthomosaic) covering the entire
study area. Orthomosaics can be generated either with
RGB values as they are or after calculating the desired
vegetation indices [77]. If RGB images are to be used
in machine learning algorithms, the workflow is differ-
ent [78–81]. In this case, it is necessary to collect a large
dataset of images for the training and testing of the algo-
rithm [82]. is dataset may already be available from
third-party sources, such as PlantVillage [83] or PlantDoc
[84]. Alternatively, it can be created from scratch if the
purpose of the research is not covered by existing data-
sets [85]. In this case, the acquisition, selection and pro-
cessing of the images are critical, because the final dataset
can affect both the training and the use of the neural net-
work, with risks of producing biased results [86].
Multispectral sensors
e multispectral sensors are used for a wider range of
calculations of vegetation indices as they can rely on a
higher number of radiometric bands. A comparison of
the most common multispectral sensors, specific for
UAV systems, is shown in Table2.
With multispectral sensors, the range of vegetation indi-
ces that can be monitored is considerably extended com-
pared to those that can be calculated with only three RGB
bands. Moreover, the workflow has minor variations. For
these sensors, in phase 1, the radiometric calibration and
atmospheric correction phases are strictly required. Many
multispectral sensors, such as the Micasense RedEdge
series or the Parrot Sequoia + , have downwelling irradi-
ance sensors and a calibrated reflectance panel to address
some of the requirements for radiometric calibration [87].
Due to a lower resolution of the sensors compared to RGB
ones, a lower flight altitude and an adequate horizontal
and vertical overlap of recorded images must be taken into
Table 1 RGB cameras andtheir main specications
a * UAV with already supplied camera. Payload not interchangeable
Camera model Sensor type
andresolution
[Mpx]
Sensor format Sensor Size [mm] Weight [kg] Price (approx.) [€]
Canon EOS 5d Mark IV CMOS 30.4 Full Frame 36.0 × 24.0 ca. 1.0 ca. 1000
Nikon D610 CMOS 24.3 Full Frame 36.0 × 24.0 ca. 1.250 ca. 1000
Sony Alpha 7R II CMOS 42 Full Frame Mirrorless 35.0 × 24.0 ca. 0.6 ca. 1200
Sony Alpha a6300 CMOS 24 Small Frame Mirrorless 23.5 × 15.6 ca. 0.8 ca. 800
Panasonic Lumix DMC GX8 CMOS 20 Small Frame Mirrorless 17.3 × 13 ca. 0.5 ca. 1000
Panasonic Lumix DMC GX80 DLMOS 16 Small Frame Mirrorless 17.3 × 13 ca. 0.5 ca. 500
DJI Phantom 4 Pro * CMOS 20 Small Frame 13.2 × 8.8 ca. 1.5 (with UAV) ca. 1500 (with UAV)
DJI Mavic 2 ProaCMOS 20 Small Frame 13.2 × 8.8 ca. 1.5 (with UAV) ca. 1500 (with UAV)
Table 2 Multispectral sensors andtheir main specications
Camera model Resolution [Mpx] Spectral bands Ground sample distance
[cm/px]Weight [kg] Price (approx.) [€]
Micasense RedEdge-M 1280 × 960 (1.2 Mpx per EO
band) Red, Green, Blue, Near-
Infrared, Red Edge 8 (per band) at 120 m AGL ca. 0.180 ca. 5000
Micasense RedEdge-MX 1280 × 960 (1.2 Mpx per EO
band) Blue, green, red, red edge,
near infrared (NIR) 8 (per band) at 120 m AGL ca. 0.231 ca. 5000
Micasense Altum 2064 × 1544 (3.2 Mpx per EO
band) 160 × 120 thermal
infrared
EO: Blue, green, red, red
edge, near-infrared (NIR)
LWIR: thermal infrared
8–14 µm
5.2 cm per pixel (per EO
band) at 120 m AGL—
81 cm per pixel (thermal)
at 120 m
ca. 0.405 ca. 6000
TertaCam MCAW 6 1.3 6 user selectable narrow
bands (450–1000 µm) – ca. 0.550 ca. 17000
TetraCam ADC Lite 3.2 Green, Red, Near-Infrared
(NIR) – ca. 0.2 ca. 3000
TetraCam ADC Micro 3.2 Green, Red, Near-Infrared
(NIR) – ca. 0.09 ca. 3000
Parrot Sequoia + 1.2 Blue, Green, Red, Red Edge,
Near-Infrared (NIR) – ca. 0.7 ca. 5000
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account to obtain an adequate ground resolution for the
surveyed objective and to avoid missing data [88]. In phase
2, having a higher number of radiometric bands to record,
the dataflow will be higher so is critical to avoid I/O errors,
missing data or mission failures [89]. Due to multi-lenses
nature of the sensors in phase 3, the data collected suffer
from the parallax problem. As a consequence, images have
to be rectified, georeferenced and must be stacked to gen-
erate a single image with different radiometric levels, and
calibrated with the downwelling irradiance sensors data
acquired during the flight [90]. After this procedure, it is
possible to generate a multispectral orthomosaic and then
calculate the requested indexes [91]. Multispectral images
are also used in machine learning applications [80, 85, 92]
taking into account the multi-camera nature of sensors
and the different bands recorded. anks to the availabil-
ity of a higher number of radiometric bands, the machine
learning algorithms can be extended to not-visible recog-
nition such as early stage plant disease, field quality assess-
ment, soil water content, and more [91].
Hyperspectral sensors
e hyperspectral sensors can record hundreds to thou-
sands of narrow radiometric bands, usually in visible and
infrared ranges. To deal with hyperspectral applications, the
choice of number and radiometric range of bands is criti-
cal. Each band or combination of bands, being very narrow,
can detect a specific field characteristic. Each hyperspectral
sensor can detect only a certain number of bands, so the
aim of survey must be very clear to choose the right sensor.
Although hyperspectral sensors have decreased in price in
recent years, they are still an important starting investment
since they are much more expensive than RGB and multi-
spectral sensors. In addition, they are heavier and bigger
than other sensors, often making their use on UAV systems
difficult and/or excessively onerous in terms of payload.
Some of most used hyperspectral sensors in UAVs applica-
tion and their main characteristics are shown in Table3.
In this case, the workflow for radiometric calibration is
more complex compared to other sensors. Some calibration
methods needed for these sensors are derived from manned
aircraft hyperspectral platforms, based on artificial targets
to assess data quality, to correct radiance, and to generate
a high-quality reflectance data-cube [93]. In phase 1, the
planning must also be carried out in time and not only in
space because, in addition to the spectrometric resolution,
hyperspectral sensors have a temporal resolution due to the
different acquisition method. In phase 2, it should be con-
sidered that both images’ size and data flow are bigger than
multispectral/RGB images. Moreover, these sensors may
acquire a large amount of data, but the payload limitations
of UAVs may not allow the transport of adequate file storage
systems. Phase 3 for hyperspectral images is critical: qual-
ity assessment is one of the critical issues of hyperspectral
data and some problems associated with the quality of the
images have not been completely overcome. Among those,
the stability of the sensor itself (due to the nature of UAV
platforms) and the vibrations involved can comprise a good
calibration of the sensor. Subsequently, on post-processed
data, it is possible to calculate narrowband indices such as
chlorophyll absorption ratio index (CARI), greenness index
(GI), greenness vegetation index (GVI), modified chloro-
phyll absorption ratio index (MCARI), modified normalized
difference vegetation index (MNDVI), simple ratio (SR),
transformed chlorophyll absorption ratio index (TCARI),
triangular vegetation index (TVI), modified vegetation
stress ratio (MVSR), modified soil-adjusted vegetation index
(MSAVI) and photochemical reflectance index (PRI) [94].
Applications ofUAVs toweed management
UAVs are ideal to identify weed patches. e main advan-
tages of UAVs compared to UTVs are the shorter moni-
toring/surveying time they require and optimal control
in the presence of obstacles, which is critical when work-
ing between crop rows [95]. In a few minutes, UAVs can
cover many hectares flying over the field, thus providing
the photographic material for weed patches identification
[61]. ese images are processed via deep neural network
[78], convolutional neural network, and object-based
image analysis [96, 97]. Based on a systematic review of
the literature concerning weed identification by UAVs,
it can be concluded that mainly three types of cameras
are used for weed patches identification: RGB, multispec-
tral and hyperspectral cameras (Table4). ese cameras
Table 3 Hyperspectral sensors andtheir main characteristics
Camera model Lens Spectral range [µm] Spectral bands
[number andµm] Weight [kg] Price (approx.) [€]
CUBERT Snapshot + PA N 450–995 125 (8 µm) ca. 0.5 ca. 50000
Cornirg microHSI 410 SHARK CCD/CMOS 400–1000 300 (2 µm) ca. 0.7 –
Rikola Ltd. hyperspectral camera CMOS 500–900 40 (10 µm) ca. 0.6 ca. 40000
Specim-AISA KESTREL16 Push-broom 600–1640 350 (3 – 8 µm) ca. 2.5 –
Headwall Photonics
Micro-hyperspec X-series NIR InGaAs 900–1700 62 (12.9 µm) ca. 1.1 –
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Table 4 Weed patches identication bydierent types ofcamera (multispectral, RGB, hyperspectral)
Crop Weed (common name) Weed (scientic name) Type ofcamera Main results References
Palmer amaranth Amaranthus palmeri Hyperspectral camera Discriminate glyphosate-
resistant from glyphosate-
sensitive weeds
[110]
Spotted knapweed
Babysbreath Centaurea maculosa
Gypsophila paniculata
Hyperspectral camera Detection invasive species
affecting forests, range-
lands, and pastures
[111]
Bunchgrass
Egyptian crowfoot grass
False amaranth
Awnless barnyard grass
Phalaris minor
Dactyloctenium aegyptium
Digera arvensis
Echinochloa colona
RGB camera Identify different weeds [112]
Ragwort Jacobaea vulgaris (Senecio
jacobaea)
Multispectral camera Discriminate weeds in
pastures [113]
Buffel Grass
Spinifex Cenchrus ciliaris
Triodia sp.
RGB camera Discriminate two different
weeds [114]
Beta vulgaris
Zea mays
Hordeum vulgare
Lens esculenta
Pisum sativum
Phaseolus vulgaris
Carthamus tinctorius
Cicer arietinum
Kochia
Marestail
Common lambsquarters
Bassia scoparia
Conyza canadensis
Chenopodium album
Hyperspectral camera Discriminate glyphosate
and dicamba resistant
genotypes from sensitive
genotypes
[115]
Triticum spp.
Triticosecale RGB camera Comparison of cereal geno-
types [116]
Beta vulgaris Weeds Multispectral camera Discriminate crop vs weeds [98]
Beta vulgaris Weeds Multispectral camera Discriminate crop vs weeds [85]
Beta vulgaris Thistle Cirsium arvense Multispectral camera Discriminate crop vs weeds [117]
Beta vulgaris Thistle
Wild buckwheat
Ryegrass
Cirsium arvense
Fallopia convolvulus
Lolium multiflorum
Multispectral camera Discriminate crop vs weeds [101]
Beta vulgaris Thistle Cirsium arvense Multispectral camera Discriminate crop vs weeds [112]
Cicer arietinum Weeds Hyperspectral camera Discriminate crop vs weeds [118]
Glycine max Palmer amaranth
Barnyardgrass
Large crabgrass
Amaranthus palmeri
Echinochloa crus-galli
Digitaria sanguinalis
RGB camera
Multispectral camera Assessment of crop injury
from dicamba [102]
Heliathus annuus Pigweed
Mustard
Bindweed
Lambsquarters
Amaranthus blitoides
Sinapis arvensis
Convolvulus arvensis L
Chenopodium album L
RGB camera
Multispectral camera Discriminate crop vs weeds [64]
Hordeum vulgare Thistle
Coltsfoot Cirsium arvense
Tussilago farfara
RGB camera Discriminate crop vs weeds [119]
Hordeum vulgare Thistle Cirsium arvense RGB camera Discriminate crop vs weeds [99]
Hordeum vulgare Thistle Cirsium arvense RGB camera Discriminate crop vs weeds [100]
Lactuca sativa Common groundsel
Shepherd’s purse
Sow thistle
Senecio vulgaris
Capsella bursa pastoris
Sonchus spp.
Multispectral camera Discriminate crops vs weeds [120]
Sorghum spp. Amaranth
Pigweed
Barnyard grass
Mallow
Nut grass
Fat Hen
Amaranthus macrocarpus
Portulaca oleracea
Echinochloa crus-galli
E. colona
Malva spp.
Cyperus rotundus
Chenopodium album
Hyperspectral camera Discriminate crop vs weeds [121]
Triticum durum Wild oat
Canarygrass
Ryegrass
Avena sterilis
Phalaris canariensis
Lolium rigidum
Multispectral camera Discriminate crop vs weeds [122]
Triticum durum Wild oat
Canarygrass
Ryegrass
Avena fatua
Phalaris canariensis
Lolium rigidum
Hyperspectral camera
Multispectral camera Discriminate crop vs weeds [105]
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are very similar in terms of information obtained for the
purpose of weeds identification. Indeed, the three cam-
era types can recognize weed patches with good accuracy
depending on flying altitude, camera resolution and UAV
used. UAVs have been mainly tested on important crops
such as Triticum spp., Hordeum vulgare, Beta vulgaris,
Zea mays [98–101]. ese are among the most cultivated
crops worldwide and are highly susceptible to weed com-
petition especially in early phenological stages. In these
crops, it was possible to identify several dicotyledonous
weeds including Amaranthus palmeri, Chenopodium
album and Cirsium arvense [102–104], as well as differ-
ent monocotyledonous such as Phalaris spp., Avena spp.
and Lolium spp. [105, 106]. ese weed species are wide-
spread globally and can be a serious threat to different
crops [107, 108]. erefore, the combined use of UAVs
and image processing technologies may contribute to
effectively control different weed species interfering with
the crops with relevant environmental benefits [28, 109].
Conclusion
e use of UAVs and machine learning techniques
allow for the identification of weed patches in a cul-
tivated field with accuracy and can improve weed
management sustainability [97]. Weed patches iden-
tification by UAVs can facilitate integrated weed man-
agement (IWM), reducing both the selection pressure
vs herbicide-resistant weeds and herbicides diffusion
in the environment [64]. Recent research has shown
that new technologies are able to discern single weed
species in open fields [63, 106, 126]. If integrated with
weed management planning, this information gathered
via remote imaging analysis can contribute to sustain-
ably improve weed management. In addition, imag-
ing analysis can help in the study of weed dynamics
in the field, as well as their interaction with the crop,
which both represent a necessary step to define new
strategies for weed management based on interspe-
cific crop–weed interactions [127–129]. Recent studies
demonstrate that some weed communities are actually
not detrimental to crop yield and quality [127, 128].
In winter wheat cultivation, a highly diversified weed
community caused lower yield losses than a less diver-
sified one [129]. In soybean, through a combination of
field experiments in which weed species were manipu-
lated in composition and abundance, it has been shown
that increasing levels of weed competition resulted in
an increase in seed protein content without impairing
yield [130].
Most likely, the integration of known and emerging
technologies in this field will greatly improve the sustain-
ability of weed control, following the SSWM approach.
By image analysis, different machine learning techniques
will be able to provide a reliable overview of the level and
type of infestation. Specific algorithms can be trained to
manage weeds removal by Autonomous Weeding Robot
(AWR), via herbicide spray or mechanical means [131].
Also, the creation of a specific weed images dataset is
crucial to achieve this goal. is approach must neces-
sarily rely on a dataset of photographs taken in dedicated
experimental fields, labeled in extended COCO/POCO
(Common Objects in COntext/ Plant Objects in COn-
text) format [86] and integrated with images from Plant-
Village dataset [83] or other existing ones.
New insights on weed population dynamics and their
competition with crops are needed in order to extend
this approach to real agricultural contexts, so as to specif-
ically recognize and eliminate only harmful weed species.
e overall objective is to overcome the consequences
of biological vacuum around the crop, which has been
proved to be highly impacting for both biotic and the
abiotic components of the environment [132, 133], with
long-term consequences on human safety on earth.
These types of cameras can be assembled with drones for reaching SSWM purposes
Table 4 (continued)
Crop Weed (common name) Weed (scientic name) Type ofcamera Main results References
Triticum sp. Thistle Cirsium arvense RGB camera Discriminate crop vs weeds [99]
Triticum spp. Weeds Hyperspectral camera Discriminate crop vs weeds [118]
Vitis vinifera Bermuda grass Cynodon dactylon RGB camera Discriminate crop vs weeds [123]
Zea mays Weeds Multispectral camera Discriminate crop vs weeds [124]
Zea mays Common lambsquarters
Thistle Chenopodium album
Cirsium arvense
Multispectral camera Discriminate monocotyle-
dons (crops) vs dicotyle-
dons (weeds)
[104]
Zea mays Common lambsquarters
Thistle Chenopodium album
Cirsium arvense
Multispectral camera Discriminate crop vs weeds [101]
Zea mays Mat amaranth
Johnsongrass Amaranthus blitoides
Sorghum halepense
Multispectral camera Discriminate crop vs weeds [125]
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 8 of 11
Espositoetal. Chem. Biol. Technol. Agric. (2021) 8:18
Abbreviations
IWM: Integrated Weed Management; UAV: Unmanned Aerial Vehicles; UTV:
Unmanned Terrestrial Vehicles; SSWM: Specific Weed Management; RGB: Red
Green Blue; VIS: Visible; GRVI: Green/Red Vegetation Index; GI: Greenness Index;
ExG: Excessive Greenness; GPS: Global Positioning System; CARI: Chlorophyll
Absorption Ratio Index; MCARI: Modified Chlorophyll Absorption Ratio
Index; MNDVI: Modified Normalized Difference Vegetation Index; SR: Simple
Ratio; TCARI: Transformed Chlorophyll Absorption Ratio Index; TVI: Triangular
Vegetation Index; MVSR: Modified Vegetation Stress Ratio; MSAVI: Modified
Soil-Adjusted Vegetation Index; PRI: Photochemical Reflectance Index; AWR
: Autonomous Weeding Robot; COCO/POCO: Common Objects in COntext/
Plant Objects in COntext.
Acknowledgements
Not applicable.
Authors’ contributions
ME and MC wrote the original draft and produced all the tables and the
figures. AM, FS and VC revised the text for the final version. VC and ME con-
ceived the idea of writing the review. All authors read and approved the final
manuscript.
Funding
This research was funded by MEDES Foundation.
Availability of data and materials
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Received: 7 January 2021 Accepted: 15 February 2021
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