ArticlePDF Available

Abstract and Figures

Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.
Content may be subject to copyright.
A Review on UAV-Based Applications for
Precision Agriculture
Dimosthenis C. Tsouros *, Stamatia Bibi and Panagiotis G. Sarigiannidis
Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece; (S.B.); (P.G.S.)
*Correspondence: or
This paper is an extended version of our paper published in IoT4 2019 Workshop, co-located with IEEE
DCOSS 2019.
Received: 7 October 2019; Accepted: 7 November 2019; Published: 11 November 2019
Emerging technologies such as Internet of Things (IoT) can provide significant potential
in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time
environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited
in a variety of applications related to crops management, by capturing high spatial and
temporal resolution images. These technologies are expected to revolutionize agriculture, enabling
decision-making in days instead of weeks, promising significant reduction in cost and increase in the
yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of
precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the
right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not
been as robust as expected mainly due to the challenges confronted when selecting and deploying
the relevant technologies, including the data acquisition and image processing methods. The main
problem is that still there is no standardized workflow for the use of UAVs in such applications, as it
is a relatively new area. In this article, we review the most recent applications of UAVs for Precision
Agriculture. We discuss the most common applications, the types of UAVs exploited and then we
focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of
each one. We also point out the most popular processing methods of aerial imagery and discuss the
outcomes of each method and the potential applications of each one in the farming operations.
remote sensing; IoT; UAV; UAS; Unmanned Aerial Vehicle; Unmanned Aerial System;
image processing; Precision Agriculture; Smart Farming; review
1. Introduction
In the last five years, the total volume of investments in the agricultural sector has increased by
80%. The goal of these investments is to achieve productivity growth of at least 70% by 2050 [
] to meet
the increased needs of the population of the Earth considering the fact that the area under cultivation
will decrease. Emerging technologies such as Internet of Things (IoT) can provide significant potential
in Precision Agriculture and Smart Farming, enabling the long-term increase in productivity [
The IoT (Internet of Things) paradigm offers a new perspective for precision agriculture enabling the
real- time and site specific management of the cultivated fields. In IoT-based Smart farming, a system
is built for monitoring the crops targeting in the automation of various important farming operations
such as monitoring of the growth, irrigation process, application of fertilizers, disease detection, etc.
In this context, technologies such as IoT can assist in the acquisition of real-time information from the
agricultural fields. This information can be timely processed and exploited to support critical decisions
regarding the management of the crops.
Information 2019,10, 349; doi:10.3390/info10110349
Information 2019,10, 349 2 of 26
Remote sensing is generally considered one of the most important technologies for Precision
Agriculture and Smart Farming. It is commonly used for monitoring cultivated fields, providing
effective solutions for Precision Agriculture in the last 35 years [
]. Remote sensing can monitor many
crops and vegetation parameters through images at various wavelengths. In the past, remote sensing
was often based on satellite images [
] or images acquired by using manned aircraft in order to
monitor vegetation status at specific growth stages. However, satellite imagery is often not the best
option because of the low spatial resolution of images acquired and the restrictions of the temporal
resolutions as satellites are not always available to capture the necessary images. In addition, it is often
required to wait long periods between acquisition and reception of images. In addition, environmental
conditions, such as clouds, often hinder their reliable use. Considering the use of manned aircrafts,
usually it results in high costs, and many times it is not possible to carry out multiple flights to obtain
more than a few crop images.
The development of UAV-based remote sensing systems have taken remote sensing and Precision
Agriculture (PA) one step further. The use of UAVs to monitor crops offers great possibilities to acquire
field data in an easy, fast and cost-effective way compared to previous methods. UAV-based IoT
technology is considered as the future of remote sensing in Precision Agriculture. UAVs’ ability to fly
at a low altitude results in ultra-high spatial resolution images of the crops (i.e., a few centimeters).
This significantly improves the performance of the monitoring systems. Furthermore, UAV-based
monitoring systems have high temporal resolution as they can be used at the user’s will. This enhances
the flexibility of the image acquisition process. In addition, UAVs are a lot simpler to use and also
cheaper than manned aircrafts. Moreover, they are more efficient than the ground systems as they can
cover a large field in a short amount of time and in a non-destructive way, which is very important.
UAVs are not a recent technology since the first attempt to construct a powered UAV was recorded
in 1916 [
]. UAVs were initially exploited for military purposes; however, in recent years, their use has
rapidly expanded to other types of applications (commercial, scientific, agricultural, etc.). The wider
use of UAVs was led by the technology advancements and the miniaturization of the associated
hardware during the 1980s and 1990s.
Unmanned Aerial Systems (UAS) are now very commonly used in remote sensing applications
for Precision Agriculture. Equipped with sensors of different types, UAVs can be exploited to identify
which zones of the crops need different management, e.g. some kind of input. This gives the farmers the
ability to react on time in any problem detected. UAS can be used in a plethora of different applications
on Precision Agriculture, such as health monitoring and disease detection, growth monitoring and
yield estimation, weed management and detection, etc. As the use of UAVs in PA applications is very
frequent in the last years and it is considered the future of remote sensing, it is a field that draws a
lot of attention. Thus, several reviews exist for their application in Precision Agriculture and Smart
Farming. Most of the reviews focus mainly on the different types of applications that UAVs can have in
agricultural crops [
] or environmental monitoring in general [
]. In [
], the authors reviewed the
hyperspectral imagery and the techniques used in these cases. Maes et al. [
] focused on the suitability
of the different available sensors for each application, providing with important perspectives for the
use of UAVs in PA. However, this work does not review the techniques used for exploiting the acquired
information. In addition, a survey that discusses the use of Deep Learning in agricultural data has
been conducted [16].
To the best of the authors knowledge, a review focusing also on the most frequently used
techniques exploiting and processing UAV imagery from agricultural fields is currently missing from
the literature, despite its necessity. We believe that it is very important mainly because the absence of a
standardized workflow is one of the major drawbacks that affects the wider use of UAV systems in
commercial PA applications. This fact results in the adoption of a variety of heterogeneous procedures
and methods by different researchers, for the same goal. This results to not always having the best
outcome. Furthermore, we believe that a study reviewing the most recent works is of paramount
importance, as it is a research area that is advancing really quickly. In this work we extend [
Information 2019,10, 349 3 of 26
reviewing the most recent studies about UAV-based applications for PA, focusing on the most common
techniques applied on UAV imagery in recent works to monitor crop fields in Precision Agriculture.
The goal is to identify the most used sensors and practices for each type of application.
To properly perform a review on UAV applications in Precision Agriculture, we formulated the
following research questions (RQs):
1. Which are the different types of UAV applications in Precision Agriculture?
In this research
question, we aim to explore the current trends in the application of UAVs in precision agriculture.
The initial goal of UAVs in their early application in agriculture was to derive direct image-based
products. Nowadays, this has changed, and the applications of UAVs in agriculture are
intelligence-based oriented products that process images and provide informed decision-making
applications to the farmers. In this question, we provide a thorough description of the different
types of applications that UAVs can support based on the different operational needs of
agriculture fields.
2. What types of crops are monitored by UAV systems?
In this research question, our target is
to record the different types of crops that have been monitored so far with the help of UAVs.
Additionally, we provide general information regarding the geographical distribution of these
crops, their size and the different stages of growth where monitoring can take place. By answering
this research question, we can identify how the different characteristics of each crop and its life
cycle affect the use of UAVs.
3. Which UAV system technologies are adopted in Precision Agriculture?
In this research
question, we identify the system characteristics of UAV-based applications for Precision
Agriculture. By answering this question we can locate the specific UAV types and sensors
that can be used for monitoring crops.
4. What types of data can be acquired by UAVs?
In this research question, we record the different
types of data that can be acquired by UAVs based on the sensor technology employed. We also
provide a review of the advantages and disadvantages of the different types of data that can be
gathered with the help of different sensors based on the associated cost and the types of the field
operations applied.
5. Which data processing methods can be used to exploit the agricultural data acquired by UAVs?
In this research question, we identify the methods that are used for image analysis in agricultural
crops. We distinguish between three types of data processing methods that can be used alone or
complementary so as to gain insights regarding a field namely: (a) Photogrammetric techniques;
(b) Machine Learning techniques; and (c) Vegetation Indices.
To answer these research questions, we reviewed 100 recent papers [
] published during the
2017–2019 period.
The rest of the article is organized as follows. Section 2describes the basic UAV-based applications
for Precision Agriculture, including the types of crops being monitored, the application domains and
the UAV technologies being used for Precision Agriculture purposes. In Section 3, we focus on the
basics of UAV-based data acquisition and the types of sensors used. Section 4discusses the most
used image processing methods that stood out in the literature: the photogrammetry techniques, the
vegetation indices calculation and machine learning. Next, Section 5focuses on the limitations in the
use of UAVs for Precision Agriculture. Finally, Section 6discusses the results of the review and make
some concluding remarks.
2. UAV-Based Monitoring of Crops
In this section, we introduce the applications of UAVs for Precision Agriculture, along with the
types of crops being monitored and the UAV technologies adopted.
Information 2019,10, 349 4 of 26
2.1. Types of UAV Applications in Precision Agriculture
To date, UAV technologies have been successfully employed in a variety of applications for
Precision Agriculture such as site-specific herbicide applications, water deficiency identification,
detection of diseases, etc. Using the information acquired by the UAVs several decisions can be made
to handle the problem(s) detected and/or optimize harvesting by estimating the yield.
The most common applications of UAVs for Precision Agriculture, as recorded in the literature,
are the following:
Weed mapping and management [70,98]
Vegetation growth monitoring and yield estimation [48,65,87]
Vegetation health monitoring and diseases detection [46,77]
Irrigation management [64,118]
Corps spraying [89,95]
Among the most popular application of UAVs in Precision Agriculture is
Weed mapping
Weeds are not desirable plants, which grow in agricultural crops and can cause several problems.
They are competing for available resources such as water or even space, causing losses to crop yields
and in their growth. In addition to the problems in the growth of the crops, weeds can cause problems
at harvesting. The use of herbicides is the dominant choice for weed control. In conventional farming,
the most common practice of weed management is to spray the same amounts of herbicides over the
entire field, even within the weed-free areas. However, the overuse of herbicides can result in the
evolution of herbicide-resistant weeds and it can affect the growth and yield of the crops. In addition,
it poses a heavy pollution threat to the environment. In addition, the above practice significantly
increases the cost. To overcome the above problems, in Precision Agriculture practices, Site-Specific
Weed Management (SSWM) is used. SSWM refers to the spatially variable application of herbicides
rather than spraying them in the whole field. In this context, the field is divided into management
zones that each one receives a customized management, as usually weed plants spread through only
few spots of the field. To achieve this goal, it is necessary to generate an accurate weed cover map for
precise spraying of herbicide. UAVs can gather images and derive data from the whole field that can
be used to generate a precise weed cover map depicting the spots where the chemicals are needed:
(a) the most; (b) the least; or (c) they should not be applied at all.
UAVs are also frequently used for
Monitoring the growth of the vegetation and providing
estimation regarding the yield
. The lack of means for systematically monitoring the progress of
cultivation is considered as one of the major obstacles to increasing the agricultural productivity
and quality. This problem is also compounded by the variability of weather conditions that alter the
micro-climate of crops jeopardizing the agricultural production. Regular collection of information
and visualization of crops using UAVs, provides increased opportunities to monitor crop growth
and record the variability observed in several parameters of the field. Many recent works focus on
monitoring the biomass and nitrogen status of the crops along with yield estimation. Biomass is the
most common crop parameter, which together with information related to nitrogen content can be
used to determine the need for additional fertilizer or other actions. In addition, the information
acquired by the UAVs can be used for the creation of three-dimensional digital maps of the crop, and
for the measurement of various parameters, such as crop height, distance between rows or between
plants, and the index Leaf Area Index (LAI). UAVs offer the potential to systematically collect crop
information, therefore farmers can plan in a controlled manner the crop management, use of inputs
(e.g., use of nutrients), timing of harvesting and soil and yield pathogens, or even identify possible
management errors.
UAVs are also used to
monitor vegetation health
. Crop health is a very important factor that
needs to be monitored, as diseases in crops can cause significant economic loss due to the reduced yield
and the reduction of quality. Crops should be monitored constantly to detect the diseases in time and
avoid spreading problems. Traditionally, this task is performed by human experts directly in the field.
Information 2019,10, 349 5 of 26
However, this can be very time consuming, as it can require months to inspect an entire crop preventing
the potentials of “continuous” monitoring. Another common disease control method is the application
of pesticides in certain dates. Such a strategy incurs a high cost and also increases the likelihood of
ground water contamination as pesticide residues in the products. In Precision Agriculture, site-specific
disease control takes place. PA practices adopt a decision-based disease management strategy, in which
automated non-destructive crops disease detection plays a very important role. Disease detection
is feasible as diseases induce changes in biophysical and biochemical characteristics of the crops.
UAV-based data processing technologies use crop imaging information to identify changes in plant
biomass and their health. Therefore, diseases can be detected in their early stages enabling farmers
to intervene in order to reduce losses. In this context, UAVs can be used in the two different stages
of disease control: (a) at the initial stage of infection by collecting crop health relevant information,
during which UAVs can detect a possible infection before visual indications appear and map the size of
the infection to different parts of a culture; and (b) during the treatment of infection when farmers can
use UAVs for targeted spraying as well as for accurately monitoring the course of their intervention.
irrigation management
is a very important area of application of UAV technologies in
Precision Agriculture. Currently, 70% of the water consumed worldwide is used for the irrigation of
crops [
], a fact that highlights the need for precision irrigation techniques. Precision irrigation
techniques can improve the efficiency of water use, so that the resource is applied effectively: (a) in
the right places; (b) at the right time; and (c) in the right quantity. The detection of the areas where
major irrigation is needed can help the farmers to save time and water resources. At the same time,
such precision farming techniques can lead to increased crop productivity and quality. In the context
of precision agriculture, the field is divided in different irrigation zones, to precisely manage the
resources. The use of Unmanned Aerial Vehicles incorporating suitable sensor types makes it possible
to identify parts of a crop that need more water. At the same time, the above technologies allow for the
production of specialized maps that illustrate the morphology of the soil, thus supporting the more
efficient irrigation planning of each crop separately.
An application of UAVs in precision agriculture that is more rarely met is
Crop spraying
. The main
spraying equipment used in conventional farming are the manual air-pressure and battery-powered
knapsack sprayers. However, these conventional sprayers can cause major pesticide losses. In addition,
the operators need to be present when spraying, which leads to exposure of the operators. In addition,
it may be time-consuming to spray the entire field, which is not only limiting the resources but also can
lead to not-timely spraying. In this manner, UAVs can be useful due to the lower operator exposure
and improved ability to apply chemicals in a timely and highly spatially resolved manner. The use
of precision systems for measuring distances allows UAVs to follow the morphology of the ground,
keeping their height constant. Therefore, an aircraft has the ability to spray the appropriate amount
of herbicide spatially, adjusting both its height and the amount it sprays depending on the crop site
in which it is located. Crop spraying is particularly important in cases where diseases have been
identified where it is important to reduce pesticide use without affecting crop yield. In conclusion,
UAV-based systems can make a decisive contribution to crop spray management.
In addition to the common applications mentioned above, UAVs have also been used for soil
analysis [
], cotton genotype selection [
], mammal detection [
], and assessment of soil
electrical conductivity [66].
2.2. Types and Properties of Crops Monitored by UAV-Based Systems
In the recent years, UAV technologies have been employed to monitor a variety of different types
of crops, located in several countries all over the world. UAV applications in Precision Agriculture
have been carried out in 29 different countries. The majority of applications are located in economically
developed countries, with the US and China standing out. Lately, however, the applications in Europe
have increased as well.
Information 2019,10, 349 6 of 26
Regarding the different types of crop species that can be monitored with the help of UAVs, more
than 30 different species were identified. Among the most common crop species monitored by UAV
technologies are Maize, Wheat, Cotton, Vineyards, Rice and Soya. In addition, we observe that these
technologies have been used to monitor crops with completely different characteristics, such as olive
trees and rice crops.
Another observation is that the monitoring of crops can take place during the different stages
of growth, even at the early ones before being able to draw inferences from soil characteristics.
The purpose of constant and continuous monitoring of crops at different stages of development
is to record various factors that may affect the final performance of crops, as well as to evaluate
the effectiveness of actions taken to address problems identified at an earlier stage of development.
Regarding the size of the crops monitored we observe in this review that it is possible:
To monitor large fields (>10 ha), where data are collected from all areas.
To monitor small farms or small parts of a field.
To monitor areas of great heterogeneity. This is achieved by using a UAV equipped with automatic
pilot systems and ground-level sensors.
2.3. UAV System Technologies
An Unmanned Aerial System that applies to Precision Agriculture usually includes the following
key elements [121]:
One or more UAVs: Flying vehicles that have no operator on their spindle but operate either
autonomously or remotely.
A Ground Control Station (GCS): It is a computer that either communicates with the UAV Control
System or controls and monitors the UAV directly. The GCS monitors information related to the
flight of the UAV. The user has the ability to receive data relevant to the flight of the aircraft, but
also data recorded by the sensors that support the flight (i.e., ground-based sensors or sensors
embedded in the aircraft). In addition, the GCS contains the software required for the processing
of data acquired by the UAV and the extraction of the information needed by the system operator
for the crop monitoring.
UAV Control System (UAV CS): It is used to control the UAV. It can be either a two-way data
link, such as a remote control, or a built-in computer (usually with a built in GPS). The UAV CS
includes the flight control system and/or the autopilot system, which controls the operation of
the UAV. This system receives and processes data from the autopilot or flight control system for
the proper operation of the UAV. It usually contains sensors to monitor the flight properties, such
as sensors for measuring distance from ground, air force, etc. The control system has the ability to
process information from sensors to correct any problems that may arise, and to communicate
with the GCC wireless and in real time by sending and receiving the necessary information.
Sensors for data acquisition: They are cameras intended to collect the information needed. The
next section provides a detailed presentation of possible ways of collecting information and the
technologies exploited. In the case that UAVs are not intended to collect information but are used
for another purpose, such as spraying, the sensors are replaced with the necessary components.
Focusing on the UAV technologies being used for PA, the types of Unmanned Aerial Vehicles can
be divided into five basic categories, based on their design characteristics (see Figure 1).
Information 2019,10, 349 7 of 26
(a) (b) (c)
(d) (e) (f)
Figure 1.
) Fixed-wing (eBee
) [
]; (
) helicopter (Hornet Maxi) [
]; (
) octocopter [
(d) blimp [123]; (e) flapping-wing (SmartBird) [122]; and (f) parafoil-wing (Tetracam) [90].
Fixed-wing: These are unmanned planes with wings that require a runway to take off from
the ground or a catapult. This type of UAVs has high endurance as well as the ability to
fly at high speeds. In addition, fixed-wing UAVs have the ability to cover large areas on
each flight and can carry more payload. However, they are more expensive than the other
types. In the works reviewed, 22% used fixed-wing UAVs. One type of fixed-wing UAVs that
has not been identified in the reviewed literature, but is a very promising technology, is the
solar-powered UAVs [
]. Solar-powered UAVs offer significantly increased flight times because
they exploit and store the sun’s energy during the day. This is the reason that they are preferred
for long-endurance operations.
Rotary-wing: The rotary-wing UAVS, also called rotorcrafts or Vertical Take-Off and Landing
(VTOL), offer the advantages of steady flying at one place while keeping the maneuverability
attribute. These features are useful for many different types of missions. However, they cannot
fly at very high speed or stay in the air for a long time. They are generally the most widely used
UAVs in all kinds of applications, but especially in Precision Agriculture. One reason for this
is the fact that they present lower cost compared to the other types of UAVs. In addition, this
type of UAVs is suitable when the monitored crops are not very large, which is usually the case.
A UAV of this type may be:
An unmanned helicopter: They include main and tail rotors such as conventional helicopters.
Overall, 4% of the works used this type.
Multi-rotor: This category includes rotary-wing UAVs with four or more rotors (quadcopter,
hexacopter, octocopter, etc.). These aircraft are generally more stable in flight than unmanned
helicopters. Overall, 72% of the works used this type.
Blimps: This type of UAV is lighter than air, has high endurance, flies at low speeds and is
generally larger in size compared to the other types. Their manufacturing characteristics allows
them to remain in the air even in the event of a total loss of power, while being considered
relatively safe in the event of a collision. Usually, they are not used in Precision Agriculture
applications. In the recent works reviewed, no application was found using this type of UAVs.
Information 2019,10, 349 8 of 26
Flapping wing: These UAVs are very small and they have flexible, shaped little wings inspired by
the way birds and insects fly. They are not often used in Precision Agriculture as they require
high energy consumption due to their size. No work was found in the literature review using
this type of UAVs.
Parafoil-wing. Usually aircrafts of this type have one or more propellers at the back in order to
control the course of their flight, but at the same time for harnessing the power of the air to fly
without consuming much energy. They are also capable of carrying a larger payload. They are
not usually exploited for PA applications. Only 2% of the works analyzed use this type.
In addition to the above classification, UAVs can also be categorized according to their size [
However, the categorization used in this study is more common, as it takes into account more factors
affecting the performance of UAVs and their use in Precision Agriculture.
The majority of the recent works in Precision Agriculture use multi-rotor UAVs. This is mainly
due to the fact that in most applications the area under consideration is not very large. For this reason,
it is not necessary to use UAVs with high speed and the ability to cover large areas in a few flights, such
as fixed-wing UAVs. Thus, rotary-wing aircraft are preferred because of the following advantages:
Easy to operate
Slower speeds
Ability to maneuver
Relatively low cost
These advantages provide greater opportunities for collecting information from crops through
imaging, which is the main use of UAVs in vegetation monitoring. In cases where the monitoring area
is relatively large, fixed-wing aircraft are preferred, which enable the monitoring of the entire area in a
short time.
3. UAV Data Acquisition
Equipped with specialized sensors, UAVs are becoming powerful sensing systems that
complement the IoT-based techniques. The role of the sensors is to capture images of high-spatial and
temporal resolution, which can assist in monitoring many different characteristics of the vegetation.
A variety of different types of sensors can be used in an agricultural UAV depending on the different
crop parameters that should be monitored [
]. However, the needs for low payload capacity and the
utilization of small platforms pose several limitations on the selection s of the sensor(s) to be used.
The main criteria that the sensors have to meet are the low weight, the low energy consumption
and the small size. Of course, all of the above must be combined with the ability to capture high
resolution images.
Modern commercial on-board sensors complying with the above restrictions that are used for PA ,
mainly belong to the following four types:
Visible light sensors (RGB)
Multispectral sensors
Hyperspectral sensors
Thermal sensors
In addition to the above types of sensors, other types of sensors can be used, such as laser
scanners, also mentioned in the literature as light detection and ranging (LiDAR). Laser scanners
are a well-established technology used extensively for environmental sciences, however they are
mostly used for terrestrial scanning. Airborne laser scanning has been exploited since 1994 [
], when
commercial systems became available. However, they were not widely used in the studies reviewed,
mainly due to the increased cost compared to other types of sensors used for data acquisition [84].
Each sensor type can monitor different characteristics of the vegetation, such as the color and
texture of vegetation or the geometric outline of the crops. In addition, some sensors can measure
Information 2019,10, 349 9 of 26
the radiation in certain wavelengths. The data acquired by these sensors can be further processed to
monitor plant biomass, vegetation health, soil moisture and other important crop characteristics at the
different growth stages. Figure 2presents examples of the main types of sensors used.
(a) (b)
(c) (d)
Figure 2.
Examples of sensors used by UAVs for PA: (
) thermal sensor [
]; (
) RGB sensor [
(c) multispectral sensor [26]; and (d) hyperspectral sensor [88].
Visible light sensors (RGB)
: RGB sensors are the most frequently used sensors by UAV systems
for Precision Agriculture applications. They are relatively low cost compared to the other types
and can acquire high resolution images. In addition, they are easy to use and operate and they
are lightweight. In addition, the information acquired requires simple processing. The images
can be acquired in different conditions, on both sunny and cloudy days, but a specific time frame
is required based on weather conditions to avoid inadequate or excessive exposure of the image.
Considering the drawbacks of these sensors, the main disadvantage is the fact that they are
inadequate for analyzing a lot of vegetation parameters that require spectral information in the
non-visible spectrum. They are commonly used in tandem with the other types of sensors.
By using
multispectral or hyperspectral imaging sensors
, UAVs can acquire information about
the vegetation’s spectral absorption and reflection on several bands. Spectral information can
be significantly helpful in assessing a lot of biological and physical characteristics of the crops.
For example, unhealthy parts of the crops can be discriminated in an image, as visible radiation
in the red channel is absorbed by chlorophyll, while near infrared (NIR) radiation is strongly
reflected. Thus, even if it is not yet visible in the red channel, it can be identified by the information
in the NIR channel. Spectral information can be used to calculate several vegetation indices and
monitor several crop characteristics based on them.
Multispectral and hyperspectral sensors are frequently used, despite their higher costs. However, a
drawback of these sensors arises from the fact that it is required to apply more complex
pre-processing methods in order to extract useful information from the captured images.
The pre-processing procedure of spectral images often contains the radiometric calibration,
Information 2019,10, 349 10 of 26
geometric correction, image fusion and image enhancement. The main difference between
multispectral and hyperspectral sensors is the number of bands (or channels) that each sensor
can capture and the width of the bands. Multispectral sensors capture 5–12 channels while
hyperspectral images can usually capture hundreds or thousands bands, but in a narrower
bandwidth. Although in the recent works studied multispectral sensors are used a lot more
frequently than hyperspectral because of their lower cost, hyperspectral technology seems to have
a lot of potential and is considered as the future trend for crop phenotyping research [9].
Thermal infrared sensors
capture information about the temperature of the objects and generate
images displaying them based on this information and not their visible properties. Thermal
cameras use infrared sensors and an optical lens to receive infrared energy. All objects warmer
than absolute zero (
F) emit infrared radiation at specific wavelengths (LWIR and
MWIR bands) in an amount proportional to their temperature. Hence, thermal cameras focus
and detect the radiation in these wavelengths and usually translate it into a grayscale image for
the heat representation. Many thermal imaging sensors can also generate colored images. These
images often show warmer objects as yellow and cooler objects as blue. This type of sensors
is used for very specific applications (e.g., irrigation management). As a result, they are not
frequently used in PA applications of UAV systems that focus on monitoring other characteristics
of the crops.
In the image acquisition by UAVs, it is typical to acquire several overlapping images of the
crops. In the most cases they capture both front and side overlapping images. This is desired as the
overlapping images can be used for the construction of 3D models and/or orthophotos of the crops, as
the next section discusses. The rate of the overlap depends on the type of the application. The front
overlap usually ranges 60–95% while the side overlap ranges 40–95% to generate three-dimensional
models and 25–40% for other uses.
In addition, the altitude of the UAV flights varies according to the application and spatial accuracy
of the information we want to collect. The distance between the target (crop) to be visualized and the
UAV plays an important role in determining the detail of the information acquired. This is something
that depends on both the sensors and the resolution they offer. In the majority of cases, depending on
the purpose of the application, the spatial resolution of the photographs is between 0.5 cm/pixel and
10 cm/pixel.
Quite often, RGB sensors are modified to acquire information about the radiation in other bands
too, usually the Near Infrared (NIR) or the Red Edge (RE) band. This approach is observed when
the stakeholders want to avoid the higher costs of buying multispectral cameras. This is achieved by
replacing one of the original optical filters with one that enables the perception of near-infrared channel,
resulting often in a hybrid (e.g., NIR-RGB) sensor. The visible channel that is no longer captured by
the modified RGB sensor is often captured by using another embedded RGB sensor. The use of both
multispectral and visible sensors was observed in many cases [
4. UAS Data Processing
This section focuses on the data processing techniques utilized to analyze the UAV imagery. To be
more precise, we discuss the different ways the information UAVs capture can be exploited to study
different vegetation features. The most common features that can be monitored with UAV-based
remote sensing for Precision Agriculture are presented in Table 1:
Information 2019,10, 349 11 of 26
Table 1. Crop features that can be monitored with UAVs.
Crop Features
biomass [22,103]
nitrogen status [22,99,103,110]
moisture content [109,110]
vegetation color [49,54]
spectral behavior of chlorophyll [64,99]
temperature [64,69]
spatial position of an object [32,106]
size and shape of different elements and plants
vegetation indices [5456]
moisture content [109,112]
temperature [66,69]
electrical conductivity [66]
With the use of specialized sensors, UAVs can acquire information for various features of the
cultivated field. However, as mentioned above, there is still no standardized workflow or well
established techniques to follow for analyzing and visualizing the information acquired. The most
commonly used image processing methods y to analyze UAV imagery for Precision Agriculture
purposes are the following:
Photogrammetry techniques:
Photogrammetry regards the accurate reconstruction of a scene or
an object from several overlapping pictures. Photogrammetric techniques can process the 2D data
and establish the geometric relationships between the different images and the object(s), obtaining
3D models. To construct the 3D models, photogrammetry requires at least two overlapping
images of the same scene and/or object(s), captured from different points of view. These kind of
techniques can be used for extracting three-dimensional digital surface or terrain models [
and/or orthophotos [
]. UAV low-altitude data acquisition enables the construction of 3D
models with a much higher spatial resolution compared to other remote sensing technologies
(such as satellites). However, the collection of many images is required to have information for
the entire field under study. Thus, in most cases, it is necessary to collect many overlapping
images to construct Digital Elevation Models (DEMs) of the crops and/or create orthophotos
(also referred to as orthomosaics). The 3D models and the orthophotos include information about
the 3D characteristics of the crops based on the structure of the vegetation (e.g., the vegetation
height, the canopy, the density, etc.) and can be very useful for applications that can exploit only
RGB imagery. The works reviewed showed that photogrammetric techniques are very commonly
used in all types of applications as they are also required to create vegetation indices maps.
In addition, the 3D information they include is very important and is often used in tandem with
other techniques.
Machine learning methods:
Machine Learning (ML) has been used to process the data acquired,
for prediction and/or identification purposes, with great results in many domains, such as medical
systems [
], marketing [
], biology [
], etc. Machine learning techniques are often
been applied in Precision Agriculture to exploit the information from the large amount of data
acquired by the UAVs. ML is able to estimate some parameters regarding the crop growth rate,
detect diseases or even to identify/discriminate objects in the images. Machine learning usage
has increased a lot recently due to the fast advancements taking place especially in the deep
learning field.
Vegetation Indices calculation:
Vegetation Indices (VIs) are one of the most popular products
of remote sensing applications for Precision Agriculture. They use different mathematical
combinations/transformations of at least two spectral bands of the electromagnetic spectrum,
designed to maximize the contribution of the vegetation characteristics while minimizing the
external confounding factors. They can deliver reliable spatial and temporal information about the
Information 2019,10, 349 12 of 26
agricultural crops monitored. In most cases, many VIs are extracted and used to draw conclusions.
They can be calculated based on information of either each photograph individually or after the
production of orthophotos depicting the whole crop. Calculating vegetation indices may serve
in the identification of useful crop characteristics, such as biological and physical parameters of
the vegetation.
Since the processing of data may be time consuming, several software tools and techniques have
been developed to enable faster data processing. The most commonly adopted software solutions in
the works reviewed to support and accelerate the data analysis procedure are summarized in Table 2.
In addition to the software tools referenced in the table, there are several other promising software
tools that can assist in the data analysis process, such as Erdas Imagine [
], eCognition [
], and
PixelWrench 2 [132].
Table 2. Most common software tools used in the literature for image processing.
Software Tool Description
Adobe Photoshop [21,100] Applied to correct distortion/use of other image processing methods
Agisoft Photoscan [22,36,37]
Exploited for the construction of 3D models and orthomosaics. It also
allows the calculation of vegetation indices
QGIS [23,55]
Usually exploited for the calculation of the vegetation indices from
multispectral data
MATLAB [35,100]
Applied mainly for the calculation of vegetation indices. It can also be
exploited for other image processing methods
Pix4D [29,35,55]
The most commonly used tool. It can be used for calculating VIs and/or
constructing of 3D models and orthomosaics
In the following subsections, we provide the details of the three most commonly used data
processing techniques to analyze data acquired from UAV flights in the agricultural domain.
4.1. Photogrammetric Techniques
Photogrammetric techniques are mainly be applied for the construction of orthomosaics and/or
Digital Elevation Models (DEMs) in order to exploit the 3D information regarding the vegetation.
As mentioned above, this can be done by acquiring many overlapping images of the agricultural crops
monitored. The advancements in computer vision and photogrammetric techniques have lead to many
different techniques and algorithms that are able to match large numbers of overlapping images and
detect common objects and scenes in them. The images are processed by applying aerial triangulation
and adjusting camera orientation. Computer vision methods are used for matching the overlapping
images and the common characteristics. To achieve object tracking as well as to identify the scale and
the orientation of a particular image; in some cases, Ground Control Points (GCPs) are being used.
GCPs distributed in the cultivated field can be identified within the overlapping images: (a) to link the
images; and (b) to identify the coordinates of each image and its slope. However, recent advancements
have made this redundant, as many techniques can be used without GCPs, with similar precision.
The construction of the 3D Digital Elevation Models can provide to the producer information
about the altitude of the earth surface, the natural and artificial objects/structures on the surface, the
density of the vegetation, and their growth, among others. There are two types of DEMs used:
The Digital Terrain Model (DTM) represents the altitude of the surface of the Earth, i.e., of the
terrain. These models do not take into account either artificial or natural (e.g., trees, vegetation,
buildings) objects that exist in the field. DTMs just present the elevation of the bare Earth. Figure 3
shows a Digital Terrain Model from Ronchetti et al. [79].
The Digital Surface Model (DSM) represents the altitude of the surface that is first encountered by
the remote sensing system (i.e., when the aerial image captures the top of a building, tree, the
Information 2019,10, 349 13 of 26
vegetation etc.). Hence, the elevation model generated includes the elevation of the bare Earth
along with artificial and natural objects that may exist in the field.
The DEMS constructed can be exploited either for the extraction of 3D information directly
or to construct orthomosaics of the crops. An orthoimage, orthomosaic or orthophoto is an aerial
photograph that is orthorectified (i.e., geometrically corrected). Thus, the scale of the constructed
image is uniform. As a result, the final orthophoto has the same lack of distortion as a map. In contrast
with a simple aerial image of a field, an orthophoto can be used to measure true distances as it contains
the 3D characteristics of the crops.
The use of photogrammetry techniques have the following procedure: create point cloud
representations of the 3D surface and either combine all objects into a single Digital Elevation Model
or use the DEMs to generate an orthophoto. The most commonly used set of algorithms for this
purpose is Structure from Motion (SfM) [
]. The main advantage of SfM is that it does not require
any information regarding the camera parameters or the environmental settings.
(a) (b)
Figure 3. (a) A Digital Terrain Model [79]; and (b) a Digital Surface Model [109].
Photogrammetric techniques are commonly used in all types of applications as they are also
required for constructing the vegetation indices maps. Photogrammetric techniques are used in the
majority of recent works (93%) to exploit and extract information regarding the 3D characteristics of
the crops. However, photogrammetric techniques are in most cases used to compliment other types of
data processing methods.
4.2. Using Machine Learning
Machine Learning (ML) and Data Mining methods are widely used in PA to exploit the
information acquired by the UAVs. Taking into account the large amounts of data collected from
agricultural fields, machine learning can be applied to enhance the performance of UAV-based systems
for PA, by extracting knowledge for several parameters of the vegetation. ML is used in many cases
and for different purposes. Both unsupervised and supervised learning techniques are being exploited,
via clustering, classification and regression methods.
Regression methods are widely used in UAV applications for PA for a variety of purposes.
Regression has been used to estimate spectral vegetation indices by analyzing data acquired from RGB
images [
], presenting generally good results. Additionally, regression has been used to examine the
correlation of some vegetation indices with vegetation features such as nitrogen [
], leaf are
index [
], and biomass [
]. For this purpose, both linear (simple and multiple) regression
Information 2019,10, 349 14 of 26
and nonlinear regression methods have been used. In a comparison of different regression algorithms,
for estimating leaf nitrogen content, Random Forests presents the best results among 14 algorithms,
with the coefficient of determination (
) being up to 0.79 [
]. Regression methods are also used
to predict crop water status, by using information derived from RGB, multispectral and thermal
sensors [
]. In this context, Artificial Neural Networks present generally good results (
to 0.87 by using and information from some spectral bands).
Classification methods are also very commonly used for weed mapping [
] and
disease detection [
]. The most popular and precise classification techniques are the Artificial
Neural Networks (ANNs) family [
] and the Random Forest algorithm [
]. These
algorithms directly use the RGB colors, the intensity, spectral information or other features derived
from the image acquired. In some cases, data about the neighborhood of each pixel are also considered.
Apart from the above data, classification algorithms can also use vegetation indices as features in
the model to achieve higher accuracy. In general, ANNs present higher accuracy, compared to other
classification algorithms, that reaches up to 99% for weed mapping in some cases [
]. The accuracy
of the method depends on the type of the crops monitored [
] as expected. Convolutional Neural
Networks (CNNs), is among the most used family of algorithms. CNNs belong to deep learning
algorithms that have been proved to be very effective in object detection in large datasets.
The use of Deep Learning (DL) in Precision Agriculture applications is a recent, modern and
promising technique, having increasing popularity. Deep learning techniques extend typical ML by
adding more complexity into the derived models. DL techniques transform the data using various
functions that allow data representation in a hierarchical way, through several levels of abstraction.
Advancements and applications of Deep Learning into other domains indicate its large potential.
As indicated in [
], the use of Machine Learning and more particular Deep Learning will be even
more widespread in the next years.
A very common application of Machine Learning methods in PA is Object Based Image Analysis
(OBIA). The purpose of OBIA is to discriminate objects within agricultural images obtained from
UAVs [
]. In contrast to traditional pixel-based image classification, which classifies each
pixel, OBIA groups small pixels together into vector objects. With the higher spatial resolution
of UAV imagery, pixel-based classifications have become much less effective. That is due to
the fact that the relationship between the pixel size and the object size has changed significantly.
Therefore, object-oriented classification methods are increasing in popularity. They use segmentation
methods to divide the image pixels into homogeneous segments/groups. Then, these segments/objects
are arranged into classes based on their geometric, spectral, textural and other characteristics.
OBIA is usually composed of two main steps:
1. Image segmentation
2. Feature extraction and classification
These methods are exploited to recognize and detect weeds or discriminate different species in
the field. A detailed review of algorithms and challenges for OBIA, from a remote sensing perspective,
was reported by Hossain and Chen [135].
4.3. Vegetation Indices
Vegetation Indices (VIs) have been widely used in remote sensing applications for Precision
Agriculture. They are considered to be very effective for monitoring the growth and health of crops
in qualitative and quantitative vegetation analysis [
]. Vegetation Indices are based on the
absorption of electromagnetic radiation by the vegetation.
They are mathematical transformations of the absorption and scattering in different bands of
the electromagnetic spectrum. The reflectance in several bands is affected by parameters such as
vegetation biochemical and physical properties, environmental effects, soil background properties,
moisture content, etc. The understanding of the spectral behavior of the vegetation is fundamental
Information 2019,10, 349 15 of 26
to remote sensing applications to monitor various vegetation features (e.g., biomass [
], nitrogen
status [
], vegetation health [
], etc.) It has been shown that certain VIs are significantly related
with different parameters of the vegetation.
Simple Vegetation Indices that can combine RGB information and some spectral bands such as
NIR and RE have significantly improved the ability to detect green and healthy vegetation. Several VIs
have been developed, as different environments have their own complex characteristics, which needs
to be taken into account when using a Vegetation Index. Thus, each VI has its own specific combination
of the reflectance in different bands, in order to detect vegetation. Hence, its VI is suitable for specific
uses. The main concept is to combine the reflections of different bands to decrease the “noise” from
external factors (e.g., sensors calibration, lighting, atmosphere, soil properties, etc.). For example, as
mentioned in the previous section, visible radiation to the red is absorbed by the chlorophyll while the
radiation in the NIR band is strongly reflected. In this way, vegetation can be discriminated by the soil
in an image. In way, unhealthy vegetation can also be detected.
Vegetation Indices that are based on the radiation in the Red and NIR channels, such as the RVI
or the NDVI index, are designed to increase the contrast between the vegetation and the soil. The
relationship between the reflections of the two zones allows the elimination of disturbances by factors
that affect the radiation of each zone in the same way.
The effort to model the biophysical parameters of vegetation has led to the creation of several
different vegetation indices [
]. The vegetation indices can be divided into two main categories:
Vegetation Indices based on multispectral or hyperspectral data. Most of the developed Vegetation
Indices use multispectral and/or hyperspectral information that can combine several bands.
Vegetation Indices based on information from the visible spectrum. Several VIs in the visible
spectrum have been developed and are widely used due to the high cost of multispectral and
hyperspectral sensors.
A list of the most used vegetation indices is presented in Table 3. For the interpretation of
the following formulas, the following abbreviations represent the reflection in the respective color
or spectrum:
R: Red (620–670 nm)
G: Green (500–560 nm)
B: Blue (430–500 nm)
NIR: Near Infrared (720–1500 nm)
RE: Red Edge (670–720 nm)
Concerning the multispectral vegetation indices, one of the first well-known indices was Ratio
Vegetation Index (RVI). This index enhances the contrast among vegetation and soil. However, it is
sensitive to the optical properties of ground. The best known and most widely used vegetation index
is the Normalized Difference Vegetation Index (NDVI), which is the evolution of RVI and is calculated
by the visible and near infrared light reflected from the vegetation. Unhealthy or sparse vegetation
reflects more visible light and less near infrared light, making it easy to monitor the growth and health
of many agricultural crops. It is based on absorption in Red due to chlorophyll and reflectance in NIR.
RVI and NDVI are calculated as shown in Table 3.
Information 2019,10, 349 16 of 26
Table 3. Most used vegetation indices.
Vegetation Index Abbreviation Formula
Vegetation Indices derived from multispectral information
Ratio Vegetation Index RVI NIR
Normalized Difference Vegetation Index NDVI NIR R
Normalized Difference Red Edge Index NDRE NIR RE
Green Normalized Difference Vegetation Index GNDVI N IR G
RGB-based Vegetation Indices
Excess Greenness Index ExG 2 GRB
Normalized Difference Index NDI GR
Several other VIs have been developed based on NDVI. NDRE uses the method of NDVI to
normalize the ratio of
radiation with Red Edge (
) radiation. The same applies for GNDVI with
NIR and Green (G) bands.
Figure 4shows examples of crop maps constructed from information of spectral VIs (NDVI and
NDRE) in different growth stages. We can see that the difference in the maps while the vegetation
grows is quite clear.
(a) (b)
Figure 4.
Vegetation indices maps of crops in different growth stages [
]: (
) NDVI maps;
and (b) NDRE maps
Focusing on the VIs extracted from RGB images, Excess Greenness Index (ExG) and Normalized
Difference Index (NDI) are the most used indices. ExG is based on the assumption that plants display
a clear high degree of greenness, and soil is the only background element. Thus, it is calculated
by doubling the radiation in the Green channel minus the radiation in Red and Blue channels.
NDI was proposed to separate plants from soil and residue background images, using only green and
red channels.
Information 2019,10, 349 17 of 26
Although the VIs that use information in the visible light can be useful for crop monitoring,
they cannot provide information for several parameters of the vegetation and also they are sensitive
to working environment properties such as the atmosphere, lighting, etc. Hyperspectral remote
sensing is expected to be the future trend in crop monitoring and this is mainly because it allows the
development of new bands combination of vegetation indices. In many cases, it has been proved that
hyperspectral vegetation indices are less sensitive to saturation, change in viewing/lighting geometry,
and atmospheric contamination. The combination of new bands can eliminate noise from the working
environment and in the same time exploit the information of certain bands and extract information for
more biophysical features of the vegetation.
5. Limitations in the Use of UAVs for Precision Agriculture
Although the use of UAVs for PA is expanding there are several limitations that prevent their
wider use. The absence of a standardized workflow leads to the adoption of ad-hoc procedures for
deploying PA applications, a fact that discourages the relevant stakeholders. In addition, as PA requires
data-intensive procedures for the exploitation of the images acquired, skilled and expert personnel
is usually needed. This means that an average farmer may need training or even been forced to hire
experts to assist with the image processing, which may be costly. This fact may prohibit the adoption
of UAV technologies from individual farmers with only a few and small agricultural fields.
The high investment cost to purchase the Unmanned Aerial System is another prohibitive factor.
Producers with larger cultivated areas and higher profit rates are able to use more sophisticated,
high-cost systems, though this is not the case for the majority of the fields in Europe. There were
10.5 million agricultural holdings in the EU in 2016, two-thirds of which were less than 5 hectares in
size, as shown in the Eurostat survey [140].
Another drawback stems from UAV technology limitations. Most commercial UAVs have a short
flight time, ranging from 20 min to 1 h, covering a very restricted area at every flight. UAVs that can
offer longer flight time are relatively expensive. In addition, the effective use of UAVs is prone to
climatic conditions. For example, on a very windy or rainy day, the flight should be postponed.
As the use of UAVs for agriculture purposes is considered to be commercial, UAV flights should
also adhere to the related legislation and national rules. However, the EU regulations [
] for the
use of UAVs will replace national rules from July 2020. In practice, these regulations report that, once
an UAV pilot has received the appropriate authorization, he/she will be allowed to freely use UAVs in
the EU. The EU legislation will cover all types of possible UAVs operations, fostering the development
of innovative applications (e.g., for PA).
The EU regulations consider three categories of operations:
1. Open category
: Operations in this category do not require authorization or pilot license.
This category is limited to operations: in visual line of sight (VLOS), up to 120 m flight height and
performed with UAVs compliant with some technical requirements defined.
2. Specific category
: This is the second level of operations, which covers operations that are not
compliant to the restrictions of the open category and are considered “medium risk” operations.
Operators must perform a risk assessment (using a standardized method) and define mitigation
measures. Operations involving UAVs of more than 25 kg or not operated in VLOS will typically
fall under this category. The technical requirements for this category depend on the specific
authorized operation.
3. Certified category
: This category is considered high risk and includes operations involving large
drones in controlled air-spaces. Rules applicable to this category will be the same as for manned
aviation. This category does not concern the use of UAVs for Precision Agriculture.
Taking into account the rapid developments in UAV technology and the sensors use in PA,
the cost of the Unmanned Aerial Systems will be reduced in the near future. Practical limitations,
such as the short flight time, are also expected to be solved by the advancements in technology.
Information 2019,10, 349 18 of 26
These improvements will ensure that farmers can reap more from the use of UAVs for remote sensing
in Precision Agriculture.
6. Discussion and Conclusions
In this work, we review 100 recent applications of UAVs for Precision Agriculture. We present the
most frequent applications, the UAV types used and the Unmanned Aerial systems architecture for
PA. Then, we focus on the most used sensors for monitoring the crops and the processing methods
exploiting UAV imagery. We show that UAVs can have several applications, however monitoring crop
growth is the one that stands out. The following three main processing techniques stand out in the
literature for exploiting the information acquired:
Photogrammetry techniques are used to construct Orthophotos or DEMs from the overlapping
images acquired. They are used in most of the applications to create vegetation maps considering
several characteristics; however, they are also used standalone mainly when only RGB sensors
are available, to exploit the 3D characteristics of the vegetation.
Machine Learning methods can be used to monitor several different characteristics of the crops.
They can exploit RGB and/or multispectral/hyperspectral images. A very frequent technique
exploited in the literature is the Object Based Image Analysis.
Vegetation Indices use combinations of the reflection of several bands obtained by RGB or spectral
sensors. They are proved to be most effective when multispectral or hyperspectral information is
used. They have been used in many recent studies. They have been proved to be very effective in
monitoring various parameters of the crops, by using different combinations of spectral bands.
In Figure 5, we present some statistics for the use of sensors and processing methods for the
different applications.
Figure 5. The use of sensors and processing techniques in the different UAVs applications.
We can observe that there is no standardized workflow in most cases and different techniques
are used to exploit UAV-acquired information for the same type of application. For example, looking
at the sensors and techniques used for
monitoring the health of the crops
, we can see that it is
relatively premature. Disease detection is mainly applied through temporal analysis of the vegetation’s
growth. However, multispectral and hyperspectral sensors seem to have great potential in this
context. An approach using multispectral sensing technologies could reliably deliver Vegetation
Indices, discriminating healthy and unhealthy portions of the vegetation. In such an approach, the
farmer could locate the “weak” corps field areas and take timely decisions to save the parts of the crop
production that seem unhealthy.
On the contrary, considering
weed mapping
, we can see that some studies use machine learning
techniques to detect weeds, mainly OBIA, while others use the 3D characteristics of the crops though
DEMs, VIs or other processing methods. Weed detection with UAVs based on object-based image
analysis seems to be at an advanced stage and can be used for site-specific weed management. We can
see that machine learning techniques are exploited in 62.50% of the applications that used RGB sensors
and in all the applications with multispectral sensors.
Information 2019,10, 349 19 of 26
Focusing on UAV-based
growth monitoring
, many different methods are really promising but it
seems that their integration in a standardized workflow can improve its applicability and efficiency.
Growth monitoring and yield prediction is most commonly performed with the use of RGB (30%)
and multispectral (59%) sensors. The information acquired is exploited for estimating the density of
production and the biomass. The findings of this review appoint that combining information coming
from Vegetation Indices with the 3D characteristics of the crops coming from RGB images can improve
the accuracy of growth and yield estimation methods.
In contrast with the above, the use of UAV systems for
irrigation management
is closer to
a standard workflow, which involves the use of thermal and/or multispectral sensors to monitor the
needs of water of different parts of the crops. Equipped with thermal sensors, the Unmanned Aerial
Vehicles are able to detect possible deficiencies in the irrigation of different sites. This information
can be processed through photogrammetry techniques into a single high-resolution vegetation map,
highlighting the stressed areas. A map in this context can be constructed though the use of some VIs
when multispectral imagery is used. Thus, a map such as this can support the producers to apply
Variable Rate Irrigation (VRI) applications.
This research was co-funded by the European Union and Greek national funds through the Operational
Program Competitiveness, Entrepreneurship, and Innovation, grant number T1EDK-04873.
Conflicts of Interest: The authors declare no conflict of interest.
1. FAO. Declaration of the World Summit on Food Security; FAO: Rome, Italy, 2009.
Mylonas, P.; Voutos, Y.; Sofou, A. A Collaborative Pilot Platform for Data Annotation and Enrichment in
Viticulture. Information 2019,10, 149. [CrossRef]
Mulla, D.J. Twenty five years of remote sensing in precision agriculture: Key advances and remaining
knowledge gaps. Biosyst. Eng. 2013,114, 358–371. [CrossRef]
Bauer, M.E.; Cipra, J.E. Identification of Agricultural Crops by Computer Processing of ERTS MSS Data; LARS
Technical Reports; Purdue University: West Lafayette, IN, USA 1973; p. 20.
Mora, A.; Santos, T.; Lukasik, S.; Silva, J.; Falcao, A.; Fonseca, J.; Ribeiro, R. Land cover classification from
multispectral data using computational intelligence tools: A comparative study. Information
,8, 147.
Taylor, J.; William, R.; Munson, K. Jane’s Pocket Book of Remotely Piloted Vehicles: Robot Aircraft Today; Collier
Books: New York, NY, USA, 1977.
Zhang, C.; Kovacs, J.M. The application of small unmanned aerial systems for precision agriculture: A review.
Precis. Agric. 2012,13, 693–712. [CrossRef]
Yang, S.; Yang, X.; Mo, J. The application of unmanned aircraft systems to plant protection in China.
Precis. Agric. 2018,19, 278–292. [CrossRef]
Yang, G.; Liu, J.; Zhao, C.; Li, Z.; Huang, Y.; Yu, H.; Xu, B.; Yang, X.; Zhu, D.; Zhang,X.; et al. Unmanned aerial
vehicle remote sensing for field-based crop phenotyping: Current status and perspectives.
Front. Plant Sci.
2017,8, 1111. [CrossRef] [PubMed]
Mogili, U.R.; Deepak, B. Review on application of drone systems in precision agriculture.
Procedia Comput. Sci. 2018,133, 502–509. [CrossRef]
Puri, V.; Nayyar, A.; Raja, L. Agriculture drones: A modern breakthrough in precision agriculture. J. Stat.
Manag. Syst. 2017,20, 507–518. [CrossRef]
12. Kulbacki, M.; Segen, J.; Knie´c, W.; Klempous, R.; Kluwak, K.; Nikodem, J.; Kulbacka, J.; Serester, A. Survey
of Drones for Agriculture Automation from Planting to Harvest. In Proceedings of the 2018 IEEE 22nd
International Conference on Intelligent Engineering Systems (INES), Las Palmas de Gran Canaria, Spain,
21–23 June 2018; pp. 000353–000358.
Manfreda, S.; McCabe, M.; Miller, P.; Lucas, R.; Pajuelo Madrigal, V.; Mallinis, G.; Ben Dor, E.; Helman,
D.; Estes, L.; Ciraolo, G.; et al. On the use of unmanned aerial systems for environmental monitoring.
Remote Sens. 2018,10, 641. [CrossRef]
Information 2019,10, 349 20 of 26
Adão, T.; Hruška, J.; Pádua, L.; Bessa, J.; Peres, E.; Morais, R.; Sousa, J. Hyperspectral imaging: A review on
UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens.
,9, 1110.
Maes, W.H.; Steppe, K. Perspectives for remote sensing with unmanned aerial vehicles in precision
agriculture. Trends Plant Sci. 2019,24, 152–164. [CrossRef] [PubMed]
Kamilaris, A.; Prenafeta-Boldu, F.X. Deep learning in agriculture: A survey. Comput. Electron. Agric.
147, 70–90. [CrossRef]
Tsouros, D.C.; Triantafyllou, A.; Bibi, S.; Sarigannidis, P.G. Data acquisition and analysis methods in
UAV-based applications for Precision Agriculture. In Proceedings of the 2019 IEEE 15th International
Conference on Distributed Computing in Sensor Systems (DCOSS), Santorini Island, Greece, 29–31 May
2019; pp. 377–384
Huang, H.; Deng, J.; Lan, Y.; Yang, A.; Deng, X.; Zhang, L. A fully convolutional network for weed mapping
of unmanned aerial vehicle (UAV) imagery. PLoS ONE 2018,13, 1–19. [CrossRef] [PubMed]
Hunt, E.R.; Horneck, D.A.; Spinelli, C.B.; Turner, R.W.; Bruce, A.E.; Gadler, D.J.; Brungardt, J.J.; Hamm, P.B.
Monitoring nitrogen status of potatoes using small unmanned aerial vehicles. Precis. Agric.
,19, 314–333.
Zhang, J.; Basso, B.; Price, R.F.; Putman, G.; Shuai, G. Estimating plant distance in maize using Unmanned
Aerial Vehicle (UAV). PLoS ONE 2018,13, e0195223. [CrossRef] [PubMed]
Wang, J.J.; Ge, H.; Dai, Q.; Ahmad, I.; Dai, Q.; Zhou, G.; Qin, M.; Gu, C. Unsupervised discrimination
between lodged and non-lodged winter wheat: A case study using a low-cost unmanned aerial vehicle. Int.
J. Remote Sens. 2018,39, 2079–2088. [CrossRef]
Näsi, R.; Viljanen, N.; Kaivosoja, J.; Alhonoja, K.; Hakala, T.; Markelin, L.; Honkavaara, E. Estimating Biomass
and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D
Features. Remote Sens. 2018,10, 1082. [CrossRef]
Yonah, I.B.; Mourice, S.K.; Tumbo, S.D.; Mbilinyi, B.P.; Dempewolf, J. Unmanned aerial vehicle-based
remote sensing in monitoring smallholder, heterogeneous crop fields in Tanzania. Int. J. Remote Sens.
39, 5453–5471. [CrossRef]
Kellenberger, B.; Marcos, D.; Tuia, D. Detecting mammals in UAV images: Best practices to address
a substantially imbalanced dataset with deep learning. Remote Sens. Environ.
,216, 139–153. [CrossRef]
Mozgeris, G.; Jonikaviˇcius, D.; Jovarauskas, D.; Zinkeviˇcius, R.; Petkeviˇcius, S.; Steponaviˇcius, D.
Imaging from manned ultra-light and unmanned aerial vehicles for estimating properties of spring wheat.
Precis. Agric. 2018,19, 876–894. [CrossRef]
Deng, L.; Mao, Z.; Li, X.; Hu, Z.; Duan, F.; Yan, Y. UAV-based multispectral remote sensing for precision
agriculture: A comparison between different cameras. ISPRS J. Photogramm. Remote Sens.
,146, 124–136.
Zheng, H.; Cheng, T.; Li, D.; Zhou, X.; Yao, X.; Tian, Y.; Cao, W.; Zhu, Y. Evaluation of RGB, color-infrared and
multispectral images acquired from unmanned aerial systems for the estimation of nitrogen accumulation in
rice. Remote Sens. 2018,10, 824. [CrossRef]
Tewes, A.; Schellberg, J. Towards remote estimation of radiation use efficiency in maize using uav-based
low-cost camera imagery. Agronomy 2018,8, 16. [CrossRef]
Raeva, P.L.; Šedina, J.; Dlesk, A. Monitoring of crop fields using multispectral and thermal imagery from
UAV. Eur. J. Remote Sens. 2019,52, 192–201. [CrossRef]
Huang, Y.; Reddy, K.N.; Fletcher, R.S.; Pennington, D. UAV low-altitude remote sensing for precision weed
management. Weed Technol. 2018,32, 2–6. [CrossRef]
Gracia-Romero, A.; Vergara-Díaz, O.; Thierfelder, C.; Cairns, J.; Kefauver, S.; Araus, J. Phenotyping
conservation agriculture management effects on ground and aerial remote sensing assessments of maize
hybrids performance in Zimbabwe. Remote Sens. 2018,10, 349. [CrossRef]
De Castro, A.I.; Torres-Sánchez, J.; Peña, J.M.; Jiménez-Brenes, F.M.; Csillik, O.; López-Granados, F.
An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows
Using UAV Imagery. Remote Sens. 2018,10, 285. [CrossRef]
Lambert, J.; Hicks, H.; Childs, D.; Freckleton, R. Evaluating the potential of Unmanned Aerial Systems
for mapping weeds at field scales: A case study with Alopecurus myosuroides. Weed Res.
,58, 35–45.
[CrossRef] [PubMed]
Information 2019,10, 349 21 of 26
Uddin, M.A.; Mansour, A.; Jeune, D.L.; Ayaz, M.; Aggoune, E.-H.M. UAV-assisted dynamic clustering of
wireless sensor networks for crop health monitoring. Sensors 2018,18, 555. [CrossRef] [PubMed]
Khan, Z.; Rahimi-Eichi, V.; Haefele, S.; Garnett, T.; Miklavcic, S.J. Estimation of vegetation indices for
high-throughput phenotyping of wheat using aerial imaging. Plant Methods
,14, 20. [CrossRef]
Fan, X.; Kawamura, K.; Xuan, T.D.; Yuba, N.; Lim, J.; Yoshitoshi, R.; Minh, T.N.; Kurokawa, Y.; Obitsu, T.
Low-cost visible and near-infrared camera on an unmanned aerial vehicle for assessing the herbage biomass
and leaf area index in an Italian ryegrass field. Grassl. Sci. 2018,64, 145–150. [CrossRef]
Ziliani, M.; Parkes, S.; Hoteit, I.; McCabe, M. Intra-Season Crop Height Variability at Commercial Farm
Scales Using a Fixed-Wing UAV. Remote Sens. 2018,10, 2007. [CrossRef]
Zheng, H.; Li, W.; Jiang, J.; Liu, Y.; Cheng, T.; Tian, Y.; Zhu, Y.; Cao, W.; Zhang, Y.; Yao, X. A Comparative
Assessment of Different Modeling Algorithms for Estimating Leaf Nitrogen Content in Winter Wheat Using
Multispectral Images from an Unmanned Aerial Vehicle. Remote Sens. 2018,10, 2026. [CrossRef]
Han, X.; Thomasson, J.A.; Bagnall, G.C.; Pugh, N.; Horne, D.W.; Rooney, W.L.; Jung, J.; Chang, A.;
Malambo, L.; Popescu, S.C.; et al. Measurement and calibration of plant-height from fixed-wing UAV
images. Sensors 2018,18, 4092. [CrossRef] [PubMed]
Torres-Sánchez, J.; de Castro, A.I.; Peña, J.M.; Jiménez-Brenes, F.M.; Arquero, O.; Lovera, M.;
López-Granados, F. Mapping the 3D structure of almond trees using UAV acquired photogrammetric
point clouds and object-based image analysis. Biosyst. Eng. 2018,176, 172–184. [CrossRef]
Comba, L.; Biglia, A.; Aimonino, D.R.; Gay, P. Unsupervised detection of vineyards by 3D point-cloud UAV
photogrammetry for precision agriculture. Comput. Electron. Agric. 2018,155, 84–95. [CrossRef]
Su, J.; Liu, C.; Coombes, M.; Hu, X.; Wang, C.; Xu, X.; Li, Q.; Guo, L.; Chen, W.H. Wheat yellow rust
monitoring by learning from multispectral UAV aerial imagery. Comput. Electron. Agric.
,155, 157–166.
De Castro, A.; Jiménez-Brenes, F.; Torres-Sánchez, J.; Peña, J.; Borra-Serrano, I.; López-Granados, F. 3-D
characterization of vineyards using a novel UAV imagery-based OBIA procedure for precision viticulture
applications. Remote Sens. 2018,10, 584. [CrossRef]
44. Bah, M.D.; Hafiane, A.; Canals, R. Deep Learning with Unsupervised Data Labeling for Weed Detection in
Line Crops in UAV Images. Remote Sens. 2018,10, 1690. [CrossRef]
Wierzbicki, D.; Fryskowska, A.; Kedzierski, M.; Wojtkowska, M.; Delis, P. Method of radiometric quality
assessment of NIR images acquired with a custom sensor mounted on an unmanned aerial vehicle. J. Appl.
Remote Sens. 2018,12, 015008. [CrossRef]
Kerkech, M.; Hafiane, A.; Canals, R. Deep leaning approach with colorimetric spaces and vegetation indices
for vine diseases detection in UAV images. Comput. Electron. Agric. 2018,155, 237–243. [CrossRef]
Latif, M.A.; Cheema, M.J.M.; Saleem, M.F.; Maqsood, M. Mapping wheat response to variations in N, P, Zn,
and irrigation using an unmanned aerial vehicle. Int. J. Remote Sens. 2018,39, 7172–7188. [CrossRef]
Jung, J.; Maeda, M.; Chang, A.; Landivar, J.; Yeom, J.; McGinty, J. Unmanned aerial system assisted
framework for the selection of high yielding cotton genotypes. Comput. Electron. Agric.
,152, 74–81.
Wan, L.; Li, Y.; Cen, H.; Zhu, J.; Yin, W.; Wu, W.; Zhu, H.; Sun, D.; Zhou, W.; He, Y. Combining UAV-Based
Vegetation Indices and Image Classification to Estimate Flower Number in Oilseed Rape. Remote Sens.
10, 1484. [CrossRef]
Sa, I.; Popovi´c, M.; Khanna, R.; Chen, Z.; Lottes, P.; Liebisch, F.; Nieto, J.; Stachniss, C.; Walter, A.; Siegwart, R.
Weedmap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep
neural network for precision farming. Remote Sens. 2018,10, 1423. [CrossRef]
Ballesteros, R.; Ortega, J.F.; Hernandez, D.; Moreno, M.A. Onion biomass monitoring using UAV-based RGB
imaging. Precis. Agric. 2018,19, 840–857. [CrossRef]
Simic Milas, A.; Romanko, M.; Reil, P.; Abeysinghe, T.; Marambe, A. The importance of leaf area index
in mapping chlorophyll content of corn under different agricultural treatments using UAV images. Int. J.
Remote Sens. 2018,39, 5415–5431. [CrossRef]
Mesas-Carrascosa, F.J.; Pérez-Porras, F.; Meroño de Larriva, J.; Mena Frau, C.; Agüera-Vega, F.;
Carvajal-Ramírez, F.; Martínez-Carricondo, P.; García-Ferrer, A. Drift correction of lightweight
microbolometer thermal sensors on-board unmanned aerial vehicles. Remote Sens.
,10, 615. [CrossRef]
Information 2019,10, 349 22 of 26
Varela, S.; Dhodda, P.; Hsu, W.; Prasad, P.; Assefa, Y.; Peralta, N.; Griffin, T.; Sharda, A.; Ferguson, A.;
Ciampitti, I. Early-season stand count determination in corn via integration of imagery from unmanned
aerial systems (UAS) and supervised learning techniques. Remote Sens. 2018,10, 343. [CrossRef]
Marino, S.; Alvino, A. Detection of homogeneous wheat areas using multi-temporal UAS images and ground
truth data analyzed by cluster analysis. Eur. J. Remote Sens. 2018,51, 266–275. [CrossRef]
Marcial-Pablo, M.d.J.; Gonzalez-Sanchez, A.; Jimenez-Jimenez, S.I.; Ontiveros-Capurata, R.E.;
Ojeda-Bustamante, W. Estimation of vegetation fraction using RGB and multispectral images from UAV.
Int. J. Remote Sens. 2019,40, 420–438. [CrossRef]
Oliveira, H.C.; Guizilini, V.C.; Nunes, I.P.; Souza, J.R. Failure detection in row crops from UAV images using
morphological operators. IEEE Geosci. Remote Sens. Lett. 2018,15, 991–995. [CrossRef]
Mafanya, M.; Tsele, P.; Botai, J.O.; Manyama, P.; Chirima, G.J.; Monate, T. Radiometric calibration framework
for ultra-high-resolution UAV-derived orthomosaics for large-scale mapping of invasive alien plants in
semi-arid woodlands: Harrisia pomanensis as a case study. Int. J. Remote Sens.
,39, 5119–5140.
Hassanein, M.; Lari, Z.; El-Sheimy, N. A new vegetation segmentation approach for cropped fields based on
threshold detection from hue histograms. Sensors 2018,18, 1253. [CrossRef] [PubMed]
Jeong, S.; Ko, J.; Choi, J.; Xue, W.; Yeom, J.-m. Application of an unmanned aerial system for monitoring
paddy productivity using the GRAMI-rice model. Int. J. Remote Sens. 2018,39, 2441–2462. [CrossRef]
Iwasaki, K.; Torita, H.; Abe, T.; Uraike, T.; Touze, M.; Fukuchi, M.; Sato, H.; Iijima, T.; Imaoka, K.; Igawa, H.
Spatial pattern of windbreak effects on maize growth evaluated by an unmanned aerial vehicle in Hokkaido,
northern Japan. Agrofor. Syst. 2019,93, 1133–1145. [CrossRef]
Li, Y.; Qian, M.; Liu, P.; Cai, Q.; Li, X.; Guo, J.; Yan, H.; Yu, F.; Yuan, K.; Yu, J.; et al. The recognition of rice
images by UAV based on capsule network. Clust. Comput. 2018, 1–10. [CrossRef]
Aasen, H.; Bolten, A. Multi-temporal high-resolution imaging spectroscopy with hyperspectral 2D
imagers–From theory to application. Remote Sens. Environ. 2018,205, 374–389. [CrossRef]
Quebrajo, L.; Perez-Ruiz, M.; Pérez-Urrestarazu, L.; Martínez, G.; Egea, G. Linking thermal imaging and soil
remote sensing to enhance irrigation management of sugar beet. Biosyst. Eng. 2018,165, 77–87. [CrossRef]
Han, L.; Yang, G.; Yang, H.; Xu, B.; Li, Z.; Yang, X. Clustering field-based maize phenotyping of plant-height
growth and canopy spectral dynamics using a UAV remote-sensing approach. Front. Plant Sci.
,9, 1638.
[CrossRef] [PubMed]
rížová, K.; Kroulík, M.; Haberle, J.; Lukáš, J.; Kumhálová, J. Assessment of soil electrical conductivity using
remotely sensed thermal data. Agron. Res. 2018,16, 784–793.
Huang, C.-y.; Wei, H.L.; Rau, J.Y.; Jhan, J.P. Use of principal components of UAV-acquired narrow-band
multispectral imagery to map the diverse low stature vegetation fAPAR. GISci. Remote Sens.
,56, 605–623.
Souza, I.R.; Escarpinati, M.C.; Abdala, D.D. A curve completion algorithm for agricultural planning.
In Proceedings of the 33rd Annual ACM Symposium on Applied Computing, Pau, France, 9–13 April 2018;
pp. 284–291.
Pascuzzi, S.; Anifantis, A.S.; Cimino, V.; Santoro, F. Unmanned aerial vehicle used for remote sensing on an
apulian farm in southern Italy. In Proceedings of the 17th International Scientific Conference Engineering for
Rural Development, Jelgava, Latvia, 23–25 May 2018; pp. 23–25.
Bah, M.D.; Hafiane, A.; Canals, R. Weeds detection in UAV imagery using SLIC and the hough transform.
In Proceedings of the 2017 Seventh International Conference on Image Processing Theory, Tools and
Applications (IPTA), Montreal, QC, Canada, 28 November–1 December 2017; pp. 1–6.
Pantelej, E.; Gusev, N.; Voshchuk, G.; Zhelonkin, A. Automated field monitoring by a group of light
aircraft-type UAVs. In Proceedings of the International Conference on Intelligent Information Technologies
for Industry, Sochi, Russia, 17–21 September 2018; Springer: Cham, Switzerland, 2018; pp. 350–358.
Parraga, A.; Doering, D.; Atkinson, J.G.; Bertani, T.; de Oliveira Andrades Filho, C.; de Souza, M.R.Q.;
Ruschel, R.; Susin, A.A. Wheat Plots Segmentation for Experimental Agricultural Field from Visible and
Multispectral UAV Imaging. In Proceedings of the SAI Intelligent Systems Conference, London, UK,
6–7 September 2018; Springer: Cham, Switzerland, 2018; pp. 388–399.
Information 2019,10, 349 23 of 26
Bah, M.D.; Dericquebourg, E.; Hafiane, A.; Canals, R. Deep Learning Based Classification System for
Identifying Weeds Using High-Resolution UAV Imagery. In Proceedings of the Science and Information
Conference, London, UK, 10–12 July 2018; Springer: Cham, Switzerland, 2018; pp. 176–187.
Mancini, A.; Frontoni, E.; Zingaretti, P. Improving Variable Rate Treatments by Integrating Aerial and
Ground Remotely Sensed Data. In Proceedings of the 2018 International Conference on Unmanned Aircraft
Systems (ICUAS), Dallas, TX, USA, 12–15 June 2018; pp. 856–863.
Palomino, W.; Morales, G.; Huamán, S.; Telles, J. PETEFA: Geographic Information System for Precision
Agriculture. In Proceedings of the 2018 IEEE XXV International Conference on Electronics, Electrical
Engineering and Computing (INTERCON), Lima, Peru, 8–10 August 2018; pp. 1–4.
De Oca, A.M.; Arreola, L.; Flores, A.; Sanchez, J.; Flores, G. Low-cost multispectral imaging system for crop
monitoring. In Proceedings of the 2018 International Conference on Unmanned Aircraft Systems (ICUAS),
Dallas, TX, USA, 12–15 June 2018; pp. 443–451.
Montero, D.; Rueda, C. Detection of palm oil bud rot employing artificial vision. In IOP Conference Series:
Materials Science and Engineering; IOP Publishing: Bristol, UK, 2018; Volume 437, p. 012004.
Wang, X.; Sun, H.; Long, Y.; Zheng, L.; Liu, H.; Li, M. Development of Visualization System for Agricultural
UAV Crop Growth Information Collection. IFAC-PapersOnLine 2018,51, 631–636. [CrossRef]
Ronchetti, G.; Pagliari, D.; Sona, G. DTM Generation Through UAV Survey With a FISHEYE Camera On a
Vineyard. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2018,42, 2. [CrossRef]
Hassanein, M.; El-Sheimy, N. An efficient weed detection procedure using low-cost UAV imagery system for
precision agriculture applications. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2018. [CrossRef]
Lussem, U.; Bolten, A.; Gnyp, M.; Jasper, J.; Bareth, G. Evaluation of RGB-based vegetation indices from
UAV imagery to estimate forage yield in Grassland. ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.
2018, 1215–1219. [CrossRef]
Rudd, J.D.; Roberson, G.T. Using unmanned aircraft systems to develop variable rate prescription maps
for cotton defoliants. In Proceedings of the 2018 ASABE Annual International Meeting, Detroit, MI, USA,
29 July–1 August 2018; American Society of Agricultural and Biological Engineers: St. Joseph, MI, USA,
2018; p. 1.
Soares, G.A.; Abdala, D.D.; Escarpinati, M. Plantation Rows Identification by Means of Image Tiling and
Hough Transform. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging
and Computer Graphics Theory and Applications (VISIGRAPP 2018), Madeira, Portugal, 27–29 January
2018; pp. 453–459.
Zhao, T.; Niu, H.; de la Rosa, E.; Doll, D.; Wang, D.; Chen, Y. Tree canopy differentiation using instance-aware
semantic segmentation. In Proceedings of the 2018 ASABE Annual International Meeting, Detroit, MI, USA,
29 July–1 August 2018; American Society of Agricultural and Biological Engineers: St. Joseph, MI, USA,
2018; p. 1.
Zhao, T.; Yang, Y.; Niu, H.; Wang, D.; Chen, Y. Comparing U-Net convolutional network with mask R-CNN
in the performances of pomegranate tree canopy segmentation. Proc. SPIE 2018,10780, 107801J.
Pap, M.; Kiraly, S. Comparison of segmentation methods on images of energy plants obtained by UAVs.
In Proceedings of the 2018 IEEE International Conference on Future IoT Technologies (Future IoT), Eger,
Hungary, 18–19 January 2018; pp. 1–8.
Wahab, I.; Hall, O.; Jirström, M. Remote Sensing of Yields: Application of UAV Imagery-Derived NDVI for
Estimating Maize Vigor and Yields in Complex Farming Systems in Sub-Saharan Africa. Drones
,2, 28.
Bhandari, S.; Raheja, A.; Chaichi, M.R.; Green, R.L.; Do, D.; Pham, F.H.; Ansari, M.; Wolf, J.G.; Sherman, T.M.;
Espinas, A. Effectiveness of UAV-Based Remote Sensing Techniques in Determining Lettuce Nitrogen and
Water Stresses. In Proceedings of the 14th International Conference in Precision Agriculture, Montreal, QC,
Canada, 24–27 June 2018.
Xue, X.; Lan, Y.; Sun, Z.; Chang, C.; Hoffmann, W.C. Develop an unmanned aerial vehicle based automatic
aerial spraying system. Comput. Electron. Agric. 2016,128, 58–66. [CrossRef]
Hunt, E., Jr.; Horneck, D.; Hamm, P.; Gadler, D.; Bruce, A.; Turner, R.; Spinelli, C.; Brungardt, J. Detection
of nitrogen deficiency in potatoes using small unmanned aircraft systems. In Proceedings of the 12th
International Conference on Precision Agriculture, Sacramento, CA, USA, 20–23 July 2014.
Information 2019,10, 349 24 of 26
Bellvert, J.; Zarco-Tejada, P.J.; Marsal, J.; Girona, J.; González-Dugo, V.; Fereres, E. Vineyard irrigation
scheduling based on airborne thermal imagery and water potential thresholds. Aust. J. Grape Wine Res.
22, 307–315. [CrossRef]
Romero, M.; Luo, Y.; Su, B.; Fuentes, S. Vineyard water status estimation using multispectral imagery from
an UAV platform and machine learning algorithms for irrigation scheduling management. Comput. Electron.
Agric. 2018,147, 109–117. [CrossRef]
Wang, G.; Lan, Y.; Qi, H.; Chen, P.; Hewitt, A.; Han, Y. Field evaluation of an unmanned aerial vehicle (UAV)
sprayer: Effect of spray volume on deposition and the control of pests and disease in wheat. Pest Manag. Sci.
2019,75, 1546–1555. [CrossRef] [PubMed]
Hentschke, M.; Pignaton de Freitas, E.; Hennig, C.; Girardi da Veiga, I. Evaluation of Altitude Sensors for
a Crop Spraying Drone. Drones 2018,2, 25. [CrossRef]
Garre, P.; Harish, A. Autonomous Agricultural Pesticide Spraying UAV. In IOP Conference Series: Materials
Science and Engineering; IOP Publishing: Bristol, UK, 2018; Volume 455, p. 012030.
Lan, Y.; Chen, S. Current status and trends of plant protection UAV and its spraying technology in China.
Int. J. Precis. Agric. Aviat. 2018,1, 1–9. [CrossRef]
Albetis, J.; Jacquin, A.; Goulard, M.; Poilvé, H.; Rousseau, J.; Clenet, H.; Dedieu, G.; Duthoit, S. On the
potentiality of UAV multispectral imagery to detect Flavescence dorée and Grapevine Trunk Diseases.
Remote Sens. 2019,11, 23. [CrossRef]
Dos Santos Ferreira, A.; Freitas, D.M.; da Silva, G.G.; Pistori, H.; Folhes, M.T. Weed detection in soybean
crops using ConvNets. Comput. Electron. Agric. 2017,143, 314–324. [CrossRef]
Ballester, C.; Hornbuckle, J.; Brinkhoff, J.; Smith, J.; Quayle, W. Assessment of in-season cotton nitrogen
status and lint yield prediction from unmanned aerial system imagery. Remote Sens.
,9, 1149. [CrossRef]
Gnädinger, F.; Schmidhalter, U. Digital counts of maize plants by unmanned aerial vehicles (UAVs).
Remote Sens. 2017,9, 544. [CrossRef]
Percival, D.; Gallant, D.; Harrington, T.; Brown, G. Potential for commercial unmanned aerial vehicle
use in wild blueberry production. In XI International Vaccinium Symposium 1180; International Society for
Horticultural Science (ISHS): Orlando, FL, USA, 2016; pp. 233–240.
Yao, X.; Wang, N.; Liu, Y.; Cheng, T.; Tian, Y.; Chen, Q.; Zhu, Y. Estimation of wheat LAI at middle to
high levels using unmanned aerial vehicle narrowband multispectral imagery. Remote Sens.
,9, 1304.
Maimaitijiang, M.; Ghulam, A.; Sidike, P.; Hartling, S.; Maimaitiyiming, M.; Peterson, K.; Shavers, E.;
Fishman, J.; Peterson, J.; Kadam, S.; et al. Unmanned Aerial System (UAS)-based phenotyping of soybean
using multi-sensor data fusion and extreme learning machine. ISPRS J. Photogramm. Remote Sens.
134, 43–58. [CrossRef]
Poblete, T.; Ortega-Farías, S.; Moreno, M.; Bardeen, M. Artificial neural network to predict vine water status
spatial variability using multispectral information obtained from an unmanned aerial vehicle (UAV). Sensors
2017,17, 2488. [CrossRef] [PubMed]
Jermthaisong, P.; Kingpaiboon, S.; Chawakitchareon, P.; Kiyoki, Y. Relationship between vegetation indices
and SPAD values of waxy corn using an unmanned aerial vehicle. Inf. Model. Knowl. Bases XXX
312, 312.
Gao, J.; Liao, W.; Nuyttens, D.; Lootens, P.; Vangeyte, J.; Pižurica, A.; He, Y.; Pieters, J.G. Fusion of pixel
and object-based features for weed mapping using unmanned aerial vehicle imagery. Int. J. Appl. Earth
Obs. Geoinf. 2018,67, 43–53. [CrossRef]
Iqbal, F.; Lucieer, A.; Barry, K. Poppy crop capsule volume estimation using UAS remote sensing and random
forest regression. Int. J. Appl. Earth Obs. Geoinf. 2018,73, 362–373. [CrossRef]
Huuskonen, J.; Oksanen, T. Soil sampling with drones and augmented reality in precision agriculture.
Comput. Electron. Agric. 2018,154, 25–35. [CrossRef]
Jorge, J.; Vallbé, M.; Soler, J.A. Detection of irrigation inhomogeneities in an olive grove using the NDRE
vegetation index obtained from UAV images. Eur. J. Remote Sens. 2019,52, 169–177. [CrossRef]
Bhandari, S.; Raheja, A.; Chaichi, M.R.; Green, R.L.; Do, D.; Pham, F.H.; Ansari, M.; Wolf, J.G.; Sherman, T.M.;
Espinas, A. Lessons Learned from UAV-Based Remote Sensing for Precision Agriculture. In Proceedings of
the 2018 International Conference on Unmanned Aircraft Systems (ICUAS), Dallas, TX, USA, 12–15 June
2018; pp. 458–467.
Information 2019,10, 349 25 of 26
Franco, C.; Guada, C.; Rodríguez, J.T.; Nielsen, J.; Rasmussen, J.; Gómez, D.; Montero, J. Automatic detection
of thistle-weeds in cereal crops from aerial RGB images. In International Conference on Information Processing
and Management of Uncertainty in Knowledge-Based Systems; Springer: Cham, Switzerland, 2018; pp. 441–452.
Sobayo, R.; Wu, H.H.; Ray, R.; Qian, L. Integration of Convolutional Neural Network and Thermal Images
into Soil Moisture Estimation. In Proceedings of the 2018 1st International Conference on Data Intelligence
and Security (ICDIS), South Padre Island, TX, USA, 8–10 April 2018; pp. 207–210.
Oliveira, R.; Khoramshahi, E.; Suomalainen, J.; Hakala, T.; Viljanen, N.; Honkavaara, E. Real-time and
post-processed georeferencing for hyperpspectral drone remote sensing. Int. Arch. Photogramm. Remote Sens.
Spat. Inf. Sc. 2018,42, 789–795. [CrossRef]
Liu, J.; Chen, P.; Xu, X. Estimating Wheat Coverage Using Multispectral Images Collected by Unmanned
Aerial Vehicles and a New Sensor. In Proceedings of the 2018 7th International Conference on
Agro-geoinformatics (Agro-geoinformatics), Hangzhou, China, 6–9 August 2018; pp. 1–5.
Maurya, A.K.; Singh, D.; Singh, K. Development of Fusion Approach for Estimation of Vegetation Fraction
Cover with Drone and Sentinel-2 Data. In Proceedings of the IGARSS 2018-2018 IEEE International
Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018; pp. 7448–7451.
Kumpumäki, T.; Linna, P.; Lipping, T. Crop Lodging Analysis from Uas Orthophoto Mosaic, Sentinel-2
Image and Crop Yield Monitor Data. In Proceedings of the IGARSS 2018-2018 IEEE International Geoscience
and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018; pp. 7723–7726.
Falco, N.; Wainwright, H.; Ulrich, C.; Dafflon, B.; Hubbard, S.S.; Williamson, M.; Cothren, J.D.; Ham, R.G.;
McEntire, J.A.; McEntire, M. Remote Sensing to Uav-Based Digital Farmland. In Proceedings of the IGARSS
2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018;
pp. 5936–5939.
Albornoz, C.; Giraldo, L.F. Trajectory design for efficient crop irrigation with a UAV. In Proceedings of the
2017 IEEE 3rd Colombian Conference on Automatic Control (CCAC), Cartagena, Colombia, 18–20 October
2017; pp. 1–6.
Chartzoulakis, K.; Bertaki, M. Sustainable water management in agriculture under climate change.
Agric. Agric. Sci. Procedia 2015,4, 88–98. [CrossRef]
120. Saccon, P. Water for agriculture, irrigation management. Appl. Soil Ecol. 2018,123, 793–796. [CrossRef]
Gupta, S.G.; Ghonge, M.M.; Jawandhiya, P. Review of unmanned aircraft system (UAS). Int. J. Adv. Res.
Comput. Eng. Technol. (IJARCET) 2013,2, 1646–1658. [CrossRef]
Cai, G.; Dias, J.; Seneviratne, L. A survey of small-scale unmanned aerial vehicles: Recent advances and
future development trends. Unmanned Syst. 2014,2, 175–199. [CrossRef]
Palossi, D.; Gomez, A.; Draskovic, S.; Keller, K.; Benini, L.; Thiele, L. Self-sustainability in nano unmanned
aerial vehicles: A blimp case study. In Proceedings of the Computing Frontiers Conference, Siena, Italy,
15–17 May 2017; pp. 79–88.
Oettershagen, P.; Stastny, T.; Mantel, T.; Melzer, A.; Rudin, K.; Gohl, P.; Agamennoni, G.; Alexis, K.;
Siegwart, R. Long-endurance sensing and mapping using a hand-launchable solar-powered uav. In Field and
Service Robotics; Springer: Cham, Switzerland, 2016.
Maltamo, M.; Naesset, E.; Vauhkonen, J. Forestry applications of airborne laser scanning. Concepts Case Stud.
Manag. For. Ecosyst. 2014,27, 460.
Tsouros, D.C.; Smyrlis, P.N.; Tsipouras, M.G.; Tsalikakis, D.G.; Giannakeas, N.; Tzallas, A.T.; Manousou, P.
Automated collagen proportional area extraction in liver biopsy images using a novel classification via
clustering algorithm. In Proceedings of the 2017 IEEE 30th International Symposium on Computer-Based
Medical Systems (CBMS), Thessaloniki, Greece, 22–24 June 2017; pp. 30–34.
Bonotis, P.A.; Tsouros, D.C.; Smyrlis, P.N.; Tzallas, A.T.; Giannakeas, N.; Evripidis, G.; Tsipouras, M.G.
Automated Assesment of Pain Intensity based on EEG Signal Analysis. In Proceedings of the IEEE 19th
International Conference on BioInformatics and BioEngineering, Athens, Greece, 28–30 October 2019.
Cui, D.; Curry, D. Prediction in marketing using the support vector machine. Mark. Sci.
,24, 595–615.
Tarca, A.L.; Carey, V.J.; Chen, X.W.; Romero, R.; Dr˘aghici, S. Machine learning and its applications to biology.
PLoS Comput. Biol. 2007,3, e116. [CrossRef] [PubMed]
130. Leica Geosystems. ERDAS Imagine; Leica Geosystems: Atalanta, GA, USA, 2004.
Information 2019,10, 349 26 of 26
Baatz, M.; Benz, U.; Dehghani, S.; Heynen, M.; Holtje, A.; Hofmann, P.; Lingenfelder, I.; Mimler, M.;
Sohlbach, M.; Weber, M. eCognition Professional User Guide 4; ADefiniens Imaging: Munich, Germany, 2004.
132. Tetracam, Inc. ADC Users Guide V2.3; Tetracam, Inc.: Chatsworth, CA, USA, 2011.
Westoby, M.J.; Brasington, J.; Glasser, N.F.; Hambrey, M.J.; Reynolds, J. Structure-from-Motion
photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology
,179, 300–314.
Liakos, K.; Busato, P.; Moshou, D.; Pearson, S.; Bochtis, D. Machine learning in agriculture: A review. Sensors
2018,18, 2674. [CrossRef] [PubMed]
Hossain, M.D.; Chen, D. Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms
and challenges from remote sensing perspective. ISPRS J. Photogramm. Remote Sens.
,150, 115–134.
Wiegand, C.; Richardson, A.; Escobar, D.; Gerbermann, A. Vegetation indices in crop assessments.
Remote Sens. Environ. 1991,35, 105–119. [CrossRef]
Tanriverdi, C. A review of remote sensing and vegetation indices in precision farming. J. Sci. Eng.
9, 69–76.
Bannari, A.; Morin, D.; Bonn, F.; Huete, A. A review of vegetation indices. Remote Sens. Rev.
,13, 95–120.
Xue, J.; Su, B. Significant remote sensing vegetation indices: A review of developments and applications.
J. Sens. 2017,2017, 1353691. [CrossRef]
140. Eurostat. Agriculture, Forestry and Fishery Statistics; Eurostat: Luxembourg, 2018.
European Commission. Commission Delegated Regulation (EU) 2019/945 of 12 March 2019 on unmanned
aircraft systems and on third-country operators of unmanned aircraft systems. Off. J. Eur. Union
2019,L 152, 1–40.
European Commission. Commission Implementing Regulation (EU) 2019/947 of 24 May 2019 on the rules
and procedures for the operation of unmanned aircraft. Off. J. Eur. Union 2019,L 152, 45–70.
2019 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 (
... Furthermore, certain sensors can monitor plant biomass, vegetation health, and other critical agricultural properties at various phases of plant development. This data can also be utilized to monitor utilizing certain wavelengths of radiation [29]. ...
... Drone technology which is an emerging technology is capable of providing significant functions in precision agriculture and smart farming, to enable the increases in long-term production [45] by the acquisition of real-time environmental data. Drone is one of the breakthroughs for smart and precision agriculture farming, which is utilized for monitoring vast and cultivated lands and provides practical solutions for precision farming [5,29]. With that, the main purpose of precision farming to optimize yields and maintain sustainable crop production capacity based on crop monitoring and crop health assessment [44] can be effectively achieved. ...
... In just a few minutes, a drone can be able to collect data covering several acres of area and provide images to detect the weed patches [120]. Later, those images will be processed using deep neural networks [121], convolutional neural networks, and OBIA [29,60]. The final data will be concluded in three types of sensors such as RGB, multispectral, and hyperspectral sensors. ...
Full-text available
Oil palm has become one of the largest plantation industries in Malaysia, but the constraints in terms of manpower and time to monitor the development of this industry have caused many losses in terms of time and expense of oil palm plantation management. The introduction to the use of drone technology will help oil palm industry operators increase the effectiveness in the management of oil palm cultivation and production. In addition, knowledge gaps on drone technology were identified, and suggestions for further improvement could be implemented. Therefore, this study reviews the application and potential of drone technology in oil palm plantation, and the limitation and potential of the methods will be discussed.
... Remotely captured images/data in huge continuous narrow bands can be used for understanding the biophysical properties [6][7][8][9]. Remote sensing can detect interaction between electromagnetic radiation and soil or plant material on the Earth's surface. The amount of energy reflected from the leaves varies based on its biochemical properties. ...
... Agriculture crop monitoring system, generally preferred with light weight UAV class. UAVs are classified based on the type of flight mechanism as fixed wing, Multirotor, single rotor and fixed wing hybrid [3][4][5][6][7][8][9][10]. Table 1 projects classification of UAVs based on different parameters [31]. ...
... In satellite based remote sensing technique the degree of overlap between X and Y during crop-growth periods is low and hence to overcome this UAV-LARS serves the best emerging technology with significant advantages as simple construction, high spatial temporal resolution [3][4][5][7] [8]. Hence, UAV's can be deployed to deliver multispectral and hyperspectral data of the field in research for monitoring crop health [6]. ...
Full-text available
It is a known fact that India is one among the major food producers in the world. Due to the rapid urbanization and Agrochemical interventions, plants are infected by insects, pathogens, massive infestations, which leads to the deficiency of growth nutrients and ions, thereby minimizing agricultural crop yield. These infections occur in several forms like aphids, black root rot, cankers, leaf curls, rusts, anthracnose, and blights make agriculture fail to thrive and increase the impact of crop diseases. Since plant disease prevention is a continuous process that occurs regularly, agriculturists follow several strategies to keep their crops disease-free. An efficient monitoring and supporting system for continuous and long-term plant and soil health monitoring is needed to meet the needs of growing population. In this paper, existing research works in Precision agriculture, emerging technologies – GPS, GIS, Machine learning and UAVs in analyzing crop health analysis, soil health monitoring, and crop yield prediction are reviewed.
... Multi-temporal imaging using an unmanned aerial vehicle (UAV) has recently become a rapidly developing technology. It has been broadly applied in monitoring field crops due to its high competence, elevated temporal and geographical resolution, easy customization, and low cost [1][2][3][4][5]. It provides farmers and scientists with the actual and instinctive determination of crop growth status, as color images indicate particular vegetation greenness. ...
Full-text available
The current study aimed to evaluate the potential of the normalized difference vegetation index (NDVI), and the normalized difference yellowness index (NDYI) derived from red–green–blue (RGB) imaging to monitor the growth status of winter oilseed rape from seeding to the ripening stage. Subsequently, collected values were used to evaluate their correlations with the yield of oilseed rape. Field trials with three seed densities and three nitrogen rates were conducted for two years in Salzdahlum, Germany. The images were rapidly taken by an unmanned aerial vehicle carrying a Micasense Altum multi-spectral camera at 25 m altitudes. The NDVI and NDYI values for each plot were calculated from the reflectance at RGB and near-infrared (NIR) bands’ wavelengths pictured in a reconstructed and segmented ortho-mosaic. The findings support the potential of phenotyping data derived from NDVI and NDYI time series for precise oilseed rape phenological monitoring with all growth stages, such as the seedling stage and crop growth before winter, the formation of side shoots and stem elongation after winter, the flowering stage, maturity, ripening, and senescence stages according to the crop calendar. However, in comparing the correlation results between NDVI and NDYI with the final yield, the NDVI values turn out to be more reliable than the NDYI for the real-time remote sensing monitoring of winter oilseed rape growth in the whole season in the study area. In contrast, the correlation between NDYI and the yield revealed that the NDYI value is more suitable for monitoring oilseed rape genotypes during flowering stages.
... Over the past decade, Unmanned Aerial Vehicles (UAVs) (drones) have emerged as one of the most popular tools for field-based phenotyping and precision agriculture [25][26][27]. They provide a flexible and easy-to-operate approach to collect high spatial (centimeter level) and temporal imagery for high-throughput crop phenotyping [28]. ...
Full-text available
Biomass is a key biophysical parameter for precision agriculture and plant breeding. Fast, accurate and non-destructive monitoring of biomass enables various applications related to crop growth. In this paper, strawberry dry biomass weight was modeled using 4 canopy geometric parameters (area, average height, volume, standard deviation of height) and 25 spectral variables (5 band original reflectance values and 20 vegetation indices (VIs)) extracted from the Unmanned Aerial Vehicle (UAV) multispectral imagery. Six regression techniques—multiple linear regression (MLR), random forest (RF), support vector machine (SVM), multivariate adaptive regression splines (MARS), eXtreme Gradient Boosting (XGBoost) and artificial neural network (ANN)—were employed and evaluated for biomass prediction. The ANN had the highest accuracy in a five-fold cross-validation, with R2 of 0.89~0.93, RMSE of 7.16~8.98 g and MAE of 5.06~6.29 g. As for the other five models, the addition of VIs increased the R2 from 0.77~0.80 to 0.83~0.86, and reduced the RMSE from 8.89~9.58 to 7.35~8.09 g and the MAE from 6.30~6.70 to 5.25~5.47 g, respectively. Red-edge-related VIs, including the normalized difference red-edge index (NDRE), simple ratio vegetation index red-edge (SRRedEdge), modified simple ratio red-edge (MSRRedEdge) and chlorophyll index red and red-edge (CIred&RE), were the most influential VIs for biomass modeling. In conclusion, the combination of canopy geometric parameters and VIs obtained from the UAV imagery was effective for strawberry dry biomass estimation using machine learning models.
... These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase the yield. Such decisions enables the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity (Tsouros et al., 2019). ...
... Unmanned Aerial Vehicles (UAVs) have been widely used in a variety of real-world applications, such as civil engineering [1], precision agriculture [2], and monitoring in mining areas [3]. One advantage of using UAVs is that they can fly to and land on complex terrains that are more difficult to reach through the ground traverse. ...
Full-text available
Autonomous Unmanned Aerial Vehicle (UAV) landing remains a challenge in uncertain environments, e.g., landing on a mobile ground platform such as an Unmanned Ground Vehicle (UGV) without knowing its motion dynamics. A traditional PID (Proportional, Integral, Derivative) controller is a choice for the UAV landing task, but it suffers the problem of manual parameter tuning, which becomes intractable if the initial landing condition changes or the mobile platform keeps moving. In this paper, we design a novel learning-based controller that integrates a standard PID module with a deep reinforcement learning module, which can automatically optimize the PID parameters for velocity control. In addition, corrective feedback based on heuristics of parameter tuning can speed up the learning process compared with traditional DRL algorithms that are typically time-consuming. In addition, the learned policy makes the UAV landing smooth and fast by allowing the UAV to adjust its speed adaptively according to the dynamics of the environment. We demonstrate the effectiveness of the proposed algorithm in a variety of quadrotor UAV landing tasks with both static and dynamic environmental settings.
... On the other hand, UAVs that monitor crops offer great possibilities for acquiring field data in an easy, fast, and cost-effective way compared to other methods [21]. Among the most popular applications of UAVs in agriculture are weed mapping [22,23], automatic identification and monitoring of plant diseases [24], and early-stage detection [25]. ...
Full-text available
Cuscuta spp. is a weed that infests many crops, causing significant losses. Traditional assessment methods and onsite manual measurements are time consuming and labor intensive. The precise identification of Cuscuta spp. offers a promising solution for implementing sustainable farming systems in order to apply appropriate control tactics. This document comprehensively evaluates a Cuscuta spp. segmentation model based on unmanned aerial vehicle (UAV) images and the U-Net architecture to generate orthomaps with infected areas for better decision making. The experiments were carried out on an arbol pepper (Capsicum annuum Linnaeus) crop with four separate missions for three weeks to identify the evolution of weeds. The study involved the performance of different tests with the input image size, which exceeded 70% of the mean intersection-over-union (MIoU). In addition, the proposal outperformed DeepLabV3+ in terms of prediction time and segmentation rate. On the other hand, the high segmentation rates allowed approximate quantifications of the infestation area ranging from 0.5 to 83 m2. The findings of this study show that the U-Net architecture is robust enough to segment pests and have an overview of the crop.
... ML algorithms have been used to monitor the crop status in many remote sensing applications in agriculture [30][31][32][33]. ML methods attempt to establish a relationship between crop parameters to forecast crop production [34]. ...
Full-text available
Sugarcane white leaf phytoplasma (white leaf disease) in sugarcane crops is caused by a phytoplasma transmitted by leafhopper vectors. White leaf disease (WLD) occurs predominantly in some Asian countries and is a devastating global threat to sugarcane industries, especially Sri Lanka. Therefore, a feasible and an effective approach to precisely monitoring WLD infection is important, especially at the early pre-visual stage. This work presents the first approach on the preliminary detection of sugarcane WLD by using high-resolution multispectral sensors mounted on small unmanned aerial vehicles (UAVs) and supervised machine learning classifiers. The detection pipeline discussed in this paper was validated in a sugarcane field located in Gal-Oya Plantation, Hingurana, Sri Lanka. The pixelwise segmented samples were classified as ground, shadow, healthy plant, early symptom, and severe symptom. Four ML algorithms, namely XGBoost (XGB), random forest (RF), decision tree (DT), and K-nearest neighbors (KNN), were implemented along with different python libraries, vegetation indices (VIs), and five spectral bands to detect the WLD in the sugarcane field. The accuracy rate of 94% was attained in the XGB, RF, and KNN to detect WLD in the field. The top three vegetation indices (VIs) for separating healthy and infected sugarcane crops are modified soil-adjusted vegetation index (MSAVI), normalized difference vegetation index (NDVI), and excess green (ExG) in XGB, RF, and DT, while the best spectral band is red in XGB and RF and green in DT. The results revealed that this technology provides a dependable, more direct, cost-effective, and quick method for detecting WLD.
... Variable-rate applicators (Nawar et al., 2017) and (Alameen et al., 2019). the Internet of Things (IoT) (Stamatiadis et al., 2020) and (Boursianis et al., 2020), geo-positioning systems (Muangprathub et al., 2019) and (Flaco et al., 2019), big data (Kamilaris et al., 2017) and (Bronson et al., 2016) unmanned aerial vehicles (UAVs, drones) (Tsouros et al., 2019), automated systems, and robotics are only a few examples. Smart farming is based on a precise and resource-efficient technique that aims to boost agricultural commodities production efficiency while also improving quality on a long-term basis (Hajjaj et al., 2016) and (Marinoudi et al., 2019). ...
Full-text available
The need for precise, effective, and reliable measurement and monitoring of environmental parameters in greenhouses is critical for crop quality and yield. In the past few years, advanced senor methods garnered considerable study in the agriculture field. Capable and efficient use of intelligent sensors in a variety of activities is optimizing resource use while minimizing human interposition. Therefore, this review article aimed to provide significant knowledge about the detection and diagnosis of environmental parameters in greenhouses and the present state of remote communication utilizing intelligent approaches, as well as providing a broad overview of the field. A wide range of sensors and actuators are used extensively in advanced agricultural facilities like plant factories and greenhouses to monitor and regulate their environmental conditions. Temperature and humidity are the most important variables that affect plant growth. The ideal temperature range for healthy plant development is between 4°C and 30°C. Temperature and humidity sensors are widely used in greenhouses. CO 2 concentration is critical for root growth and respiration. Photosynthesis and other physiological processes need an adequate amount of light and a photoperiod. CO 2 sensors and light sensors are often used to monitor smart facilities. When it comes to nutrition monitoring, electrical conductivity (EC) and pH concentration are crucial factors to measure. The most frequent method of monitoring water quality and nutrient content is using pH sensors. Wireless communication such as ZigBee, LoRa, Bluetooth, WiFi, Sigfox, and GPRS/3G/4G technology is widely used for remote monitoring of the ambient environmental condition. The fast expansion of communication networks and the availability of a broad variety of new distant, proximal, and contact sensors are creating new options for farmers. The advancement of technology creates new opportunities for smart farming, and this review article will assist in the implementation of improved monitoring technologies in smart farming.
Conference Paper
Full-text available
Emerging technologies such as Internet of Things (IoT) can provide significant potential in Precision Agriculture enabling the acquisition of real-time environmental data. IoT devices like Unmanned Aerial Vehicles (UAVs) equipped with cameras, sensors, and GPS receivers can deliver a variety of IoT services and applications related to fields management, by capturing images from great heights. However, there are many issues to be resolved before the effective use of UAVs in the agriculture domain, including the data collection and processing methods. There is still no standardized workflow and processes for most UAV-based applications for Precision Agriculture. In this paper we summarize the data acquisition methods and technologies to acquire images in UAV-based Precision Agriculture and appoint the benefits and drawbacks of each one. We also review popular data analysis methods of remotely sensed imagery and discuss the outcomes of each method and its potential application in the farming operations.
Full-text available
It took some time indeed, but the research evolution and transformations that occurred in the smart agriculture field over the recent years tend to constitute the latter as the main topic of interest in the so-called Internet of Things (IoT) domain. Undoubtedly, our era is characterized by the mass production of huge amounts of data, information and content deriving from many different sources, mostly IoT devices and sensors, but also from environmentalists, agronomists, winemakers, or plain farmers and interested stakeholders themselves. Being an emerging field, only a small part of this rich content has been aggregated so far in digital platforms that serve as cross-domain hubs. The latter offer typically limited usability and accessibility of the actual content itself due to problems dealing with insufficient data and metadata availability, as well as their quality. Over our recent involvement within a precision viticulture environment and in an effort to make the notion of smart agriculture in the winery domain more accessible to and reusable from the general public, we introduce herein the model of an aggregation platform that provides enhanced services and enables human-computer collaboration for agricultural data annotations and enrichment. In principle, the proposed architecture goes beyond existing digital content aggregation platforms by advancing digital data through the combination of artificial intelligence automation and creative user engagement, thus facilitating its accessibility, visibility, and re-use. In particular, by using image and free text analysis methodologies for automatic metadata enrichment, in accordance to the human expertise for enrichment, it offers a cornerstone for future researchers focusing on improving the quality of digital agricultural information analysis and its presentation, thus establishing new ways for its efficient exploitation in a larger scale with benefits both for the agricultural and the consumer domains.
Full-text available
We have developed a simple photogrammetric method to identify heterogeneous areas of irrigated olive groves and vineyard crops using a commercial multispectral camera mounted on an unmanned aerial vehicle (UAV). By comparing NDVI, GNDVI, SAVI, and NDRE vegetation indices, we find that the latter shows irrigation irregularities in an olive grove not discernible with the other indices. This may render the NDRE as particularly useful to identify growth inhomogeneities in crops. Given the fact that few satellite detectors are sensible in the red-edge (RE) band and none with the spatial resolution offered by UAVs, this finding has the potential of turning UAVs into a local farmer’s favourite aid tool.
Full-text available
Among grapevine diseases affecting European vineyards, Flavescence dorée (FD) and Grapevine Trunk Diseases (GTD) are considered the most relevant challenges for viticulture because of the damage they cause to vineyards. Unmanned Aerial Vehicle (UAV) multispectral imagery could be a powerful tool for the automatic detection of symptomatic vines. However, one major difficulty is to discriminate different kinds of diseases leading to similar leaves discoloration as it is the case with FD and GTD for red vine cultivars. The objective of this paper is to evaluate the potentiality of UAV multispectral imagery to separate: symptomatic vines including FD and GTD (Esca and black dead arm) from asymptomatic vines (Case 1) and FD vines from GTD ones (Case 2). The study sites are localized in the Gaillac and Minervois wine production regions (south of France). A set of seven vineyards covering five different red cultivars was studied. Field work was carried out between August and September 2016. In total, 218 asymptomatic vines, 502 FD vines and 199 GTD vines were located with a centimetric precision GPS. UAV multispectral images were acquired with a MicaSense RedEdge® sensor and were processed to ultimately obtain surface reflectance mosaics at 0.10 m ground spatial resolution. In this study, the potentiality of 24 variables (5 spectral bands, 15 vegetation indices and 4 biophysical parameters) are tested. The vegetation indices are selected for their potentiality to detect abnormal vegetation behavior in relation to stress or diseases. Among the biophysical parameters selected, three are directly linked to the leaf pigments content (chlorophyll, carotenoid and anthocyanin). The first step consisted in evaluating the performance of the 24 variables to separate symptomatic vine vegetation (FD or/and GTD) from asymptomatic vine vegetation using the performance indicators from the Receiver Operator Characteristic (ROC) Curve method (i.e., Area Under Curve or AUC, sensibility and specificity). The second step consisted in mapping the symptomatic vines (FD and/or GTD) at the scale of the field using the optimal threshold resulting from the ROC curve. Ultimately, the error between the level of infection predicted by the selected variables (proportion of symptomatic pixels by vine) and observed in the field (proportion of symptomatic leaves by vine) is calculated. The same methodology is applied to the three levels of analysis: by vineyard, by cultivar (Gamay, Fer Servadou) and by berry color (all red cultivars). At the vineyard and cultivar levels, the best variables selected varies. The AUC of the best vegetation indices and biophysical parameters varies from 0.84 to 0.95 for Case 1 and 0.74 to 0.90 for Case 2. At the berry color level, no variable is efficient in discriminating FD vines from GTD ones (Case 2). For Case 1, the best vegetation indices and biophysical parameter are Red Green Index (RGI)/ Green-Red Vegetation Index (GRVI) (based on the green and red spectral bands) and Car (linked to carotenoid content). These variables are more effective in mapping vines with a level of infection greater than 50%. However, at the scale of the field, we observe misclassified pixels linked to the presence of mixed pixels (shade, bare soil, inter-row vegetation and vine vegetation) and other factors of abnormal coloration (e.g., apoplectic vines).
Full-text available
The discussion is about the optimal design outline and examination of an Autonomous agricultural pesticide spraying UAV. Farming techniques have drastically evolved over the last few decades to keep up with the ever-growing demand for food. Among its various applications, the use of drones in farming, called agricultural drones, can help in increasing the yied of crop and to monitor its growth. This type of drone is termed as agricultural drone. These drones can be used to spray fertilizers or pesticides uniformly across the field. Also, the aerial mapping feature will give the farmers a bird eye view of their fields and help them quickly identify presence of pests, crop damages and soil conditions. Hence, our main was to build a market ready agricultural drone and not just a prototype – a drone which is affordable, user friendly, portable and can perform autonomous flight without the use of an operator. This paper gives an overview of an indigenously designed folding quad copter frame with 1300mm diameter with following features namely, 5L tank capacity with remote controlled spray module, 4K camera, lift weight up to maximum of 12kg, advanced autopilot system with high precision GPS for autonomous mission.
Conference Paper
Objective characterization of pain intensity is necessary under certain clinical conditions. The portable electroencephalogram (EEG) is a cost-effective assessment tool and lately, new methods using efficient analysis of related dynamic changes in brain activity in the EEG recordings proved that these can reflect the dynamic changes of pain intensity. In this paper, a novel method for automated assessment of pain intensity using EEG data is presented. EEG recordings from twenty-two (22) healthy volunteers are recorded with the Emotiv EPOC+ using the Cold Pressor Test (CPT) protocol. The relative power of each brain band’s energy for each channel is extracted and the stochastic forest algorithm is employed for discrimination across five classes, depicting the pain intensity. Obtained results in terms of classification accuracy reached high levels (72.7%), which renders the proposed method suitable for automated pain detection and quantification of its intensity.
Image segmentation is a critical and important step in (GEographic) Object-Based Image Analysis (GEOBIA or OBIA). The final feature extraction and classification in OBIA is highly dependent on the quality of image segmentation. Segmentation has been used in remote sensing image processing since the advent of the Landsat-1 satellite. However, after the launch of the high-resolution IKONOS satellite in 1999, the paradigm of image analysis moved from pixel-based to object-based. As a result, the purpose of segmentation has been changed from helping pixel labeling to object identification. Although several articles have reviewed segmentation algorithms , it is unclear if some segmentation algorithms are generally more suited for (GE)OBIA than others. This article has conducted an extensive state-of-the-art survey on OBIA techniques, discussed different segmentation techniques and their applicability to OBIA. Conceptual details of those techniques are explained along with the strengths and weaknesses. The available tools and software packages for segmentation are also summarized. The key challenge in image segmentation is to select optimal parameters and algorithms that can general image objects matching with the meaningful geographic objects. Recent research indicates an apparent movement towards the improvement of segmentation algorithms, aiming at more accurate, automated, and computation-ally efficient techniques.
Remote sensing with Unmanned Aerial Vehicles is a game-changer in precision agriculture. It offers unprecedented spectral, spatial and temporal resolution but can also provide detailed vegetation height data and multiangular observations. In this article, we review the progress of remote sensing with Unmanned Aerial Vehicles in drought stress, weed and pathogen detection, in nutrient status and growth vigour assessment and in yield prediction. To transfer this knowledge to everyday practice of precision agriculture, future research should focus on exploiting the complementarity of hyper- or multispectral with thermal data, on integrating observations into robust transfer or growth models rather than linear regression models and on combining Unmanned Aerial Vehicle products with other spatially explicit information.