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Variable Rate Application of Herbicides for Weed Management in Pre-and Postemergence


Abstract and Figures

With the advent of precision agriculture, it was possible to integrate several technologies to develop the variable rate application (VRA). The use of VRA allows savings in the use of herbicides, better weed control, lower environmental impact and, indirectly, increased crop productivity. There are VRA techniques based on maps and sensors for herbicide application in preemergence (PRE) and postemer-gence (POST). The adoption of the type of system will depend on the investment capacity of the producer, skilled workforce available, and the modality of application. Although it still has some limitations, VRA has been widespread and has been occupying more and more space in chemical management, the tendency in the medium-and long term is that there is a gradual replacement of the conventional method of application. Given the benefits provided by VRA along with the engagement of companies and researchers, there will be constant evolution and improvement of this technology, cheapening the costs of implementation and providing its adoption by an increasing number of producers. Thus, the objective of this chapter was to address an overview of the use of herbicides in VRA for weed management in PRE and POST.
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Variable Rate Application of
Herbicides for Weed Management
in Pre- and Postemergence
Alessandroda Costa Lima and Kassio FerreiraMendes
With the advent of precision agriculture, it was possible to integrate several
technologies to develop the variable rate application (VRA). The use of VRA allows
savings in the use of herbicides, better weed control, lower environmental impact
and, indirectly, increased crop productivity. There are VRA techniques based on
maps and sensors for herbicide application in preemergence (PRE) and postemer-
gence (POST). The adoption of the type of system will depend on the investment
capacity of the producer, skilled workforce available, and the modality of applica-
tion. Although it still has some limitations, VRA has been widespread and has been
occupying more and more space in chemical management, the tendency in the
medium- and long term is that there is a gradual replacement of the conventional
method of application. Given the benefits provided by VRA along with the engage-
ment of companies and researchers, there will be constant evolution and improve-
ment of this technology, cheapening the costs of implementation and providing its
adoption by an increasing number of producers. Thus, the objective of this chapter
was to address an overview of the use of herbicides in VRA for weed management in
Keywords: VRA, precision agriculture, chemical control, automation
. Introduction
The growing demand for food and the limitation of territorial expansion of
agricultural areas direct agriculture toward an increasing intensification with the
rational use of resources and maximization of production [1]. For 2050, the world
population is estimated at 9 billion people; this represents a need for an increase in
food production around 70 to 100% that can be achieved if more efficient cultiva-
tion techniques are adopted with fewer impacts on the environment [2]. For this
to be possible, it is necessary to have knowledge and control of the variables that
interfere in the costs of production and productivity of crops. In this sense, preci-
sion agriculture is a tool that makes it possible to meet these needs.
Precision agriculture comprises a set of technologies that combines sensors,
information systems, improved machinery, and informed management to optimize
production, considering variability and uncertainties in agricultural systems [3].
Pests - Classification, Management and Practical Approaches
This modern agriculture starts from the concept that an area of production is
not homogeneous, that is, it has great variation. Thus, it is not appropriate to use
agricultural inputs and management techniques equally for areas that have different
characteristics. The aggregated knowledge throughout history helps to scientifically
explain the variability observed and offers paths to localized management with
more technique and rigor [4].
This new approach mainly benefits from the emergence and convergence of
various technologies, including the global positioning system (GPS), geographic
information system (GIS), microcomputers, control automation, remote sens-
ing, mobile computing, advanced information processing, and telecommunica-
tions [5]. With these technologies, it is possible to analyze spatial variability,
through data collection, information management, application of inputs at
varying rate, and, finally, the economic and environmental evaluation of the
results [6].
Precision agriculture allowed to perform not only the mapping of the physi-
cochemical properties of the soil, application of fertilizers in a localized way, pest
monitoring, harvesting and post-harvest operations, among others [3] but also the
mapping and control of weeds, with localized sprays through mapping equipment
or real-time systems and thus rationalize the use of pesticides and also minimize
damage to the environment. Thus, the objective of this chapter was to address
an overview of the use of herbicides in variable rate application (VRA) for weed
management in PRE and POST.
. Variable rate application (VRA) of herbicides
Weed control with herbicides makes up much of the production costs of a
crop. In conventional agriculture, herbicide doses are recommended for large
areas, without considering many aspects of spatial and temporal variation.
When the use of herbicides is made at a fixed rate, economic losses occur directly
and indirectly, both due to the above—what is necessary for herbicides and for
possible control failures that decrease productivity. In addition, environmental
contamination may occur by leaching herbicides into groundwater and rivers.
To fix these problems, it is necessary to use the precision agriculture tools and
implement a VRA system [7].
VRA refers to the application of herbicides based on area, location, and soil
conditions, among other characteristics. Important characteristics such as the varia-
tion in infestation and weed density in the application of herbicides in POST and
in the sorption capacity that the soil exerts in the application of herbicides in PRE
are considered in this system. This allows us to control weeds more efficiently and
reduce environmental risks, as there are no applications of underdoses or overdoses.
This technology works by integrating a variable rate control system with the sprayer
for herbicide application [8, 9].
VRA systems can be different in many ways, but have components in common;
the basic system deployment consists of five components that are represented in
Figure : GPS receiver for location and orientation of the machinery at the time of
application, a computer that will perform the data processing, a software capable
of relating the data collected in the area and determine the dose to be applied, in
addition to controllers that will be responsible for changing the flow and pressure of
the spray syrup [7].
The application at a varied rate can be fundamentally based on maps or sensors
(Table). Such methodologies require specific resources that differ greatly from
each other.
Variable Rate Application of Herbicides for Weed Management in Pre- and Postemergence
. Map-based variable rate application (VRA)
Application maps of specific areas are generated by analyzing previous georefer-
enced samples of soil or plants of the area to be managed. Due to the need to collect
many samples to create a representative map of the area, the costs of analysis tend
to increase with this method and need more time to get ready. The map-based sys-
tem is highly dependent on GPS and differential global positioning system (DGPS),
as it is necessary to cross-reference the coordinates of the samples collected with the
Figure 1.
Main components of a variable rate spraying system (spray rate controller, computer and software, GPS
receiver, and control valve). Source: adapted from Grisso etal. [7].
Parameter Map based Sensor based
Methodology Grid sampling—lab analyses—site-
specific maps and the use of variable
rate applicator
Real-time sensors—feedback control
measures and the use of variable rate
GPS/DGPS Very much required Not necessary
Laboratory analysis
(plant and soil)
Required Not required
Mapping Required May not required
Time consumption More Less
Limitations Cost of soil testis and analysis limit
the usage
Lack of sufficient sensors for getting crop
and soil information
Operation Difficult Easy
Skills Required Required
Sampling unit 2 to 3 acres Individual spot
Relevance Popular in developing countries Popular in developed countries
Source: Ahmad and Mahdi [].
Table 1.
Comparison of the application in varied rate based on maps and sensors.
Pests - Classification, Management and Practical Approaches
coordinate occupied by the machinery at the time of application. Thus, the opera-
tional difficulty of map-based systems is greater.
Although it has some disadvantages referring to operating costs and complexity,
the map method is very efficient when used correctly and with accurate equip-
ment. Figure  shows a mapping of weed distribution in a given area and correlated
with the required amount of herbicide needed to control weeds according to their
density. The result of this crossing of information is a varied rate application map.
In the area, there were infestations ranging from 0 to >30 plants m−2; so, it is not
necessary to apply the same dose at all levels of infestations [11]. Areas with higher
infestation will receive more herbicide than areas with low infestation. In the
specific case, the volume of syrup varies from 100 to 250Lha−1, which corresponds
to a variation of 150%. If the volume of syrup was kept constant, there would
certainly be herbicide wasting due to excess or lack in certain places. In the example
of Figure , the VRA allowed uniform yield of the crop that was implanted, reduced
environmental impacts, and provided savings of 29% in the amount of herbicide.
. Sensor-based variable rate application (VRA)
Data collection of weed presence and processing in sensor-based VRA are made
fractions of seconds before herbicide application, avoiding the need to generate a
previous map of the area. Sensor-based systems have the ability to vary application
rate without any mapping or prior data collection. Sensors measure in real time
the desired properties while they are in motion. The measurements made by the
system are processed immediately and sent to the controller who will perform the
application at a varied rate.
The use of sensors does not necessarily require the use of a positioning sys-
tem, map generation, or extensive data analysis before making the VRA. Thus,
it is an easier-to-use system, consumes less time, and has greater accuracy when
compared to the map-based method. Its current limitation is related to the state of
Figure 2.
Weed density map (left) and variable rate application (VRA) of herbicide (right). Source: Carrara etal. [11].
Variable Rate Application of Herbicides for Weed Management in Pre- and Postemergence
the development of sensors and algorithms with sufficient accuracy to collect and
process more detailed information of plants and soil.
In Figure , there is an example of this type of method, where an optical sensor
along with an infrared light source is implanted in the machinery spray bar. This set
will be responsible for identifying weeds in the field by reflecting the green color of
the leaves and indicating to the controller which sites will be necessary to carry out
herbicide application.
. Variable rate application (VRA) in preemergence (PRE)
The objective of an herbicide application in preemergency is to manage weeds
that have not yet germinated, and the herbicide application is made directly in
the soil so that as soon as the seeds/propagules germinate, they can absorb the
herbicide. But for this to occur, the herbicide must be bioavailable in the soil
solution. The application of herbicides in PRE follows different destinations due
to the herbicide-soil interactions regulated by physical, chemical, and biological
processes [12].
The efficiency of chemical control is associated with several factors that will
determine whether the herbicides will be in the soil solution, thus being absorbed
by the vegetables; leached, including groundwater; transported by the process of
erosion or runoff; and volatilized [13]. In addition, they can be sorbed by soil col-
loids, thus becoming unavailable to plants.
The variability of soil properties can cause a differential sorption of herbicides,
which, in turn, reflects on the different availability of the herbicide in the soil
solution, and may generate variation in weed control [14, 15], especially in large
cultivated areas where herbicide application is made in a single dose. Thus, the VRA
for herbicides in PRE should obtain the main data related to herbicide retention
and availability in the soil solution in order to have the correct deposition of the
Herbicide sorption is dependent on the interaction of the molecules of the
product with the soil, and the process is influenced by the management and climate,
mainly soil temperature and humidity. The main physicochemical characteristics
of the soil that affect herbicide sorption are organic matter (OM), texture, pH, and
cation exchange capacity (CEC). Regarding the herbicide physicochemical charac-
teristics, water solubility (Sw), acid/base dissociation constant (pKa/pKb), octanol-
water coefficient (Kow) half-life degradation time (DT50), and mainly sorption/
desorption coefficient (Kd) [10].
Each herbicide will have a type of behavior in different soil classes. Therefore,
to perform VRA in PRE, a previous study of sorption and desorption of the her-
bicide molecule in the soil type of interest is necessary for the VRA to be efficient.
Currently, the technique for sorption and desorption studies of herbicides most
Figure 3.
Acting of an optical sensor in the control of spray nozzles. Source: Grisso etal. [7].
Pests - Classification, Management and Practical Approaches
used and mentioned in the literature is liquid or gas chromatography. The chro-
matographic technique can identify individual compounds quantitatively and
qualitatively even at small concentrations, being very useful to identify herbicide
concentrations in a solution. However, sorption and desorption studies can also
be performed with radioisotopes (14C and 3H), in addition to bioassay with plant
species sensitive to herbicide [1618].
Data on soil characteristics are difficult to obtain with sensors in the field; so,
most methods for applying herbicides in PRE are based on the generation of maps
from laboratory analyses of soil samples. From soil information and herbicide sorp-
tion and desorption, a map is interpolated with application information at varying
rate [10].
A study of sorption and desorption of the herbicide cyanazine was carried out
in different soils (Table ). From this study, the herbicide application was recom-
mended based on soil texture and OM content. Herbicide doses increase as clay and
OM contents increases.
Thus, for the application of PRE, herbicide is necessary to analyze the soils
physicochemical properties to interpolate the VRA map. Figure  contains the VRA
map in which the different colors represent doses of herbicide to be applied. In this
study [15], the use of VRA in PRE decreased the total amount of herbicide by 13%.
In addition to the herbicide economy, it should be considered that other benefits are
obtained such as better efficiency in weed control, which can help in an increase in
productivity, in addition to reducing environmental risks.
Laboratory analyses of soil characteristics are very efficient and accurate. The
major disadvantage is the high costs of soil analysis, compromising its use for very
large areas. An alternative to map the soil characteristics responsible for herbicide
retention and availability without the need for labor collection and analysis is the
use of electrical conductivity sensors in the field. The mapping of electrical con-
ductivity with the aid of GPS is a simple tool, which is used to estimate soil texture,
in addition to other properties [19]. This quantification considers the clay and ion
contents in the soil, resulting in significant correlations [20].
An example of a sensor used to measure electrical conductivity is the VARIS
3100 platform (Figure ). The operation of the equipment consists in the emission
of an electric current by two intermediate discs, while two internal discs and two
external discs detect the potential difference, which occurs in the electromagnetic
field generated in the soil resulting from the applied electric current [21]. The
spacing between the discs is calculated so that values of electrical conductivity are
measured at depths of 0–0.30m and 0–0.90m. Data obtained in the field can be
Soil texture Soil organic matter content ()
<. . . . . .
Sand 0.60 0.75 1.25 1.50 1.75 2.00
Sandy loam 0.75 1.25 1.50 1.75 2.00 2.25
Loam, silty loam, silt 1.25 1.50 1.75 2.00 2.25 2.50
Sand clay loam, clay loam, and
silty clay loam
1.50 1.75 2.00 2.25 2.50 2.75
Sandy clay, silty clay, and clay 1.75 2.00 2.25 2.50 3.75 3.00
Peat or muck Not recommended
Source: Mohammadzamani etal. [].
Table 2.
Recommendation of doses of cyanazine (Lha−1) according to the texture and organic matter content of the soil.
Variable Rate Application of Herbicides for Weed Management in Pre- and Postemergence
visualized, recorded, and exported, since the sensor has a data logger. Data collec-
tion occurs with moving equipment, coupled to a tractor and the whole process can
be georeferenced by a global navigation satellite system (GNSS) receiver. According
to the manufacturer’s instructions, two tests must be performed to confirm the cor-
rect calibration of the equipment. After data collection, the electrical conductivity
is correlated with the clay content for the generation of a textural map.
Figure 4.
Two-dimensional (I) and three-dimensional (II) maps for variable rate application (VRA) of cyanazine.
Source: Mohammadzamani etal. [15].
Figure 5.
Veris Platform® 3100 to measure the electrical conductivity of the soil. ESALQ/USP, Piracicaba, SP, Brazil.
Pests - Classification, Management and Practical Approaches
Studies show that the electrical conductivity measured by contact sensor
adequately reflects the variation in clay contents of the studied soil, being efficient
to generate soil texture maps, including in no-tillage areas [21]. Figure  shows
a conductivity map elaborated with the data collected in VARIS 3100; the lowest
conductivity values correlated with lower clay contents. However, for high clay
contents, the model was less efficient. Thus, the mapping of electrical conductivity
can be a useful tool in the design of more homogeneous areas, which present more
similar soil conditions.
Considering that other factors such as moisture, salt concentration, and total
carbon remain in the same conditions, soils with higher clay contents conduct more
electricity than those with sandier texture. However, these factors may vary and
affect the correlation between electrical conductivity and soil texture. Therefore, as
the electrical conductivity method does not quantify the CEC and soil OM contents,
the use of the same may have reduced efficiency in some situations.
There are companies on the market that provide the VRA service for herbicides
in PRE, one of which is APagri which has the HTV® method which consists of
a process developed and patented for the application of herbicides in PRE at the
varied rate based on maps (Figure ), that considers the clay, OM, and CEC content
of the soil [22]. The objective is to adjust the dose according to the soil ability to
retain each type of herbicide so that the final concentration in the soil solution is
equal regardless of the position in space.
Due to technological limitations, there is still no VRA available on the market
for PRE herbicides based on sensors that read, process, and apply the herbicide
without the need for the generation of maps. One of the great challenges of this
market is precisely to eliminate this stage, in view of the costs of generating
the maps.
Figure 6.
Interpolated map of electrical conductivity measured with mobile contact measurement equipment. Source:
Machado etal. [21].
Variable Rate Application of Herbicides for Weed Management in Pre- and Postemergence
. Variable rate application (VRA) in postemergence (POST)
The purpose of a POST application is to control weeds that have already emerged
in the field. Thus, the target of the application is the aerial part of the plant species.
For the VRA to be used in POST, it is necessary that the system has information
about the weed population in the area. This information can be collected by the map-
based and sensor-based systems. Therefore, both methods can be used VRA in POST.
. Map based: weed mapping
The literature mentions several methodologies for weed mapping, where each
one has its specificity. Some have processing algorithms to differentiate monocot
and eudicot plants [23]. Others use machine learning with deep neural network to
identify weeds [24, 25]. However, all have the principle based on the quantitative
and qualitative identification of the infested area, generation of the recommenda-
tion map, and integration with the VRA system.
Remote sensing is generally considered one of the most important technologies
for precision agriculture. This technology can be used in weed mapping. Remote
sensing can monitor many crops and vegetation parameters through images at
various wavelengths. Images can be acquired by satellites, manned aircrafts, or
unmanned aerial vehicles (UAV). 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 neces-
sary images [26]. 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. 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 [27]. In addition,
UAVs are a lot simpler to use and also cheaper than manned aircrafts. Moreover,
Figure 7.
Variable rate application (VRA) map drawn up with the system HTV®. Source: APagri [22].
Pests - Classification, Management and Practical Approaches
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
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 herbicide are needed in
different rates [28].
A variety of different types of sensors can be used in an agricultural UAV
depending on the different vegetation parameters that should be monitored. The
main sensors used that meet the limitations mentioned above are: visible light sen-
sors, red, green, and blue (RGB) color model, multispectral sensors, hyperspectral
sensors, and thermal sensors. RGB are relatively low cost compared to the other
types and can acquire high resolution images, are easy to use and operate, and are
lightweight [29]. In addition, the information acquired requires simple processing.
However, they are inadequate for analyzing a lot of vegetation parameters that
require spectral information in the non-visible spectrum. Thus, commonly are used
with the other types of sensors.
Multispectral or hyperspectral imaging sensors 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 plants. This information is important to determinate which
weed species are in the field [30]. 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 preprocessing methods
in order to extract useful information from the captured images. The preprocessing
procedure of spectral images often contains the radiometric calibration, 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 thou-
sands of bands, but in a narrower bandwidth. Multispectral sensors are used much
more frequently than hyperspectral sensors due to their lower cost, but hyperspec-
tral technology appears to have a lot of potential and is considered the future trend
for crop phenotyping research. 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. This type of sensors is used for very
specific applications (irrigation management). As a result, they are not frequently
used in remotely piloted aircraft applications of UAV systems that focus on moni-
toring other characteristics of the crops [2628].
UAVs can acquire information for various features of the cultivated field by
using specialized sensors. However, as mentioned above, there is still no stan-
dardized workflow or well-established techniques to analyze and visualize the
information acquired. The most commonly used image processing methods to
analyze UAV imagery for weed mapping are photogrammetry and machine learn-
ing. Photogrammetry regards the accurate reconstruction of a scene or an object
from several overlapping pictures. Photogrammetric techniques are very commonly
used in all types of applications as they are also required to create vegetation indices
maps. However, photogrammetric techniques are in most cases used to compliment
other types of data processing methods [29].
Machine learning is used to process the data acquired, for prediction and/or
identification purposes, with great results in many domains. Machine learning
techniques are often applied in precision agriculture to exploit the information from
the large amount of data acquired by the UAVs. Machine learning is able to estimate
some parameters regarding the crop growth rate, detect diseases, or even identify/
discriminate objects in the images [30].
Variable Rate Application of Herbicides for Weed Management in Pre- and Postemergence
The most promising technique for weed mapping is machine learning, especially
those based on object-based image analysis (OBIA). Weed detection with UAVs
based on object-based image analysis appears to be at an advanced stage and can be
used for specific weed management.
In an example of weed mapping performed on corn, an UAV coupled with a
six-band multispectral camera (visible and near infrared range) was used to map
the area (Figure ).
After mapping, an OBIA procedure processes the data and generates a classifica-
tion of weed, crop, and bare soil (Figure ).
The identification and delimitation of the weeds allows generating maps show-
ing the infestation level (Figure ). The information of this map can be integrated
into VRA system and used for POST herbicide application. In this study, weed-free
areas corresponded to 23% and areas with low infestation (<5% of weeds) to 47%
of the total, indicating a high potential to reduce herbicide application [31].
Figure 8.
Unmanned aerial vehicle (UAV) used for weed mapping. Source: Peña [31].
Figure 9.
Partial view of the outputs of the object-based image analysis (OBIA) procedure: classified image with crop,
weeds, and bare soil. Source: Peña [31].
Pests - Classification, Management and Practical Approaches
When data collection and map generation is done for POST herbicide applica-
tion, the whole process must be done as quickly as possible because in a few days,
the weed dynamics can be changed and infestation levels can increase, making the
recommendation map obsolete.
. Sensor based: real time
When applying POST herbicides using a real-time based sensor method,
there is no need of a prior area mapping. Spraying is based on sensors attached to
the sprayer responsible for detecting weeds and applying the herbicide dose. In
Figure , there is a basic model for this application type.
In real-time-based sensor method, the optical sensor collects data that are
immediately processed by the computer, where the locations and doses to be applied
are determined. This information is sent as a command to a nozzle controller. In
Figure 11.
Sensor-based VRA model for POST herbicide application. The system includes a multiple-camera vision
system, a ground speed sensor, and nozzle controller. Source: Tian [32].
Figure 10.
Partial view of the outputs of the OBIA procedure: weed coverage map showing three levels of infestation (low,
moderate, and high), crop rows, and weed-free zones. Source: Peña [31].
Variable Rate Application of Herbicides for Weed Management in Pre- and Postemergence
the spray boom, each nozzle is opened or closed by a solenoid valve connected to
the controller, so that the nozzle controller can vary the flow applied or the total
opening and closing of each nozzle. The presence of a GPS system is not essential
for the operation of system, but it does provide guidance to the machinery opera-
tor and is useful for recording sprayed areas. The database can be used to improve
weed control in the following years, especially for perennial species that reproduce
vegetatively, in view of their stability in spatial distribution [33].
Depending on the model, the system components can vary in several character-
istics. Optical sensors can be multispectral or infrared. The software can be com-
posed from algorithms that can only identify green plants to deep neural networks
that have the ability to learn to differentiate weed species. The controller can only
open or close a spray nozzle or it can even coordinate the herbicide mixture and
control the alternating flow of dozens of nozzles. The variations are huge, and the
more research evolves, the greater the accuracy and reliability of the VRA [7, 32, 33].
Commercially, some companies have consolidated in recent years with VRA
systems for application in POST with sensor methods based on real time. Among
the most widespread are Weed-it and WeedSeeker.
.. WEED-IT
WEED-IT is a high-performance localized spraying system, formed by chlo-
rophyll detection sensors and extremely fast valves to guarantee application only
where necessary (Figure ). The system is based on the principle of chlorophyll
fluorescence: a light source in the set of sensors emits a constant beam of infrared
light that is absorbed by the plants chlorophyll and re-emits near infrared light
(NIR). This emission is detected by the sensors by performing 40,000 readings per
second and capture even the lowest chlorophyll fluorescence emissions activating
the nozzle set only on the identified weeds, applying only what is necessary, accord-
ing to the size of the plant (Figure ) [34].
The system can be installed in self-propelled and trailed sprayers, operating at
speeds of up to 25kmh−1. In the spray bar, each sensor is responsible for covering
1m in width and independently activating up to five nozzles with an opening time
of 1 ms. Its valves have a system for modulating the width of the energy pulses that
generate extremely rapid interruptions in the spray nozzle outlet; the greater the
number of interruptions, the lower the applied dose (Figure ) [34].
In curves or maneuvers, the speed on the outside of the bar is greater than the
inside; the system is able to correct the flow along the bar to apply equal amounts of
herbicide even in curves or with speed variations (Figure ).
Figure 12.
WEED-IT performing application with weed detection by infrared sensors. Source: SmartSensing [34].
Pests - Classification, Management and Practical Approaches
The system has an important limitation. As the sensor is based only on the
chlorophyll fluorescence, the system is not able to differentiate the crop and weeds,
both are interpreted as living plants. Therefore, it is necessary to be careful with the
application of nonselective herbicides in POST, as the crop will certainly be sprayed
together with weeds.
.. WeedSeeker
The WeedSeeker is another widely used commercial system that has the same
WEED-IT operate principle, where a sensor emits red and near infrared light and a
Figure 14.
WEED-IT valve system modulation. Source: SmartSensing [34].
Figure 15.
Differential flow compensation system. Source: SmartSensing [34].
Figure 13.
WEED-IT operating system. Source: SmartSensing [34].
Variable Rate Application of Herbicides for Weed Management in Pre- and Postemergence
photodiode detects the intensity of the reflected light (Figure ). Afterward, the
reading is converted into a command to apply or not the herbicide (Figure ) [35].
The system can be operated at speeds of 20kmh−1 installed in trailed and self-pro-
pelled sprayers. Nozzles are opened by solenoid valves connected to a central control-
ler. The sensor spacing is 38cm, and each sensor controls one spray nozzle. Although
WEED-IT and WeedSeeker have many similarities, some aspects differentiate the two
systems. The WeedSeeker requires a prior calibration of the sensors in order for the
system to operate correctly, while the WEED-IT does not require any calibration [35].
As both systems have own light source, they can perform applications at night.
Both are highly efficient systems that fulfill your proposals well. There are few
studies that compare two systems. In a study focused on methods of comparing
commercial precision spraying technology, the authors compared the efficiency
and precision of WEED-IT and WeedSeeker and however, this comparison was
only undertaken with a 0.16ha−1. In this way, WEED-IT can be more efficient for
identifying newly emerged plants [35].
Figure 16.
How a WeedSeeker sensor works. Source: Trimble Agriculture [35].
Figure 17.
WeedSeeker spray nozzles applying herbicide only to weeds. Source: Trimble Agriculture [35].
Pests - Classification, Management and Practical Approaches
. Robots for variable rate application (VRA) in postemergence (POST)
Use of autonomous agricultural robots has an interesting potential as a valu-
able technological tool for precision agriculture, bringing the advantage of being
able to make use of the various theories in robotic control, already grounded and
consolidated for applications in several other areas [36]. The main characteristic
that differentiates an agricultural robot from a simple machine or implement is
the freedom degree and autonomy possessed by the robot, including the need for
human operation. As agricultural robots must have a high degree of autonomy, tools
are necessary so that they can distinguish targets and culture in the field, as well as
to orient themselves spatially during movement. The way the distinction is made
is through sensors. The main sensors used are GPS real-time kinematic (RTK),
cameras, gyroscope, strobe, and proximity [36–38].
The recent trend in the development of mobile robots and autonomous vehicles
to perform specific tasks is mainly guided by improving efficiency and leading to
operating gains (reduces soil compaction, absence of operator) when compared to
the use of large machines [39]. Although much smaller than conventional agri-
cultural machines, they can act cooperatively and perform tasks such as spraying
pesticides that pose risks to humans [40]. Sprayers coupled to robots can direct
spray nozzles to weeds through a computer vision system. Some models use photo-
voltaic plates to take advantage of solar energy and reduce or eliminate fossil fuel
consumption. With all the advantages related to the autonomy and efficiency of
agricultural robots, the farmer can direct his time and efforts toward other agricul-
tural activities such as negotiating sales contracts and making investment decisions.
Robots provide precision spraying, realizing the collection of weed position and
incidence information in real time and transmitting them to an atomizer or sprayer
that regulates the need for more or less herbicide. Despite having many advantages,
the use of robots still has points to be improved, among them are the following:
a. Low autonomy compared to conventional machinery
b. Operational limitations in adverse field conditions
c. State of technological development
The current limitations present in agricultural robots are being resolved with the
evolution of the available technology, since the optimization of sensors and algo-
rithms occurs constantly, while in a few years, these limitations can be overcome.
Artificial intelligence used in agricultural robots is a way of recognizing patterns
so that the computer can identify weeds, pests, disease symptoms, nutritional
deficiency, degree of maturation, and cut-off point in the harvest, among others.
In a simplified way, artificial intelligence consists of providing the machine with as
many examples of situations and decisions as possible, whether historical or simu-
lated based on existing knowledge, so that when faced with similar circumstances,
it can make a decision [37, 38]. There are several examples of robots currently used
in VRA, two of which are described below.
.. Robot for Intelligent Perception and Precision Application (RIPPA)
The Robot for Intelligent Perception and Precision Application (RIPPA) is an
autonomous system developed by the University of Sydney for detecting weeds and
applying herbicides in microdoses (Figure ) [41].
Variable Rate Application of Herbicides for Weed Management in Pre- and Postemergence
The system has infrared and monochromatic sensors working with neural net-
works that make it possible to differentiate between crop and weed. In this way, the
application and efficiency of the system are much more accurate. Due to its small
size and high precision, the system is suitable for smaller areas, such as horticul-
ture. The RIPPA is powered by solar energy through solar panels on the top of the
machine. The system also has a sensor for collecting moisture and soil temperature,
which makes data collection a little more complete, generating .XLS files so that the
producer can create a database with information from his area. Table contains
some additional information from RIPPA [41, 42].
.. BoniRob
With characteristics similar to RIPPA, BoniRob (Figure ) was developed by
the partnership between the companies BOSCH and AMAZONE, in Germany. It
is slightly larger than RIPPA, but it is still smaller than a small car and is capable of
applying localized pesticides, collecting soil samples, and analyzing to obtain real-
time characteristics such as pH and phosphorus levels [43].
Figure 18.
RIPPA robot model. Source: Sukkarieh [42].
Specification description Valu e
Track width 1.52m
Max crop height 0.6m (adjustable)
IP rating IP65
Mass (no payload) Approx. 275kg
Max payload 100kg at max operating grade (12°)
Charge-time from empty > 2hours (dependent on charger)
Idle discharge time (no solar) 43hours
Driving discharge time (0.5m/s, no solar, no payload) 21.5hours
Max area traversed per charge (no solar) 8 hectares (~10hours at 1.6m/s)
Source: Sukkarieh [].
Table 3.
Specification description of RIPPA.
Pests - Classification, Management and Practical Approaches
To ensure its operation, BoniRob has a set of cameras and sensors (Figure )
that work as follows: camera “a” points to the top of the plant with the function
of detecting and locating it; camera “b” is positioned to obtain a side view of the
plants looking for overlapping plants. Inc,” we have a set of light-emitting diodes
(LEDs) that are responsible for emitting red and infrared light to assist the cameras
when capturing photos. There is also a third camera, which has a high frame rate
and resolution (higher than cameras “a” and “b”) attached to the sensor responsible
for spraying. This sensor, to maintain accuracy in capturing images and also during
the application of pesticides, has a strobe that allows, even with variations in the
terrain, the camera and the spray tip to remain in the desired position [44].
When it comes to artificial intelligence, based on the culture and species of
plants you want to work with and control, machine learning takes place through the
developed algorithm and is trained based on obtaining images (millions of them)
that allow you to characterize the plants according to their shape, size, and color,
among other parameters, allowing them to be recognized and distinguished in the
face of a possible action such as spraying it or not [44].
As mentioned earlier, the versatility of agricultural robots is essential, since in
the field, the conditions are highly heterogeneous. For this reason, many of these
machines allow the installation of modules that perform different functions. In the
case of BoniRob, we have a module for phenotypic recognition, a penetrometer,
and a localized spraying mode already developed, but there are numerous other
possibilities for adaptation and creation based on the particular characteristics
to which the use of the machine is intended [44]. Other models of agricultural
robots are being developed and gradually made available on the market. A good
example is Ecorobotix (Figure ), which applies microdoses of herbicide and
Figure 19.
BoniRob model. Source: Sellmann etal. [44].
Figure 20.
BoniRob components. Source: Sellmann etal. [44].
Variable Rate Application of Herbicides for Weed Management in Pre- and Postemergence
works completely autonomously. Its use is recommended after an initial standard
application of herbicide, in order to replace subsequent applications and thus save
an important amount of herbicide [45].
The market robots for herbicide application are still at the beginning of its
development and consolidation, but it represents a new way of interacting with
agriculture, revolutionizing the relationship between man and the field.
. Variable rate controllers
In order for the VRA to happen efficiently, it is necessary to have a high control
in the spraying system responsible for the application of the herbicide. Controllers
can act by modifying the pressure at the spray nozzles, or they can change the
herbicide concentrations and the water flow in real time. Some of these systems
are more complex, while others are simpler. The main controllers will be discussed
. Flow-based control systems
In flow-based control system, only the flow and pressure are changed. There is
no manipulation of the herbicide concentrations. The system has a flow meter, a
speed sensor on the ground, and a servovalve with an electronic controller to apply
the desired rate of the tank mixture (Figure ). A microprocessor uses informa-
tion about the width of the sprayer and the recommendation of the spray volume
per hectare to calculate the flow rate appropriate for the current speed of the soil.
The servovalve is opened or closed until equal amounts of herbicides are applied
regardless of the speed of the machinery. If the controller can be integrated with a
recommendation map system, a VRA can be done. These systems have the advan-
tage of being reasonably simple. They are also able to make rate changes across the
bar in 3 to 5 seconds [7, 46].
Depending on the speed, problems with drift can occur, as the flow sensor and
servovalve control the flow of the tank mixture, allowing variable pressure rates to
be delivered to the spray nozzles. Thus, high speeds can represent an increase in the
pressure of the nozzles and a consequent decrease in the droplet spectrum.
Figure 21.
Ecorobotix components. (1) Photovoltaic panels, (2) camera and artificial vision for steering and detection, (3)
navigation by GPS and sensors, (4) electrical drive system, (5) rapid robotic arms with sprayers, and (6) tanks
for two different products. Source: Ecorobotix [45].
Pests - Classification, Management and Practical Approaches
. Chemical direct injection systems
In this system, the mixture is prepared with direct injection of the chemical in a
flow of water. This system (Figure ) uses a controller and a pump to manage the
chemical injection rate instead of the flow rate of a tank mix [46]. The water flow
rate is constant and the herbicide injection rate is varied to accommodate changes in
soil speed or changes in the prescribed rate.
With the chemical injection, there is no leftover mixture and the direct contact
of the operator with toxic products is reduced [10]. The system allows you to control
the desired size and spectrum of droplets, since the variation of the application rate
does not depend on the flow and pressure on the spray nozzles. Its main disad-
vantage is the long transport delay between the chemical injection pump and the
discharge nozzles at the ends of the boom.
Figure 23.
VRA spraying system that incorporates chemical injection technology. Source: Grisso etal. [7].
Figure 22.
VRA spraying system that is a flow-based system of application rate. Source: Grisso etal. [7].
Variable Rate Application of Herbicides for Weed Management in Pre- and Postemergence
. Direct chemical injection with carrier control
In this system, there is control of the herbicide injection rate and water flow rate
to respond to changes in speed or application rate. A control circuit manages the
injection pump, while a second controller operates a servovalve to provide a cor-
responding water flow (Figure ). Such a system provides a mixture of constant
concentration. The system can have many of the advantages of the previous two
systems. There is no leftover mixing; the operator is not exposed to chemicals in
the tank mixing process; the variation from one rate to another occurs quickly. The
disadvantages include related to the complex system, higher initial costs, problem
in delivering variable rates of liquid through in the nozzle spray, and modulated
spraying nozzle control systems [10, 46].
. Conclusions
The variable rate application (VRA) of herbicides has great potential for use
in agriculture because it allows better control of weeds at lower costs and reduc-
tion in the use of inputs and environmental contamination. The main techniques
available are based on the generation of application maps and the use of sensors
in real time to identify weed infestations, which can be used in the preemergence
(PRE) and postemergence (POST) of weeds. Both modalities are equally important
in integrated weed management. VRA systems still require relatively high invest-
ment, restricting their use. The constant improvement of the VRA should further
increase its benefits and reduce the costs of adopting the system, allowing its use
by more farmers. The use of precision agriculture in farming systems is a path of no
return, in view of the conjuncture of food production needs and scarcity of natural
resources. Thus, VRA tends to be used more and more frequently until possible
complete replacement of the conventional way of using herbicides in agriculture.
Figure 24.
A direct chemical injection system with carrier control. Source: Rashidi and Mohammadzamani [46].
Pests - Classification, Management and Practical Approaches
Author details
Alessandroda Costa Lima and Kassio FerreiraMendes*
Department of Agronomy, Federal University of Viçosa, Viçosa,MG, Brazil
*Address all correspondence to:
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms
of the Creative Commons Attribution License (
by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly cited.
Variable Rate Application of Herbicides for Weed Management in Pre- and Postemergence
[1] FoleyJA, RamankuttyN,
BraumanKA, CassidyES, GerberJS,
JohnstonM, etal. Solutions
for a cultivated planet. Nature.
2011;(7369):337-342. DOI: 10.1038/
[2] GodfrayHCJ, BeddingtonJR,
CruteIR, HaddadL, LawrenceD,
MuirJF, etal. Food security: The
challenge to feeding 9 billion people.
Science. 2010;(5957):812-818. DOI:
[3] GeberrsR, AdamchukVI. Precision
agriculture and food security. Science.
2010;(5967):828-831. DOI: 10.1126/
[4] MolinJP, AmaralLR, ColaçoAF.
Agricultura de precisão. Oficina de
Textos: São Paulo; 2015. p. 223
[5] GibbonsG. Turning a Farm Art into
Science - An Overview of Precision
Farming [Internet]. 2000. Available
from: http://www.precisionfarming.
com [Accessed: 12 May 2020]
[6] Soares FilhoR, CunhaJPAR.
Agricultura de precisão: particularidades
de sua adoção no sudeste de Goiás –
Brasil. Engenharia Agrícola. 2015;(4):
689-698. DOI: 10.1590/1809-4430-Eng.
[7] GrissoR, AlleyM, ThomasonW,
HolshouserD, RobersonGT. Precision
farming tools: Variable-rate application.
Virginia Cooperative Extension.
[8] SökefeldM. Variable rate technology
for herbicide application. In: OerkeEC,
GerhardsR, MenzG, SikoraR, editors.
Precision Crop Protection - The
Challenge and Use of Heterogeneity.
Cham: Springer; 2010. pp. 335-347. DOI:
[9] KempenaarC, BeenT. Advances in
variable rate technology application
in potato in The Netherlands. Potato
Research. 2018;(1):295-305. DOI:
[10] AhmadL, MahdiSS. Variable rate
technology and variable rate application.
In: AhmadL, MahdiSS, editors. Satellite
Farming. Cham: Springer; 2018. pp. 67-80.
DOI: 10.1007/978-3-030-03448-1
[11] CarraraM, ComparettiA, FeboP,
OrlandoS. Spatially variable rate
herbicide application on durum wheat
in Sicily. Biosystems Engineering.
2004;(4):387-392. DOI: 10.1016/j.
[12] ArsegoIB. Sorção dos herbicidas
diuron e hexazinone em solos de texturas
contrastantes. 66 f. Dissertação (thesis).
Piracicaba: Escola Superior de Agricultura
“Luiz de Queiroz” - Universidade de São
Paulo; 2009. DOI: 10.11606/D.11.2009.
[13] PrataF, LavorentiA. Retenção e
mobilidade de defensivos agrícolas
no solo. In: Alleoni LRF, RegitanoJB,
editors. Simpósio Sobre Dinâmica de
Defensivos Agrícolas no Solo: Aspectos
Práticos e Ambientais. Piracicaba: LSN,
ESALQ/USP; 2002. pp. 58-69
[14] GerstlZ. An update on the K(oc)
concept in regard to regional scale
management. Crop Protection.
2000;(810):643-648. DOI: 10.1016/
[15] MohammadzamaniDM, MinaeiZ,
AlimardaniR, AlmassiM, RashidM,
NorouzpourH. Variable rate herbicide
application using the global positioning
system for generating a digital
management map. International
Journal of Agriculture and Biology.
[16] MendesKF, MartinsBAB, ReisFC,
DiasACR, TornisieloVL. Methodologies
to study the behavior of herbicides on
Pests - Classification, Management and Practical Approaches
plants and the soil using radioisotopes.
Planta Daninha. 2017;(1):1-21. DOI:
[17] NandulaVK, VencillWK. Herbicide
absorption and translocation in plants
using radioisotopes. Weed Science.
2015;(1):140-151. DOI: 10.1614/
[18] MendonçaCG, TornisieloVL,
Victoria FilhoR, LacerdaALS.
Absorption and translocation of 2,4-D
in plants of Memora peregrine. Journal
of Environmental Science and Health-
PartB. 2005;(1):137-143. DOI:
[19] LundED, ColinP, ChristyD,
DrummondPE. Applying soil electrical
conductivity technology to precision
agriculture. In: RobertP, RustR,
LarsonW, editors. Proceedings of
the Fourth International Conference
on Precision Agriculture. Minnesota:
American Society of Agronomy; 1999.
pp. 1089-1100. DOI: 10.2134/1999.
[20] JohnsonCK, DoranJW,
DukeHR, WienholdBJ, EskridgeKM,
ShanahanJF. Field-scale electrical
conductivity mapping for delineating
soil condition. Soil Science Society of
America Journal. 2001;(6):1829-1837.
DOI: 10.2136/sssaj2001.1829
[21] MachadoPLOA, BernardiACC,
ValenciaLIO, MolinJP, GimenezLM,
SilvaCA, etal. Mapeamento da
condutividade elétrica e relação com a
argila de Latossolo sob plantio direto.
Pesquisa Agropecuária Brasileira.
2006;(6):1023-1031. DOI: 10.1590/
[22] APagri. HTV herbicida em taxa
variável [Internet]. 2020. Available from:
taxa-variavel/ [Accessed: 12 May 2020]
[23] SchusterI, NordmeyerH, RathT.
Comparison of vision-based and manual
weed mapping in sugar beet. Biosystems
Engineering. 2007;(1):17-25. DOI:
[24] SaI, PopovićM, KhannaR, ChenZ,
LottesP, LiebischF, etal. WeedMap:
A large-scale semantic weed mapping
framework using aerial multispectral
imaging and deep neural network for
precision farming. Remote Sensing.
2018;(9):e1423. DOI: 10.3390/
[25] TamouridouAA, AlexandridisTK,
PantaziXE, LagopodiAL, KashefiJ,
KasampalisD, etal. Application of
multilayer perceptron with automatic
relevance determination on weed
mapping using UAV multispectral
imagery. Sensors. 2017;(10):e2307.
DOI: 10.3390/s17102307
[26] TsourosDC, BibiS, SarigiannidisPG.
A review on UAV-based applications
for precision agriculture. Information.
2019;(11):349-375. DOI: 10.3390/
[27] PajaresG. Overview and current
status of remote sensing applications
based on unmanned aerial vehicles
(UAVs). Photogrammetric Engineering
& Remote Sensing. 2015;(4):281-329.
DOI: 10.14358/PERS.81.4.281
[28] YangG, LiuJ, ZhaoC, LiZ,
HuangY, YuH, etal. Unmanned aerial
vehicle remote sensing for field-based
crop phenotyping: Current status and
perspectives. Fronties in Plant Science.
2017;:1-26. DOI: 10.3389/fpls.2017.01111
[29] CalderónR, Navas-CortésJA,
LucenaC, Zarco-TejadaPJ. High-
resolution airborne hyperspectral and
thermal imagery for early detection
of Verticillium wilt of olive using
fluorescence, temperature and narrow-
band spectral indices. Remote Sensing
of Environment. 2013;:231-245.
[30] ColominaI, MolinaP. Unmanned
aerial systems for photogrammetry
and remote sensing: A review. ISPRS
Variable Rate Application of Herbicides for Weed Management in Pre- and Postemergence
Journal of Photogrammetry and Remote
Sensing. 2014;:79-97. DOI: 10.1016/j.
[31] PeñaJM, Torres-SánchezJ,
CastroAI, KellyM, López-GranadosF.
Weed mapping in early season maize
fields using object-based analysis of
unmanned aerial vehicle (UAV) images.
PLoS One. 2013;(10):e77151. DOI:
[32] TianL. Development of a sensor-
based precision herbicide application
system. Computers and Electronics
in Agriculture. 2002;(2-3):133-149.
[33] ShiratsuchiLS, ChristoffoletiPJ,
FontesJRA. Aplicação localizada
de herbicidas. Embrapa Cerrados –
Documentos. 2003;:1-18
[34] SmartSensing. WEED-IT Quadro
[Internet]. 2020. Available from: http:// [Accessed:
13 May 2020]
[35] Trimble Agriculture. WeedSeeker
Spot Spray System. 2020. Available
from: https://agriculture.trimble.
system/ [Accessed: 13 May 2020]
[36] KasslerM. Agricultural automation
in the new millennium. Computers
and Electronics in Agriculture.
2001;(1-3):237-240. DOI: 10.1016/
[37] WeissU, BiberP. Plant detection
and mapping for agricultural robots
using a 3D LIDAR sensor. Robotics and
Autonomous Systems. 2011;(5):265-
273. DOI: 10.1016/j.robot.2011.02.011
[38] BecharA, VigneaultC. Agricultural
robots for field operations: Concepts
and components. Biosystems
Engineering. 2016;:94-111. DOI:
[39] PedersenSM, FountasS,
HaveH, BlackmoreBS. Agricultural
robots—System analysis and economic
feasibility. Precision Agriculture.
2006;(4):295-308. DOI: 10.1007/
[40] GriftT. Robotics in crop production.
In: HeldmanDR, MoraruCI, editors.
Encyclopedia of Agricultural, Food, and
Biological Engineering. 2nd ed. New
York: CRC Press; 2010. pp. 260-262.
DOI: 10.1081/E-EAFE-120043046
[41] HollickV. RIPPA Robot Takes Farms
Forward to the Future [Internet]. 2015.
Available from:
future-.html [Accessed: 12 April 2020]
[42] SukkariehS. An Intelligent Farm
Robot for the Vegetable Industry
[Internet]. 2016. Available from: https://
complete.pdf [Accessed: 12 April 2020]
[43] KingA. The future of agriculture.
Nature. 2017;:21-23. DOI:
[44] SellmannF, BangertW, GrzonkaS,
HänselM. RemoteFarming. 1: Human-
machine interaction for a field-robot-
based weed control application in
organic farming. In: 4th International
Conference on Machine Control &
Guidance; 19-20 March 2014. Germany:
Technische Universität Braunschweig;
2014. pp. 36-42
[45] Ecorobotix. Switch to Smartweeding
with Ecorobotix [Internet]. 2020.
Available from: http://www.ecorobotix.
com/en/ [Accessed: 27 May 2020]
[46] RashidiM, MohammadzamaniD.
Variable rate herbicide application
using GPS and generating a digital
management map. In: LarramendyML,
SoloneskiS, editors. Herbicides, Theory
and Applications. London: IntechOpen;
2011. pp.127-144. DOI: 10.5772/1320
... However, conventional agriculture poorly considers the spatial variability of soil characteristics that determine the bioavailability of PRE herbicides. Variability in soil interaction dynamics and herbicide characteristics yields distinguished sorption rates, causing variations in herbicide bioavailability in soil solution and weed control (Gerstl, 2000;Lima & Mendes, 2020;Mohammadzamani et al., 2009). PRE herbicide application without considering the sorption capacity of each soil can result in direct and indirect economic losses, either due to higher than necessary herbicide expenses or possible weed control failures that can reduce yield, as well as the risk of crop injury and pollution of non-target environments (Grisso et al., 2011;Guimarães et al., 2022). ...
... The TB% represents the amount of herbicide bioavailable in the soil solution after sorption and desorption processes. For the recommendation of variable rate application (VRA) of PRE herbicides, this parameter is very important because it allows to estimate the amount of herbicide that will be bioavailable in the soil solution promoting a more sustainable application (Lima & Mendes, 2020). ...
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Preemergence herbicides are traditionally applied uniformly throughout the area; however, weed control may vary due to spatial variability of the soil within the same area. Precision agriculture tools such as variable rate applications of herbicides improve weed control, making it necessary to know the physicochemical characteristics of the soil. The objective of this study was to map the spatial variability of sorption–desorption and agronomic efficiency of indaziflam and metribuzin for weed management in a field of 17.5 ha of Minas Gerais, Brazil. Fifty-five soil samples were collected (0–10 cm depth) and based on their physicochemical characteristics and the sorption and desorption coefficients for indaziflam and metribuzin, determined by a batch equilibrium method, thematic maps were generated for each variable in QGIS (Quantum Geographic Information System). In addition, the bioavailability concentration of each herbicide was determined, and its efficacy was evaluated in controlling Amaranthus hybridus and Eleusine indica in a representative soil sample. The sorption coefficient (Kd(s)) of indaziflam ranged from 6.9 to 40.5 L kg−1, the sorbed percentage (S%) from 61 to 86.6%, the desorbed percentage (D%) from 8.4 to 33.1%, and the total bioavailability (TB%) from 26 to 55.7%. The Kd(s) values of metribuzin ranged from 1.1 to 4.3 L kg−1, S% from 22.8 to 33.9%, D% from 17.2 to 22.0%, and TB% from 79.4 to 96.7%. Organic matter was highly correlated with the TB% of indaziflam (r = –0.8) and metribuzin (r = –0.7). Soil solution equilibrium concentration (Ce) of 1.9 g a.i. ha−1 of indaziflam controlled 80% of A. hybridus and E. indica. The Ce values of metribuzin were 55.2 and 111.2 g a.i. ha−1 to control 80% of A. hybridus and E. indica, respectively. Recommendations of varied doses provided reductions of 17.6% in the total application of indaziflam and 9.8% of metribuzin concerning the highest dose recommended in the field. The characterization of the soil physicochemical characteristics as well as the determination of the total bioavailability of indaziflam and metribuzin, by knowing the sorption–desorption coefficients, allowed for the recommendation of variable and precise doses of herbicides for the efficient management of A. hybridus and E. indica in preemergence, reducing potential negative environmental impacts.
... Map-based is a common approach in which the map of an area is generated based on the georeferenced samples of soil or plants. Since this process involves manual soil sample collection for further analysis, therefore it is an expensive and time-taking process (Lima and Mendes, 2020). However, sensor-based mapping is a faster process that involves data collection and processing on-the go. ...
... The total process needs to be accomplished rapidly so that the weeds dynamic does not change. Delaying this step will render the generated map obsolete (Lima and Mendes, 2020). Therefore, these days, a lot of research work are inclined towards real-time weed detection. ...
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Deep Learning (DL) has been described as one of the key subfields of Artificial Intelligence (AI) that is transforming weed detection for site-specific weed management (SSWM). In the last demi-decade, DL techniques have been integrated with ground as well as aerial-based technologies to identify weeds in still image context and real-time setting. After observing the current research trend in DL-based weed detection, techniques are advancing by assisting precision weeding technologies to make smart decisions. Therefore, the objective of this paper was to present a systematic review study that involves DL-based weed detection techniques and technologies available for SSWM. To accomplish this study, a comprehensive literature survey was performed that consists of 60 closest technical papers on DL-based weed detection. The key findings are summarized as follows, a) transfer learning approach is a widely adopted technique to address weed detection in majority of research work, b) less focus navigated towards custom designed neural networks for weed detection task, c) based on the pretrained models deployed on test dataset, no one specific model can be attributed to have achieved high accuracy on multiple field images pertaining to several research studies, d) inferencing DL models on resource-constrained edge devices with limited number of dataset is lagging, e) different versions of YOLO (mostly v3) is a widely adopted model for detecting weeds in real-time scenario, f) SegNet and U-Net models have been deployed to accomplish semantic segmentation task in multispectral aerial imagery, g) less number of open-source weed image dataset acquired using drones, h) lack of research in exploring optimization and generalization techniques for weed identification in aerial images, i) research in exploring ways to design models that consume less training hours, low-power consumption and less parameters during training or inferencing, and j) slow-moving advances in optimizing models based on domain adaptation approach. In conclusion, this review will help researchers, DL experts, weed scientists, farmers, and technology extension specialist to gain updates in the area of DL techniques and technologies available for SSWM.
... The adoption of the type of system will depend on the investment capacity of the producer, skilled workforce available, and the modality of application. Although it still has some limitations, VRA has been widespread and has been occupying more and more space in chemical management, the tendency in the medium-and long term is that there is a gradual replacement of the conventional method of application (58). Given the benefits provided by VRA along with the engagement of companies and researchers, there will be constant evolution and improvement of this technology, cheapening the costs of implementation and providing its adoption by an increasing number of producers. ...
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India produced around 315 Mt of food grains in 2021-2022. Despite the record production every year around 973 million people could not afford a healthy meal in India. Loss of food grains worth around $11 billion occurs owing to weed infestation. Weeds are the problem creator since time immemorial causing 37-45% losses in yield of crops. They reduce the quality of the produce beside adding to the cost of production. For the control of weeds, the most prominently used method is the chemical control of weeds with the help of herbicides. The chemical control with herbicides being an effective and quick option towards weed control have made the farmers more stooped towards them. This indiscriminate use of herbicides uniformly over the entire fields have led to serious complications. Herbicides resistance, weed flora shift and herbicides residues in the food chain count some among them. Since weeds typically occur in patches rather than uniformly across a field; however, conventional management practices rely on whole-field management. These problems can be reduced by more targeted methods of herbicide application. Site-specific weed management (SSWM) can be an approach towards achieving the targeted control of weeds. Site-specific weed management (SSWM) strategies aim to address these challenges by leveraging remote sensing technologies. Remote sensing enables the identification and mapping of weed infestations, allowing for precise and targeted herbicide application. This review article provides an in-depth analysis of the current state of SSWM using remote sensing, including the methodologies employed, advancements made, challenges faced by this technology.
... This technology works by integrating a variable rate control system with a sprayer for fertilizer, pesticide or herbicide applications. The application at a varied rate can be fundamentally based on maps or sensors [60]. Indeed, there are two main methods for implementing site-specific variable rate applications (VRA): map-based VRA, which adjusts the application rate of a crop production input based on the information contained in a digital map of field properties, and sensor-based systems that use data from real-time sensors to match inputs to the needs of the soil and crop [61]. ...
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In the last few decades, the increase in the world’s population has created a need to produce more food, generating, consequently, greater pressure on agricultural production. In addition, problems related to climate change, water scarcity or decreasing amounts of arable land have serious implications for farming sustainability. Weeds can affect food production in agricultural systems, decreasing the product quality and productivity due to the competition for natural resources. On the other hand, weeds can also be considered to be valuable indicators of biodiversity because of their role in providing ecosystem services. In this sense, there is a need to carry out an effective and sustainable weed management process, integrating the various control methods (i.e., cultural, mechanical and chemical) in a harmonious way, without harming the entire agrarian ecosystem. Thus, intensive mechanization and herbicide use should be avoided. Herbicide resistance in some weed biotypes is a major concern today and must be tackled. On the other hand, the recent development of weed control technologies can promote higher levels of food production, lower the amount of inputs needed and reduce environmental damage, invariably bringing us closer to more sustainable agricultural systems. In this paper, we review the most common conventional and non-conventional weed control strategies from a sustainability perspective, highlighting the application of the precision and automated weed control technologies associated with precision weed management (PWM).
Mishra JS, Sushilkumar and Rao AN (Eds.). 2022. Technological glimpses on weeds and their management. Indian Society of Weed Science, Jabalpur, India, 81 p. This publication contains articles on major aspects of weed management by eminent scientists of the country and abroad, which will be useful to all our stakeholders including the young scientists, teachers, students, extension workers, and the farmers.
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The effectiveness of 12 pre-em herbicides in controlling ragweed was studied: atrazine, alachlor, acetochlor, alachlor+linuron, S-metolachlor, pendimethalin, metribuzin, prometryn, napropamide, imazethapyr, oxyfluorfen and dichlobenil. The research was conducted over two years, where the herbicides were applied in May on soil that had been prepared for sowing, but not sown. The efficacy was observed 30, 45 and 60 days after the application. Herbicides were used in 4 different doses: D1 which is ½ of the recommended dose, D2 is the lowest recommended dose, D3 is the highest recommended dose and D4 is the dose where the herbicides were used with the dose higher than it is recommended (D3×1,5). Efficacy is expressed as the percentage of efficacy for the number and fresh weed biomass, compared to the control. Coefficient of multiple correlation between the percentage of efficacy (PE), as a dependent variable, and the dose (D) and the number of days from the application of herbicides (DAA), as independent variables is statistically significant and in all cases it ranges between 0,853** and 0,961****. Partial correlation coefficient of efficacy percentage dependent on the herbicide dose is positive and in almost all cases highly significant, varying from 0,739** to 0,956****. Partial correlation coefficient between the herbicide efficacy percentage for common ragweed biomass and time after herbicide application is negative and statistically significant, or highly significant and varies between -0,606* and -0,904***. The partial correlation coefficient of the herbicide efficiency percentage for the common ragweed plant number and days after herbicide application, is also negative and varies, depending on the herbicide, between -0,182NZ and -0,923****.
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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.
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The ability to automatically monitor agricultural fields is an important capability in precision farming, enabling steps towards more sustainable agriculture. Precise, high-resolution monitoring is a key prerequisite for targeted intervention and the selective application of agro-chemicals. The main goal of this paper is developing a novel crop/weed segmentation and mapping framework that processes multispectral images obtained from an unmanned aerial vehicle (UAV) using a deep neural network (DNN). Most studies on crop/weed semantic segmentation only consider single images for processing and classification. Images taken by UAVs often cover only a few hundred square meters with either color only or color and near-infrared (NIR) channels. Although a map can be generated by processing single segmented images incrementally, this requires additional complex information fusion techniques which struggle to handle high fidelity maps due to their computational costs and problems in ensuring global consistency. Moreover, computing a single large and accurate vegetation map (e.g., crop/weed) using a DNN is non-trivial due to difficulties arising from: (1) limited ground sample distances (GSDs) in high-altitude datasets, (2) sacrificed resolution resulting from downsampling high-fidelity images, and (3) multispectral image alignment. To address these issues, we adopt a stand sliding window approach that operates on only small portions of multispectral orthomosaic maps (tiles), which are channel-wise aligned and calibrated radiometrically across the entire map. We define the tile size to be the same as that of the DNN input to avoid resolution loss. Compared to our baseline model (i.e., SegNet with 3 channel RGB (red, green, and blue) inputs) yielding an area under the curve (AUC) of [background=0.607, crop=0.681, weed=0.576], our proposed model with 9 input channels achieves [0.839, 0.863, 0.782]. Additionally, we provide an extensive analysis of 20 trained models, both qualitatively and quantitatively, in order to evaluate the effects of varying input channels and tunable network hyperparameters. Furthermore, we release a large sugar beet/weed aerial dataset with expertly guided annotations for further research in the fields of remote sensing, precision agriculture, and agricultural robotics.
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Precision agriculture is a farming management concept based on observing, measuring and responding to inter- and intra-field variability in crops. In this paper, we focus on responding to intra-field variability in potato crops and analyse variable rate applications (VRAs). We made an overview of potential VRAs in potato crop management in The Netherlands. We identified 13 potential VRAs in potato, ranging from soil tillage to planting to crop care to selective harvest. We ranked them on availability of ‘proof of concept’ and on-farm test results. For five VRAs, we found test results allowing to make a cost-benefit assessment. These five VRAs were as follows: planting, soil herbicide weed control, N side dress, late blight control and haulm killing. They use one of two types of spatial data: soil maps or biomass index maps. Data on costs and savings of the VRAs showed that the investments in VRAs will pay off under practical conditions in The Netherlands. Savings on pesticide use and N-fertilizer use with the VRAs were on average about 25%, which benefits the environment too. We foresee a slow but gradual adoption of VRAs in potato production. More VRAs will become available given ongoing R&D. The perspectives of VRAs in potatoes are discussed.
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Remote sensing techniques are routinely used in plant species discrimination and of weed mapping. In the presented work, successful Silybum marianum detection and mapping using multilayer neural networks is demonstrated. A multispectral camera (green-red-near infrared) attached on a fixed wing unmanned aerial vehicle (UAV) was utilized for the acquisition of high-resolution images (0.1 m resolution). The Multilayer Perceptron with Automatic Relevance Determination (MLP-ARD) was used to identify the S. marianum among other vegetation, mostly Avena sterilis L. The three spectral bands of Red, Green, Near Infrared (NIR) and the texture layer resulting from local variance were used as input. The S. marianum identification rates using MLP-ARD reached an accuracy of 99.54%. Τhe study had an one year duration, meaning that the results are specific, although the accuracy shows the interesting potential of S. marianum mapping with MLP-ARD on multispectral UAV imagery.
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Phenotyping plays an important role in crop science research; the accurate and rapid acquisition of phenotypic information of plants or cells in different environments is helpful for exploring the inheritance and expression patterns of the genome to determine the association of genomic and phenotypic information to increase the crop yield. Traditional methods for acquiring crop traits, such as plant height, leaf color, leaf area index (LAI), chlorophyll content, biomass and yield, rely on manual sampling, which is time-consuming and laborious. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become an important approach for fast and non-destructive high throughput phenotyping and have the advantage of flexible and convenient operation, on-demand access to data and high spatial resolution. UAV-RSPs are a powerful tool for studying phenomics and genomics. As the methods and applications for field phenotyping using UAVs to users who willing to derive phenotypic parameters from large fields and tests with the minimum effort on field work and getting highly reliable results are necessary, the current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed based on the literature survey of crop phenotyping using UAV-RSPs in the Web of Science™ Core Collection database and cases study by NERCITA. The reference for the selection of UAV platforms and remote sensing sensors, the commonly adopted methods and typical applications for analyzing phenotypic traits by UAV-RSPs, and the challenge for crop phenotyping by UAV-RSPs were considered. The review can provide theoretical and technical support to promote the applications of UAV-RSPs for crop phenotyping.
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There are a number of questions that must be answered before establishing a site-specific crop management (SSCM) program. Many of these questions are economic, some are agronomic and environmental, and others are technology-related. This publication is intended to discuss variable-rate devices that are available, while providing an understanding of which technologies might best fit a cropping system and production management strategy. Most farmers have practiced a form of variable-rate application (VRA) with a conventional sprayer. A conventional sprayer applies a chemical that is tank-mixed with a carrier (usually water) using spray nozzles and a pressure-regulating valve to provide a desired volumetric application of spray mix at a certain vehicle speed. Any change in the boom pressure or vehicle speed from that of the calibration results in an application rate different from the planned rate. Applicators have used this to their advantage at times. For example, when observing an area of heavy weed infestation, the applicator can manually increase the pressure or reduce the speed to apply a higher (but somewhat unknown) rate of herbicide.
A technological revolution in farming led by advances in robotics and sensing technologies looks set to disrupt modern practice.
This review investigates the research effort, developments and innovation in agricultural robots for field operations, and the associated concepts, principles, limitations and gaps. Robots are highly complex, consisting of different sub-systems that need to be integrated and correctly synchronised to perform tasks perfectly as a whole and successfully transfer the required information. Extensive research has been conducted on the application of robots and automation to a variety of field operations, and technical feasibility has been widely demonstrated. Agricultural robots for field operations must be able to operate in unstructured agricultural environments with the same quality of work achieved by current methods and means. To assimilate robotic systems, technologies must be developed to overcome continuously changing conditions and variability in produce and environments. Intelligent systems are needed for successful task performance in such environments. The robotic system must be cost-effective, while being inherently safe and reliable—human safety, and preservation of the environment, the crop and the machinery are mandatory. Despite much progress in recent years, in most cases the technology is not yet commercially available. Information-acquisition systems, including sensors, fusion algorithms and data analysis, need to be adjusted to the dynamic conditions of unstructured agricultural environments. Intensive research is needed on integrating human operators into the system control loop for increased system performance and reliability. System sizes should be reduced while improving the integration of all parts and components. For robots to perform in agricultural environments and execute agricultural tasks, research must focus on: fusing complementary sensors for adequate localisation and sensing abilities, developing simple manipulators for each agricultural task, developing path planning, navigation and guidance algorithms suited to environments besides open fields and known a-priori, and integrating human operators in this complex and highly dynamic situation.
Remotely Piloted Aircraft (RPA) is presently in continuous development at a rapid pace. Unmanned Aerial Vehicles (UAVs) or more extensively Unmanned Aerial Systems (UAS) are platforms considered under the RPAs paradigm. Simultaneously, the development of sensors and instruments to be installed onboard such platforms is growing exponentially. These two factors together have led to the increasing use of these platforms and sensors for remote sensing applications with new potential. Thus, the overall goal of this paper is to provide a panoramic overview about the current status of remote sensing applications based on unmanned aerial platforms equipped with a set of specific sensors and instruments. First, some examples of typical platforms used in remote sensing are provided. Second, a description of sensors and technologies is explored which are onboard instruments specifically intended to capture data for remote sensing applications. Third, multi-UAVs in collaboration, coordination, and cooperation in remote sensing are considered. Finally, a collection of applications in several areas are proposed, where the combination of unmanned platforms and sensors, together with methods, algorithms, and procedures provide the overview in very different remote sensing applications. This paper presents an overview of different areas, each independent from the others, so that the reader does not need to read the full paper when a specific application is of interest.