Precision-spraying-review-Precision-spraying-review.pdf

sensors
Review
Opportunities and Possibilities of Developing an Advanced
Precision Spraying System for Tree Fruits
Md Sultan Mahmud 1,2 , Azlan Zahid 1,2 , Long He 1,2,* and Phillip Martin 2,3

Citation: Mahmud, M.S.; Zahid, A.;
He, L.; Martin, P. Opportunities and
Possibilities of Developing an
Advanced Precision Spraying System
for Tree Fruits. Sensors 2021, 21, 3262.
https://doi.org/10.3390/s21093262
Academic Editor: Sindhuja Sankaran
Received: 28 March 2021
Accepted: 4 May 2021
Published: 8 May 2021
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1 Department of Agricultural and Biological Engineering, The Pennsylvania State University,
University Park, PA 16802, USA; mvm6735@psu.edu (M.S.M.); axz264@psu.edu (A.Z.)
2 Fruit Research and Extension Center, The Pennsylvania State University,
Biglerville, PA 17307, USA; plm30@psu.edu
3 Department of Plant Pathology and Environmental Microbiology, The Pennsylvania State University,
University Park, PA 16803, USA
* Correspondence: luh378@psu.edu; Tel.: +1-717-677-6116
Abstract: Reducing risk from pesticide applications has been gaining serious attention in the last few
decades due to the significant damage to human health, environment, and ecosystems. Pesticide
applications are an essential part of current agriculture, enhancing cultivated crop productivity and
quality and preventing losses of up to 45% of the world food supply. However, inappropriate and
excessive use of pesticides is a major rising concern. Precision spraying addresses these concerns by
precisely and efficiently applying pesticides to the target area and substantially reducing pesticide
usage while maintaining efficacy at preventing crop losses. This review provides a systematic sum-
mary of current technologies used for precision spraying in tree fruits and highlights their potential,
briefly discusses factors affecting spraying parameters, and concludes with possible solutions to
reduce excessive agrochemical uses. We conclude there is a critical need for appropriate sensing
techniques that can accurately detect the target. In addition, air jet velocity, travel speed, wind speed
and direction, droplet size, and canopy characteristics need to be considered for successful droplet
deposition by the spraying system. Assessment of terrain is important when field elevation has
significant variability. Control of airflow during spraying is another important parameter that needs
to be considered. Incorporation of these variables in precision spraying systems will optimize spray
decisions and help reduce excessive agrochemical applications.
Keywords: crop protection; canopy detection; canopy density; canopy volume; deep learning;
machine vision; sensing
1. Introduction
Tree fruits are high-value commodities worth almost $18 billion in the U.S. [1]. Tree
fruits require pesticides for consistent production, because failure to control insects, pests,
disease, and weeds results in losses of up to 100% of the crop [2,3]. However, pesticide
use in orchards has come under scrutiny for contaminating the air, soil, and sub-surface
water, which is considered hazardous to the health and wellbeing of people, animals, and
the environment [4–7]. Pesticides are also expensive, costing tree fruit growers hundreds
to low thousands of dollars per hectare, which accounts for around a third of total pro-
duction costs [8–10]. Pimentel and Burgess [11] showed that pesticide applications caused
around $8.2 billion annual environmental and economic losses in the U.S. Currently, the
conventional constant-rate air blast sprayers widely used in commercial-scale orchards
are estimated to deposit less than 30% of the pesticides on target trees [12]. Most of the
pesticide waste is from applications to the spaces above, below, and between tree canopies.
Substantial reductions in pesticide waste and misuse can be achieved by precise and
targeted applications of pesticides to the tree canopies.
Sensors 2021, 21, 3262. https://doi.org/10.3390/s21093262 https://www.mdpi.com/journal/sensors

Sensors 2021, 21, 3262 2 of 29
Precision spraying is defined as the precise spraying of pesticides based on target
information such as the size, shape, structure, and density of the tree canopy as obtained
from sensors such as cameras, ultrasound, and laser. Using precision spraying technolo-
gies, Esau et al. [13] reduced agrochemical usages from 9.90% to 51.22% by not applying
pesticides to bare spot areas in wild blueberry fields. Jejčič et al. [14] saved about 20.2% of
spray volume by applying pesticides based on the characteristics of the targets in orchards.
Asaei et al. [15] considered the spacing between trees along orchard rows for pesticide
application and obtained a reduction of up to 54% of pesticide usage. Although several
studies have reported a significant amount of pesticide reduction during spraying, most
of them evaluated their savings on a small scale or in fairly controlled environments that
are not feasible to a large-scale orchard field. Additionally, despite the advanced spraying
technologies available in the past few years, there are still concerns with the amount of
chemicals used and lost to drift [16], requiring more advanced technology for spraying in
the large scale orchards. Precision sprayers are commercially available for weed control
and plant treatment in arable land. However, very few precision spraying systems are
commercially available for tree fruit orchards, and most of them are not fully compatible
with successful spray deposition in real-time orchard conditions. Previously available
reviews on precision spraying have focused mainly on plant canopy characterizations and
flow rate calculations [17], sensing technologies used for field crops [18], and the empirical
dose expression models applied for spray decision making [19].
This review focuses on the importance of advanced spraying systems, limitations
of current technologies, factors affecting sprayer performance, and gaps in knowledge
that need to be considered in the development of advanced precision sprayer systems for
tree fruit orchards. Firstly, the history and revolution of sprayers and the limitations of
current spraying methods were discussed. Then, the core components and technologies
for precision spraying were identified and discussed, which are centered around camera,
ultrasonic, and laser-sensing technologies. The factors effecting the use of these core sensing
technologies were followed, including nozzle types, spraying distance, droplet size, canopy
characteristics, wind speed, temperature, and relative humidity. Finally, possible pathways
to address some of the challenges were discussed and concluded.
2. Overview of Sprayers and Spraying Systems
2.1. Chemical Sprayers in Tree Fruit Orchards
Over the past decades, orchard sprayer designs have evolved from hand-boom based
horse-drawn wagons to sensor-controlled tractor-pulled sprayers, due to increased con-
cern about chemical wastage and environmental contamination, changes in horticulture,
increased demand for quality products, and development of new technologies. The revolu-
tion of sprayer design is listed in (Table 1). Various early devices for pesticide application
were introduced back in the early 20th century, including pumps, nozzles, bellows, and
steam-powered sprayers [20]. Given the labor shortage problems, early rapid adoption
of air blast sprayers occurred in the 1940s. The strong farmer interest in this innovation
encouraged a surge in the use of air-assisted spraying [21]. Boom sprayers and other
innovations (e.g., mist blowers, handguns) from Europe were developed that allowed
growers to apply pesticides more efficiently [22–24]. A significant change of chemical
application strategies took place when the air jet models were developed that could carry
the spray up and into the canopy [25]. The air jet models allowed chemicals to be sprayed
from the ground instead of only being sprayed from planes. In the last two–three decades,
modern sprayers including tunnel sprayers, tower sprayers, and precision sprayers have
allowed orchard growers to reduce off-target deposition of spray materials and drift. Of
these, precision or intelligent sprayers equipped with sensing technology have shown great
potential to reduce excessive chemical applications [16,26].

Sensors 2021, 21, 3262 3 of 29
Table 1. Revolution of sprayers used in orchard spraying.
Evaluation Sprayer Types References
Early Sprayer Design
Steam-powered sprayers; boom sprayers and early
mist blowers; handguns
[20,22–24]
Air Jet Models Polar jets; plane jet sprayer [25,27,28]
Modern Air blast Sprayers Tower sprayers; tunnel sprayers [29–31]
Precision Sprayers Sensor guided air blast sprayers; intelligent sprayer [16,26]
Air blast sprayers (Figure 1) have been common in tree fruit orchards for a long time.
The sprayers consist of a single fan located at the rear of the machine that pulls air in and
redistributes it upwards into the tree canopy. The direction and volume of air are critical
factors because the volume of air released from the fan must be matched with the tree canopy
for appropriate spray deposition and coverage. These sprayers were introduced to orchards
when trees were grown in the iconic architecture with a height greater than 6 m with many
wide-spanning branches, and were planted in rows with spacings of 4–6 m. Fox et al. [32]
showed that air blast sprayers were suitable for trees with such iconic architecture due to the
large volume of air needed to push spray droplets 9 m into the tree canopy. However, many
apple trees planted in the past few years are only 2 to 4 m tall and have smaller canopies, which
creates problems in matching them to the air volume from conventional air blast sprayers [33].
Conventional sprayers bring significant challenges to matching the air output during spraying
with what is needed for successful spray droplet deposition.
s 2021, 21, x FOR PEER REVIEW 3 of 29
Table 1. Revolution of sprayers used in orchard spraying.
Evaluation Sprayer Types References
Early Sprayer Design Steam-powered sprayers; boom sprayers and early mist blowers; handguns [20,22–24]
Air Jet Models Polar jets; plane jet sprayer [25,27,28]
dern Air blast Sprayers Tower sprayers; tunnel sprayers [29–31]
Precision Sprayers Sensor guided air blast sprayers; intelligent sprayer [16,26]
Air blast sprayers (Figure 1) have been common in tree fruit orchards for a long time.
The sprayers consist of a single fan located at the rear of the machine that pulls air in and
redistributes it upwards into the tree canopy. The direction and volume of air are critical
factors because the volume of air released from the fan must be matched with the tree
canopy for appropriate spray deposition and coverage. These sprayers were introduced
to orchards when trees were grown in the iconic architecture with a height greater than 6
m with many wide-spanning branches, and were planted in rows with spacings of 4–6 m.
Fox et al. [32] showed that air blast sprayers were suitable for trees with such iconic archi-
tecture due to the large volume of air needed to push spray droplets 9 m into the tree
canopy. However, many apple trees planted in the past few years are only 2 to 4 m tall
and have smaller canopies, which creates problems in matching them to the air volume
from conventional air blast sprayers [33]. Conventional sprayers bring significant chal-
lenges to matching the air output during spraying with what is needed for successful
spray droplet deposition.
(a) (b)
Figure 1. Air blast spraying systems (a) constant rate air blast sprayer; (b) intelligent sprayer.
Air blast sprayers include conventional axial-fan air blast sprayers, cannon sprayers,
tunnel sprayers, tower sprayers, and custom-designed or modified air blast sprayers. The
axial fan air-assisted sprayer produces a large radial spray volume and is the predominant
design of sprayer used in tree fruit orchards. It is poorly targeted for modern intensive
tree fruit orchards and creates a significant risk of off-target contamination by spray drift
and chemical wastage to the ground [33]. Tunnel and crossflow sprayers have been poorly
utilized due to increased expenses and decreased operational adaptability [26]. There are
also problems associated with spraying with tunnel sprayers because of diseases that
might be spread by physical contact or by reused spray solution while driving such a large
machine through the orchards [32]. Tower type sprayers, which direct the airflow from
the fan into horizontal ducts on a vertical plane, have improved spray coverage in the top
center of trees and work well for trees with consistent heights. Cannon sprayers are com-
monly small in size and only require a small path for spray application, but may not pro-
duce uniform spray deposition and coverage because their performance is influenced by
wind speed.
Figure 1. Air blast spraying systems (a) constant rate air blast sprayer; (b) intelligent sprayer.
Air blast sprayers include conventional axial-fan air blast sprayers, cannon sprayers,
tunnel sprayers, tower sprayers, and custom-designed or modified air blast sprayers. The
axial fan air-assisted sprayer produces a large radial spray volume and is the predominant
design of sprayer used in tree fruit orchards. It is poorly targeted for modern intensive
tree fruit orchards and creates a significant risk of off-target contamination by spray drift
and chemical wastage to the ground [33]. Tunnel and crossflow sprayers have been poorly
utilized due to increased expenses and decreased operational adaptability [26]. There
are also problems associated with spraying with tunnel sprayers because of diseases that
might be spread by physical contact or by reused spray solution while driving such a large
machine through the orchards [32]. Tower type sprayers, which direct the airflow from
the fan into horizontal ducts on a vertical plane, have improved spray coverage in the
top center of trees and work well for trees with consistent heights. Cannon sprayers are
commonly small in size and only require a small path for spray application, but may not
produce uniform spray deposition and coverage because their performance is influenced
by wind speed.

Sensors 2021, 21, 3262 4 of 29
2.2. Challenges with Conventional Sprayers
Conventional air blast sprayers designed for larger trees can waste more than 50%
of pesticides to the ground and the atmosphere when spraying modern apple trees that
have smaller canopies [34–36]. Spray applications in tree fruit orchards have been based on
the application rate per unit area which was calculated from the number of rows and the
average tree spacing. This was known as broadcast spraying, and did not automatically
adjust for weather conditions, tree canopy density, missing trees, or the height and shape
of the trees, and resulted in up to 60–70% off-target losses [37]. Precision spraying can
substantially reduce off-target losses by using real-time tree canopy data to adjust the
sprayers for precise and targeted pesticide applications.
3. Core Components and Technologies for Precision Spraying
3.1. Tree Canopy Parameter Measurements
With the advancement of modern sensors and technologies, the methods utilized for
measuring complex tree canopy parameters are developing rapidly. These methods aim to
accurately measure geometric canopy features, canopy density, canopy leaf area density
(LAD), and canopy leaf area index (LAI) (Table 2). These measurements provide precise
information to assess the accurate spray volume needed for specific trees.
Table 2. Crop structure parameter equations for precision spraying in orchards 1.
Name Crop Structure Parameter References
Crown height H [38]
Leaf Wall Area (LWA) LWA = 10000H
B [39]
Tree Row Volume (TRV) TRV = 10000W×H
B [40]
Leaf Area Density (LAD) LAD ∝ A [41]
Tree Canopy Density (TCD) TCD =
Np
TA
[42]
Leaf Area Index (LAI) LAI = A×W×H
B [43]
1 Where A in m2/m3 (leaf area per unit canopy volume) is the leaf area density; B in m is the row spacing; H in m
is the height interval of the canopy; W in m is the canopy width; LAI (leaf area per unit ground area) is the leaf area
index; Np is the number of pixels or points counted in a targeted region; TA is the area of the targeted region in m2.
Geometric Canopy Dimensions: Tree geometric canopy dimensions, such as canopy
height, width, and volume, are needed for calculating the canopy density, leaf area density,
and canopy leaf area index. Camera sensors are mainly used to detect the LWA (leaf wall
area) and have limited use in detecting canopy volume. The main sensors used for canopy
geometric characteristics measurements are ultrasonic and LiDAR. Beyaz and Dagtekin [44]
measured citrus tree canopy volume using ultrasonic and camera imaging sensors and
concluded that the ultrasonic sensor can achieve 15% better correlation compared to the
imaging technique. However, the performance of ultrasonic sensors is influenced by the
movement of the carrying medium. LiDAR is a special application of laser sensors that
shows great promise in detecting canopy dimensions. Data from LiDAR are processed
mostly with slice and cuboid algorithms for canopy volume measurement. Slice algorithms
split the tree canopy horizontally or longitudinally while cuboid algorithms divide the
tree canopy into different sized cuboids. Lee and Ehsani [45] used LiDAR sensor and slice
algorithm to measure the citrus tree canopy geometric characteristics. The tree was split into
several layers or slices along the detection path, and each slice was considered a symmetric
polyhedron. The study reported the relative errors were −0.37%, 0.01%, −1.99%, and
5.96% for tree canopy height, width, surface area, and volume measurements, respectively.
Yu et al. [46] also used the slice algorithm to calculate tree canopy volume through the
LiDAR sensor and achieved a relative error of 5% compared with manual measurement.
The authors concluded that the slice algorithm is mainly suitable for trees with symmetrical
structures, and the error would be increased while tested with other structured trees.
Mahmud et al. [47] measured tree canopy volume based on the cuboids method using
a LiDAR sensor and alpha shape algorithm. The main advantage of using alpha shape

Sensors 2021, 21, 3262 5 of 29
algorithm is it allows for concave shapes that have a lower chance of overestimation.
Using the alpha value of 1, this study achieved a very high correlation of 0.98 (R2 = 0.95).
Cheraïet et al. [48] used a 2D LiDAR sensor for estimating canopy height and width in
vineyards. A cuboid-based Bayesian point cloud classification algorithm (BPCC) was
used to process LiDAR data, which combined an automatic filtering method (AFM) and
a classification method based on clustering. Estimation of canopy height and width
obtained strong correlations (R2 = 0.94 and 0.89, respectively) showed the potential of
using this technique to adjust the spray rate for variable-rate spraying. Chen et al. [49]
measured tree canopy characteristics as a plurality of cuboids along the longitudinal
direction using LiDAR sensor. The study concludes this method can be useful for various
tree canopy parameter measurements and the accuracy of measurement depends on the
number of cuboids.
Canopy Density: Tree canopy density is essential to determine the spray volume
for accurate variable-rate spray applications [47,50]. The canopy density is measured by
dividing the number of leaves of a tree by the total leaf canopy area. Li et al. [51] utilized an
ultrasonic-sensing method to build the relationship between canopy density and leaf layers
using an orthogonal regression rotation test. The maximum relative errors were 29.92%,
21.33%, 25.64%, and 17.68% between the model values and the actual values. There was a
close relationship between echo energy, detection distance, and canopy density. The study
was conducted in regular/uniform leaf distribution, and this method is not practical to
accurately calculate the density of canopies with non-uniform and irregular leaf positions.
Hu and Whitty [50] used 2D LiDAR, three RGB-D cameras, and a GPS-RTK module to
calculate apple tree canopy density and achieved an average co-efficient of determination
(R2) of 0.90. Mahmud et al. [47] developed a section-based canopy density measurement
system comprised of a Velodyne sick 3D light detection and ranging (LiDAR) sensor with
series of processing algorithms. The density variability was measured within four tree
sections divided by trellis wire positions (Figure 2). The study predicted the number of
leaves in four sections using the developed models. The average percent errors of 24.44%
and 14.21% were calculated for predicting the number of leaves in GoldRush and Fuji apple
trees, respectively. The canopy density maps were generated with the predicted numbers
of leaves for precision spraying.
Sensors 2021, 21, x FOR PEER REVIEW 5 of 29
manual measurement. The authors concluded that the slice algorithm is mainly suitable
for trees with symmetrical structures, and the error would be increased while tested with
other structured trees. Mahmud et al. [47] measured tree canopy volume based on the
cuboids method using a LiDAR sensor and alpha shape algorithm. The main advantage
of using alpha shape algorithm is it allows for concave shapes that have a lower chance of
overestimation. Using the alpha value of 1, this study achieved a very high correlation of
0.98 (R2 = 0.95). Cheraïet et al. [48] used a 2D LiDAR sensor for estimating canopy height
and width in vineyards. A cuboid-based Bayesian point cloud classification algorithm
(BPCC) was used to process LiDAR data, which combined an automatic filtering method
(AFM) and a classification method based on clustering. Estimation of canopy height and
width obtained strong correlations (R2 = 0.94 and 0.89, respectively) showed the potential
of using this technique to adjust the spray rate for variable-rate spraying. Chen et al. [49]
measured tree canopy characteristics as a plurality of cuboids along the longitudinal di-
rection using LiDAR sensor. The study concludes this method can be useful for various
tree canopy parameter measurements and the accuracy of measurement depends on the
number of cuboids.
Canopy Density: Tree canopy density is essential to determine the spray volume for
accurate variable-rate spray applications [47,50]. The canopy density is measured by di-
viding the number of leaves of a tree by the total leaf canopy area. Li et al. [51] utilized an
ultrasonic-sensing method to build the relationship between canopy density and leaf lay-
ers using an orthogonal regression rotation test. The maximum relative errors were
29.92%, 21.33%, 25.64%, and 17.68% between the model values and the actual values.
There was a close relationship between echo energy, detection distance, and canopy den-
sity. The study was conducted in regular/uniform leaf distribution, and this method is not
practical to accurately calculate the density of canopies with non-uniform and irregular
leaf positions. Hu and Whitty [50] used 2D LiDAR, three RGB-D cameras, and a GPS-RTK
module to calculate apple tree canopy density and achieved an average co-efficient of de-
termination (R2) of 0.90. Mahmud et al. [47] developed a section-based canopy density
measurement system comprised of a Velodyne sick 3D light detection and ranging (Li-
DAR) sensor with series of processing algorithms. The density variability was measured
within four tree sections divided by trellis wire positions (Figure 2). The study predicted
the number of leaves in four sections using the developed models. The average percent
errors of 24.44% and 14.21% were calculated for predicting the number of leaves in
GoldRush and Fuji apple trees, respectively. The canopy density maps were generated
with the predicted numbers of leaves for precision spraying.
(a) (b)
Figure 2. Canopy foliage density map (a) Tree canopy points without the trunk, trellis wires, and
support pole points divided into sections; (b) canopy density map considering the number of leaves
(per grid area) [47] (used with permission).
Figure 2. Canopy foliage density map (a) Tree canopy points without the trunk, trellis wires, and
support pole points divided into sections; (b) canopy density map considering the number of leaves
(per grid area) [47] (used with permission).
Canopy Leaf Area Density: Canopy leaf area density (LAD) is crucial for characteriz-
ing the differences in leaf area at different layers in the vertical direction and employing
variable-rate control of nozzles [52]. The LAD is the unilateral leaf area of the photo-

Sensors 2021, 21, 3262 6 of 29
synthetic tissue per unit canopy volume. The measurement of LAD requires multi-point
positioning, which is best obtained with LiDAR sensors. The calculation of LAD is sensitive
to volume calculation models, such as voxel models which require an accurate choice of
voxel size. Wang et al. [52] calculated the LAD by segmenting the LiDAR point cloud from
tree canopies and volume element models. A Voxel-based canopy profiling (VCP) method
was used for LAD calculation and reported a relative error of 1.26%. Chakraborty et al. [53]
compared the voxel grid and convex hull methods to measure canopy volume for apple
trees and grapevines using 3D LiDAR. They reported the voxel gird method performed
poorly with a correlation of 0.51 compared to the convex hull method of 0.81. The authors
noted that the voxel gird method is affected by voxel size and this method is computa-
tionally intense. Béland et al. [54] mapped forest leaf area density through a radiative
transfer simulation model, voxel method, and a multi-view terrestrial LiDAR. The voxel
arrays were inserted into the radiative transfer model to simulate bidirectional reflectance
factors and the vertical fraction of absorbed radiation. This study’s findings established
initial guidelines for mapping leaf area density in dense tree areas. Berk et al. [55] re-
constructed the tree canopy for calculating LAD through LiDAR sensor and the volume
element method. The trapezoidal method was used to calculate individual volumes of tree
canopy volume elements. The maximum correlation of 0.80 was achieved when establish-
ing the relationship between tree canopy volume and LAD. The study concluded that the
LAD’s accuracy depends on how accurately the size of volume element is estimated.
Canopy Leaf Area Index: The canopy leaf area index (LAI) is a core parameter of
vegetation structure used to represent the leaf area per unit surface area [56]. It is a dimen-
sionless quantity that can provide sufficient information for pesticide dose calculation for
precise control sprayer nozzles. The LAI can be measured either directly or indirectly; the
direct approach is time-consuming while the indirect method requires sensor applications.
Arnó et al. [57] measured LAI using vehicle-mounted LiDAR sensors though scanning the
tree. The tree area index achieved a strong R2 of 0.92; however, the insufficient number
of scans and the complexity of the data analysis for LAI estimation were considered the
major limitations of this study. Comba et al. [58] measured LAI from 3D point clouds
acquired in vineyards using multispectral imagery and calculated through a multivariate
linear regression model. Image data were obtained using an unmanned aerial vehicle
(UAV), and spatial distribution of the leaves along the canopy wall was measured. The
vineyard LAI estimation showed a good correlation with R2 of 0.82 compared with the
traditional method. Indirabai et al. [59] estimated LAI through a ground-based LiDAR
scanner and a point spatial density (PSD) algorithm. The algorithm involved filtering
and 3D reconstruction of individual trees using super voxel and min-cut segmentation
approaches. Experimental results reported a consistent correlation (with R2 = 0.96) between
the predicted and reference LAI measurements.
3.2. Sensor-Based Canopy Measurement Technologies for Precision Spraying
Measurements of tree canopy parameters require different advanced sensor appli-
cations and implementations. The following sections broadly discuss the application of
different sensors for precision spraying. Machine vision systems use camera sensors (RGB,
NIR, hyperspectral, multispectral, and infrared), and range-sensing systems use ultrasonic
and laser sensors to collect tree canopy data and carefully evaluate canopy parameters
and targets. Zhang et al. [60] surveyed the different types of real-time sensor applications
for variable-rate spraying, including infrared, stereo vision, ultrasonic, and laser scanner.
They reported laser scanner and stereo vision sensors as the most promising and comple-
mentary techniques to target localization and canopy density estimation of the trees. A
general workflow of the target detection procedure using different sensing techniques is
illustrated (Figure 3). Three different sensing techniques of camera, ultrasonic, and laser are
discussed in this study because these are the main technologies used in precision spraying
applications for tree fruit orchards.

Sensors 2021, 21, 3262 7 of 29
and laser are discussed in this study because these are the main technologies used in pre-
cision spraying applications for tree fruit orchards.
Figure 3. Typical flow-chart for canopy parameter measurement using different sensing systems
to support precision spraying.
3.2.1. Camera Sensor-Based Technologies for Precision Spraying
Camera-based sensing tries to mimic human perception to provide information or
inputs based on processed image data to spraying systems needed for canopy characteri-
zation-based pesticide applications. Image processing includes pre-processing, segmen-
tation, feature extraction, and classification. Image pre-processing eases the segmentation
of the targeted region (e.g., diseases, canopy, and stresses) from the crop images. It in-
volves image rotation, normalization, resizing, color transformation, denoising and con-
trast enhancement, etc. Of these, color transformation is one of the most important for
segmentation. A detailed description, including the advantages and disadvantages of dif-
ferent color spaces used in machine vision systems, is discussed by Cheng et al. [61]. The
conversion of different color spaces used in target detection including channels and trans-
formation function from RGB was thoroughly presented by García–Mateos et al. [62].
After image pre-processing, target area segmentation was carried out. With appro-
priate target area segmentation different types of targets (e.g., diseased area, canopy,
background, gaps between canopies, and so on) can be recognized for precision spraying
applications. Various features are being used to differentiate target regions in crops using
machine vision applications. Generally, these features are grouped into four sub-classes:
morphology, visual texture, spectral, and spatial contexts [63–65]. Among them, morpho-
logical and visual texture features are most popular and widely applied in orchards for
target detections. The extracted features will help the classifier know about the character-
istics of different symptoms from the captured images. LWA information and parameters,
including distance, area, shape, height, and density, were measured by Xiao et al. [66]
using a Microsoft Kinect system. The system consisted of an RGB camera along with a
monochrome complementary metal-oxide-semiconductor (CMOS) video camera and an
IR transmitter. The Otsu’s method was used to segment the acquired color images to ob-
tain binarized and segmented images from peach and apricot trees. They reported no sig-
nificant difference existed between LWA of peach and apricot trees of the same size and
shape. In the recent study, a novel color-depth fusion segmentation method (CDFS) for
collecting the LWA information was proposed [67].
After valuable feature extraction, features are combined to improve robustness when
training classifiers/models, which results in high dimensionality of features and the curse
of dimensionality. A screening and elimination of unnecessary features is essential to re-
duce feature quantity and increase data processing speed. Different types of algorithms
have been utilized to select effective features for orchard management, including stepwise
Figure 3. Typical flow-chart for canopy parameter measurement using different sensing systems to
support precision spraying.
3.2.1. Camera Sensor-Based Technologies for Precision Spraying
Camera-based sensing tries to mimic human perception to provide information or in-
puts based on processed image data to spraying systems needed for canopy characterization-
based pesticide applications. Image processing includes pre-processing, segmentation,
feature extraction, and classification. Image pre-processing eases the segmentation of the
targeted region (e.g., diseases, canopy, and stresses) from the crop images. It involves image
rotation, normalization, resizing, color transformation, denoising and contrast enhance-
ment, etc. Of these, color transformation is one of the most important for segmentation.
A detailed description, including the advantages and disadvantages of different color
spaces used in machine vision systems, is discussed by Cheng et al. [61]. The conversion
of different color spaces used in target detection including channels and transformation
function from RGB was thoroughly presented by García–Mateos et al. [62].
After image pre-processing, target area segmentation was carried out. With appro-
priate target area segmentation different types of targets (e.g., diseased area, canopy,
background, gaps between canopies, and so on) can be recognized for precision spraying
applications. Various features are being used to differentiate target regions in crops using
machine vision applications. Generally, these features are grouped into four sub-classes:
morphology, visual texture, spectral, and spatial contexts [63–65]. Among them, morpho-
logical and visual texture features are most popular and widely applied in orchards for
target detections. The extracted features will help the classifier know about the characteris-
tics of different symptoms from the captured images. LWA information and parameters,
including distance, area, shape, height, and density, were measured by Xiao et al. [66]
using a Microsoft Kinect system. The system consisted of an RGB camera along with a
monochrome complementary metal-oxide-semiconductor (CMOS) video camera and an
IR transmitter. The Otsu’s method was used to segment the acquired color images to
obtain binarized and segmented images from peach and apricot trees. They reported no
significant difference existed between LWA of peach and apricot trees of the same size and
shape. In the recent study, a novel color-depth fusion segmentation method (CDFS) for
collecting the LWA information was proposed [67].
After valuable feature extraction, features are combined to improve robustness when
training classifiers/models, which results in high dimensionality of features and the curse
of dimensionality. A screening and elimination of unnecessary features is essential to
reduce feature quantity and increase data processing speed. Different types of algorithms
have been utilized to select effective features for orchard management, including step-
wise discriminant analysis [68], quadratic discriminant analysis [69], linear discriminant
analysis [70], hybrid artificial neural networks-cultural algorithm (ANN-CA) [71], particle
swarm optimization (PSO) based differential evolution method [72], kernel principal com-

Sensors 2021, 21, 3262 8 of 29
ponent analysis (KPCA) [73], and so on. Upon locating/accessing the target to be sprayed,
spray volume at the nozzle is adjusted based on the size or quantity of the target present in
the images captured from the orchards.
Deep learning (DL), an artificial intelligence approach, is a new and emerging area
of machine learning that offers substantial potential for extracting complex structural
information [74] and has seen considerable use in agriculture in the last few years [75].
Compared to the conventional image processing technique, DL algorithms can learn the
features themselves from raw data, forming a hierarchy of higher-level features, while the
features need to be selectively extracted for conventional machine vision algorithms [76].
Another major advantage is that complex problems can be accurately solved with less time
due to the more complex models being used that allow for massive parallelization [77]. The
use of DL has been proven for disease detection, fruit recognition, and weed classification,
but has not been conducted for tree canopy characterization including the 3D-canopy
reconstruction, canopy density, and tree row volume measurements that are needed for
efficient tree fruit orchard spraying (to the best of authors knowledge). A few studies
conducted in the laboratory have used DL algorithms for precision spraying in weed
management [78], but very few DL algorithms have been used for spraying in tree fruit
orchards. Seol et al. [79] segmented leaf area for variable flow rate control using four
semantic segmentation-based deep learning algorithms including U-Net, SegNet, ICNet,
and DeepLab v3 and RGB-D cameras. The SegNet model outperformed three other models
and achieved 83.79% canopy segmentation accuracy. Chen et al. [80] conducted site-specific
pesticide spraying though Tiny-YOLOv3 deep learning algorithm using an embedded
unmanned aerial vehicle (UAV) system and achieved over 95% of pest control.
A few studies have used stereo camera vision-based systems to measure tree canopy
shapes and foliage densities and to detect tree crowns [81–83]. Malekabadi et al. [81]
computed disparity maps through stereo vision to assess the effect of canopy shapes
and densities of cherry artifact trees on the sensor performances. Ni and Burks [82]
reconstructed citrus tree using a stereo vision system to measure canopy geometry (tree
height, width, volume, and leaf cover). The stereo vision system was also used with an
UAV to reconstruct 3D tree crowns using watershed segmentation algorithms and local
area correlation coefficients [83]. The majority of the stereo vision systems were used for
field crop canopy characterizations such as corn and cotton; however, the evaluation of
these types of systems for tree fruits is still limited and require more investigations.
Camera Sensor-based Spraying: Application of camera sensors for precision spraying
in the tree fruit orchards was initiated when different image processing algorithms were
available and easily accessible. A summary of the camera sensor applications for variable-
rate spraying in orchards is listed in Table 3. Evaluation of camera sensors could be dated
back to 1980s but became more popular in 2000s, especially in agricultural applications. At
the early stage, a visible range, e.g., red-green-blue (RGB), camera-based machine vision
system was used by Thériault et al. [84] to measure the percentage of area covered by tree
canopies with a goal of increasing spray recovery in citrus orchards. Results reported spray
recovery in the citrus grove ranged from 20% to 45% depending on the size of trees and
wind directions. The recovery reached about 61% of the total volume sprayed when the
sprayer was tested in a stationary condition without any tree. Hočevar et al. [85] measured
the apple tree shape to develop a decision support precision spraying system using an RGB
camera and appropriate image analysis. Spraying was performed at a forward speed of
5.30 km·h−1 using a sprayer developed by modification-upgrading of a trailed air-assisted
sprayer (Agromehanika Kranj, Kranj, Slovenia), equipped with a piston pump and a
1000-liter tank. A total of 23% pesticide saving was calculated compared to spraying in
the control mode. Savings were presented in this study for an average tree height of 3 m;
however, the saving were not consistent for different fruit varieties with trees of varying
shape and size. Suggestions were made that changing the camera position and estimating
tree leaf density by scanning the trees may lead to better performance for the orchards with
inconsistent tree sizes. Only color information of images was used in their study. A few

Sensors 2021, 21, 3262 9 of 29
years later, Xiao et al. [66] captured both color and depth information to adjust and control
the spray intensity of sprayers and the dose of sprayed pesticides in orchards. Peach and
apricot trees had an average height of 2.5 m, a plant spacing of 3 m, and a row spacing of
approximately 4 m. The study revealed the proposed technique could be a key pathway to
control the spray intensity of nozzles and the dose of pesticides sprayed, but it was not
tested in fields for real-time spraying.
Table 3. Summary of camera sensor used in precision spraying in orchards.
Crops Sensors Detected Accuracy Chemical Saving Limitations References
Orange and
grapefruit
RGB camera Tree canopy Not reported
A saving of 22% to
45% was reported in
the citrus grove
The reported savings
could be achieved
only for spraying
small trees
[84]
Apple RGB camera Tree canopy Not reported
23% of saving of
pesticides
(0.96 L min−1 flow
rate reduction)
Size and shape
variability of trees
was not considered
for claiming the
amount of saving
[85]
Olive RGB camera Tree canopy
Greenness
detection was
not reported
Savings of up to 54%
in pesticide
usage compared
with conventional
continuous spraying
Reduction of
pesticides is only
considered in the
gaps between trees
[15]
Grapevine
Multispectral
camera
Powdery
mildew
disease
Detected about
85% to 100% of
the diseased area
A reduction of 65%
to 85% was reported
based on site-specific
spraying to the
diseased areas
False-positive of the
developed system
was from 5% to 20%
[86]
Grape RGB camera
Clusters and
foliage
Over 90%
accuracy was
achieved for both
cluster and
foliage detection
Reduction of 30% in
the use of pesticides
Algorithm
processing time was
longer and not
appropriate for
real-time application
[87]
Pear
Kinect RGB-D
camera
Fruit tree
Highest accuracy
of 83.79% was
reported using
SegNet model
Pesticide application
was reduced
up to 56.80%
Number of trials was
insufficient require
extensive
investigation
[79]
Litchi
Optical
camera
Pest detection
Up to 95.33%
average precision
was achieved
Reduce spray
volume by 87.5%
- [80]
A real-time site-specific orchard sprayer was developed by Asaei et al. [15] to save
pesticides in olive orchards based on the tree green canopy detection using a camera vision
technology. The experiments showed that the percent of sprayed liquid coverage ranged
from 64.62% to 86.42%, 38.52% to 62.50%, and 12.17% to 32.85% for 2, 3.5, and 5 km·h−1,
respectively, at different positions of the tree canopies. The difference of spray coverage
was reported due to the variation of the spatial lag (e.g., the resultant of timing delay and
forward speed of the sprayer) during spraying. The study concluded that the spatial lag is
influenced by the forward speed could result poor spray deposition.
Aside from tree canopy characteristics detection for spraying operation, few attempts
have been investigated based on disease detection. Disease control in grapevines using
a totally automatic selective spraying system was first introduced by Oberti et al. [86].
The system consisted of a six degrees of freedom CROPs manipulator, a disease detection
system and an end-effector for spraying operation. Three spectral channels were collected
including green (540 nm), red (660 nm), and NIR (800 nm) for powdery mildew disease
detection. Even though significant pesticide reductions were reported, the acceptance of
this technique in real-world applications requires further improvements, particularly in

Sensors 2021, 21, 3262 10 of 29
enhancing the ability to measure the latent infection areas around the detected symptoms.
On the other hand, Berenstein et al. [87] detected grape clusters and foliage to support
precision spraying and reported this can reduce 30% of pesticide usages. The edge detection-
based image processing approach was applied for filtering grape green pixels, which is
not suitable in field condition because the mask size of the grape is not uniform within the
orchards will increase false positive of detection. A few attempts reported in the current
year have shown promising potential for pesticide savings by using deep learning-based
variable rate spraying [79,80], but more research studies need to be conducted to evaluate
its potential more extensively.
For camera-based canopy parameter measurements, the light illumination is the
process of making tree canopy clearer or brighter for capturing or acquiring images. Direct
sunlight is used as a main source of light in most of the cases for field applications, and
under such environments shading and high contrast transitions of illumination are a
major problem. The illumination in the field usually varies with different times of a day,
the position of the sun concerning the object and overcast conditions which can greatly
affect image quality/characteristics [88]. For example, light illumination is comparatively
less in early morning and evening as compared to late morning, noon, and afternoon.
Bright illumination generally saturates the dynamic range of the camera, which can easily
deteriorate the color information of the tree canopy images. Consequently, when the
illumination decreases (e.g., low) especially in the early morning, evening, or cloud days,
an integral part of the object (tree canopy) surface showing is shaded and occluded which
creates significant differences in the diffuse part of bright illumination. The dynamic
visible range of a camera is guided by a technological upper limit that results in a sensed
image not suitable for processing when the object is irradiated by direct sunlight. Even
though the ability of the camera and lens has been improving to compensate for the
illumination variations, it is still a major challenge to in-field applications [89]. Apart
from light illumination, wind speed [90] and direction, ground speed [88], canopy overlap,
and distance between sensor and object [91], etc. are also performance-limiting factors in
machine vision applications that need to be addressed.
3.2.2. Range-Sensing Technologies for Precision Spraying
Ultrasonic Sensor-Based Technologies
Ultrasonic sensor systems are another sensing technology being used to detect the
tree canopy parameter to support precision spraying. These types of sensors are affordable,
robust, and handy tools that can be used to map target information during spraying [92,93].
To detect the object, sensors transmit waves towards the targets and receive the reflected
waves back from them (Figure 3). The ultrasonic sensors can detect the presence of the
targets and can measure the distance to the targets based on the time difference between the
sound wave being sent and reflected wave being received. The properties and structures
of the target are captured by assessing the strength of the reflection [94]. The ultrasonic-
sensing system includes an ultrasonic transceiver, an output signal generator, and an
amplifier. Various types of transceivers have been utilized and proved to be successful in
agricultural applications, including Prowave 400EP250 [95] and Prowave 400EP14D [14].
Stajnko et al. [95] reported a selective amplifier with a 40-dB gain in the frequency of 40 kHz
was effective for target spraying in the orchard. Canopy volume and target distance are
mainly measured by this sensing technique (Figure 4a).
Ultrasonic sensors for fruit tree volume measurement have been studied widely [97–99].
An ultrasonic system (Durand-Wayland Inc, LaGrange, GA, USA) consisting of 20 ultra-
sonic transducers and ranging boards (10 per side) was used to measure the canopy volume
of fifteen trees (heights: 1.7 to 3.3 m) [97]. The estimated tree canopy volumes reported a
good correlation of 0.90 with RMSE of 1.66 m3 compared to the manual measurements.
Experimenting with the same technology, ultrasonic sensors were utilized for tree vol-
ume measurement in citrus groves [93]. Various aged trees with different row spacings
and groves were measured and obtained a high correlation ranging from 0.95 to 0.99

Sensors 2021, 21, 3262 11 of 29
when compared with manual measurements and ultrasonic measurements. They also
found that the effect of forward speed was minor in dense canopy trees compared to the
light canopy trees, which was mainly attributed to the more uniform height, diameter,
and volume in the dense canopy trees, as well as more openings in the light canopies
which weakened the signal echo of the ultrasonic sensor and adversely affected the mea-
surements [98]. The same ultrasonic system was also tested on grapefruit trees with
10 transducers mounted at 0.6-m increments on a 0.1-m diameter vertical PVC mast for
tree canopy volume and height measurements [100]. No significant difference was found
between manual and ultrasonic measurement where R2 of 0.94 (RMSE = 0.16 m−3·tree−1)
and 0.94 (RMSE = 3.12 m−3·tree−1) were reported for height (2.1 to 4.3 m) and tree volume
(6.3 to 54.0 m3·tree−1) measurements. Ultrasonic-sensing system was used with a differen-
tial global positioning system (DGPS) to achieve a variable-rate nitrogen application by
generating a prescription map for citrus tree orchards [99]. Results showed the developed
system reduced 38% to 40% of fertilizer usages as compared to the uniform application of
270 kg N/ha/y. Palleja and Landers [101] used a variety of ultrasonic sensors to evaluate
canopy density in apple orchards with the goal of improving deposition and avoiding
drift. The signal obtained from sensors was highly correlated with an error of 3.8% in
apple trees; however, the study was not continued for the entire growing season because
of increasing apple tree size and the desire to evaluate the performance of the sensor in a
specific growing stage (i.e., young apple trees, BBCH: 75). The increase of variability in
tree size and shape reduces the accuracy of the ultrasonic sensors when measuring canopy
geometric information [102].
Figure 4. Measurement of canopy volume by using an ultrasonic sensor (a) and a laser sensor (b)
[96] (CC BY 4.0).
Ultrasonic sensors for fruit tree volume measurement have been studied widely [97–
99]. An ultrasonic system (Durand-Wayland Inc, LaGrange, GA, USA) consisting of 20
ultrasonic transducers and ranging boards (10 per side) was used to measure the canopy
volume of fifteen trees (heights: 1.7 to 3.3 m) [97]. The estimated tree canopy volumes
reported a good correlation of 0.90 with RMSE of 1.66 m3 compared to the manual meas-
urements. Experimenting with the same technology, ultrasonic sensors were utilized for
tree volume measurement in citrus groves [93]. Various aged trees with different row
spacings and groves were measured and obtained a high correlation ranging from 0.95 to
0.99 when compared with manual measurements and ultrasonic measurements. They also
found that the effect of forward speed was minor in dense canopy trees compared to the
light canopy trees, which was mainly attributed to the more uniform height, diameter,
and volume in the dense canopy trees, as well as more openings in the light canopies
which weakened the signal echo of the ultrasonic sensor and adversely affected the meas-
urements [98]. The same ultrasonic system was also tested on grapefruit trees with 10
transducers mounted at 0.6-m increments on a 0.1-m diameter vertical PVC mast for tree
canopy volume and height measurements [100]. No significant difference was found be-
tween manual and ultrasonic measurement where R2 of 0.94 (RMSE = 0.16 m−3·tree−1) and
0.94 (RMSE = 3.12 m−3·tree−1) were reported for height (2.1 to 4.3 m) and tree volume (6.3
3 −1
Figure 4. Measurement of canopy volume by using an ultrasonic sensor (a) and a laser sensor
(b) [96] (CC BY 4.0).
Ultrasonic Sensor-based Spraying: These types of sensors regulate the spray op-
eration based on canopy parameters measured through sound waves. A summary of
ultrasonic sensor applications for site-specific spraying is presented in Table 4. At the
initial phase, the performance of ultrasonic sensors was tested in orchard spraying by
Giles et al. [103]. The developed system adjusted the flow rate of the sprayer based on the
canopy size variations provided by the ultrasonic sensors. Spray savings ranged from 28%
to 35% and 36% to 52% in peaches and apples, respectively.
Balsari and Tamagnone [104] undertook an approach that was able to switch individ-
ual nozzles on and off using an ultrasonic-sensing system mounted on a ducted air-assisted
sprayer. The sensors were placed at different tree heights to measure canopy volume and
showed an average chemical saving of 58%. Using a similar approach, Moltó et al. [105]
developed a prototype able to control two electro-valves to apply chemical in two different
flow rates (e.g., turn off at the gaps between trees and adjust the valve flow rates according

Sensors 2021, 21, 3262 12 of 29
to tree canopy volume) and showed the system was able to save 30% of chemical use in
citrus trees. Saving was increased up to 37% when tested in both citrus and olive trees [106].
Table 4. Summary of ultrasonic sensor used in precision spraying in orchards.
Crops Detected Accuracy Chemical Saving Limitations References
Peach and apple Canopy foliage Not reported
28% to 35% for peaches and
36% to 52% for apples
Spray deposition was
reduced on some
canopy areas
[103]
Apple
Tree canopy
volume
Not reported Approximately 58%
The detected vegetation gap
width was between 0.35 and
1.20 m. Smaller gaps could
not be identified because of
the wide-angle field of view
of the sensors
[104]
Citrus Tree canopy Not reported
The system achieved 30% of
saving in time
Handgun sprayer was used
for spraying
[105]
Citrus and olive
Tree shape and
gap between
trees
Not reported Saving up to 37%
Only considered the gap
between trees to support
variable-rate spraying
[106]
Olive, pear,
and apple
Tree canopy
width
Not reported
Savings of 70%, 28%, and
39% were reported for the
olive, pear, and
apple, respectively
Droplets from the nozzle
did not follow a straight
trajectory which caused
lower spray deposition on
the tree canopy
[107]
Grape/vines
Tree row
volume
R2 = 0.99
for distance
between sensor
and crop
measurement
and R2 = 0.97
for leaf area
determination
Average of 58.8%; savings
were 83.9%, 32.7%, and
48.0% at the lower, the top,
and middle parts of the
crop, respectively
The experiments were
conducted at the very late
crop stage (BBCH 80:
ripening stage) where a
majority of the leaves was
large and uniform in size
compared to early and
middle stages, which caused
less variability
[108]
Grape
Tree row
volume
R2 of 0.66
was reported
for TRV
measurement
58% of application volume
Experiments did not
consider the effects of
ground speed
[109]
Apple
Contour of the
tree canopy
Not reported 20.2% per nozzle
The savings varied by the
size and training of
orchard trees
[14]
Apple
Distance
measurement
of apple
tree canopies
Average errors
of ±0.53 cm,
and ±5.11 cm
in laboratory
and field scales
Did not spray
Increase of variability in
field conditions significantly
reduced the accuracy
of the sensor
[102]
Pistachio
Volume
estimation of
tree sections
R2 value of 0.99
for training
and 0.96 for
testing data
was reported
using artificial
neural net-
work (ANN)
34.5% overall, 41.3%, 25.6%,
and 36.5%, for top, middle,
and bottom canopy
sections, respectively
The magnitude of chemical
savings was comparatively
lower than other studies,
especially in the center of
the trees
[110]
A prototype spraying system was tested in olive, pear, and apple orchards with an
electronic control system equipped with ultrasonic sensors [107], and reported chemical
savings from 28% to 70% compared to a conventional spraying technique. Gil et al. [108]
stated an average of 58% chemical savings could be achieved using an ultrasonic-sensing
system that adjusted to variations of grape structure for spray decisions, as compared
to the conventional uniform application. A few years later Llorens et al. [109] achieved

Sensors 2021, 21, 3262 13 of 29
mean saving of 58% by applying the ultrasonic-sensing technology in vineyards while
also achieving good leaf spray deposition. The nozzles were adjusted according to the
variability of tree canopy dimensions.
Maghsoudi et al. [110] utilized three ultrasonic ranging sensors to measure the distance
to the target at three different heights to direct precision spray in pistachio orchards. Canopy
volume estimation of tree sections was used to make spray decisions. The study reduced the
overall agrochemical usage of about 41.3%, 25.6%, and 36.5%, respectively, for the top, middle,
and bottom sections of the tree canopy. The magnitude of agrochemical savings was lower
due to a higher level of structural variations (e.g., changes of tree width, height, and canopy
density) present in pistachio trees. Escolà et al. [102] assessed the ultrasonic sensor performance
by measuring the distance to apple tree canopies under laboratory and field conditions. An
average error of ±0.53 cm was measured in laboratory conditions, and the accuracy declined
with the increase of variability in field conditions. The error of measurement increased when
the distance between sensors and target increased, which was due to apple canopies having
small surfaces that are difficult to measure at longer distances. Shorter distances between
the canopy and the transducer face can provide a stronger returning echo. Additionally, the
reflection of sound waves emitted by the ultrasonic sensor is greatly affected by the objects of
the measured plane and the directional angle [66].
Different trees have different canopy architectures and leaf angles, and the leaves
angle could easily change with the wind [14]. Zaman and Salyani [93] showed that canopy
foliage density has a significant effect on ultrasonic sensor measurements of canopy volume.
In addition, the performance of the ultrasonic sensors also depends on detection distance
and environmental conditions including temperature, and humidity [111,112]. Ultrasonic
sensors cannot perform better than laser scanner sensors for estimating canopy volume [97].
The major disadvantage of using an ultrasonic sensor is that the sensor has limited
penetration capability, which provides measurements with low spatial resolution. They
have limited detection range, and can be greatly affected by canopy detection distance,
travel speed, and outdoor weather conditions, which can limit the accuracy of the canopy
parameter measurements. Temperature fluctuation affects the speed of an ultrasonic sen-
sor’s sound waves. Sound waves travel faster to and from tree canopies with temperature
increases, which results tree canopies appearing closer than they really are. In addition,
due to not having scanning capabilities, the ultrasonic sensors provide significantly fewer
data compared to other sensing techniques including camera and LiDAR-based sensing,
making the ultrasonic technology outdated in precision agriculture as more advanced
technologies are already available [96].
Laser Sensor-Based Technologies
Laser sensing is an evolving technology mainly used in remote-sensing applications
and plant phenotyping that has shown potential in tree canopy parameter detection (e.g.,
canopy volume and distance of the target) in the last few decades. Compared to other
sensors, the major advantage of using a laser sensor for canopy volume estimation is that
its electromagnetic signal can penetrate the vegetation canopy to provide canopy density
information and the inner and outer structures of the tree [19]. The laser beams transmit
visible laser light through a lens towards the object. The laser light is reflected diffusely
from the outside of the object and a recipient point of convergence on the sensor focuses
that reflected light, making a spot of light on the direct imager. The laser bars receive a
variable number of recognized points for each yield from the object cut as indicated by the
distance to the sensor and the angle from the horizontal. The basic flow-chart is presented
in Figure 3. The sensing process begins with collecting point cloud data using a laser sensor.
The raw data accounts for the ground information as well, which must be removed by
setting a region of interest. Tree canopy points need to be sorted by removing unnecessary
points coming from unwanted regions. The methodology of using a laser sensor for canopy
volume measurement is well described by [55,113]. A schematic of the laser-sensing system
for canopy characterization is presented in Figure 4b.

Sensors 2021, 21, 3262 14 of 29
Laser scanner sensors can acquire the information and specifications of tree and fruit
more precisely than ultrasonic sensors [114–116]. Studies reported promising results using
laser scanner sensors with canopy volume measurements close to manual measurements.
Rosell et al. [117] used a laser scanner system to obtain three-dimensional (3D) structural
characteristics of trees in the orchard. The study achieved an accuracy of ±15 mm in a
single-shot measurement using a 3D LiDAR sensor with a maximum scanning angle of
180◦ and a 5-mm standard deviation in a range of up to 8 m. In addition, the correlation
coefficient was as high as 0.976 between manually measured volumes and those obtained
from the 3D LiDAR models. Another recent study showed that a 3D LiDAR-sensing
system could segment the tree canopy, trunk, and trellis system for a trellised high-density
apple orchard [118]. The generated canopy density and depth maps could be used in
orchard operations such as precision spraying and mechanical pruning. Grau et al. [119]
calculated 3D vegetation density using 3D terrestrial LiDAR, and reported the good R2
of 0.91 with RMSE of 20% for a surface density up to 2 m2·m−3. The study revealed the
error increased with angular resolution of LiDAR and vegetation density. Li et al. [120]
used the high-resolution terrestrial LiDAR to measure leaf area density of individual trees
and reported lower accuracies of 86.53% and 84.63% for two trees due to the application of
inappropriate voxel size. Hosoi and Omasa [121] also measured tree leaf area density using
high-resolution LiDAR and achieved errors of 17% due to high obstruction of laser beams.
The study reported the inclination of the laser beams could provide less obstruction and
better penetration of laser beams into the canopies, yielding better leaf density measure-
ments. Rosell Polo et al. [115] utilized a 2D LiDAR to measure tree row volume and the
total crop surface area. A strong R2 of 0.9738 was reported between the LiDAR measured
plant volume and the tree volume measured manually. Therefore, the above studies con-
cluded that the LiDAR-sensing system could be a powerful technique for non-destructive
estimates of canopy foliage area characteristics and canopy density measurements of trees.
LiDAR-sensing technologies are also widely utilized for determining spray drift and buffer
zones in 3D crops [122] showing that LiDAR sensor could be able to determine the buffer
zone in grape, citrus, peach, and apple orchards.
Laser Sensor-based Spraying: In the last decade, LiDAR-based laser-sensing tech-
nologies have been tested in fruit orchards due to their high precision and accuracy in
real-time measurements [12,123]. A summary of laser sensor applications for site-specific
spraying is presented in Table 5. Although laser-sensing technology was extensively in-
vestigated for sprayer development before the 2010s, it was first integrated successfully
into a precision sprayer by Chen et al. [124]. This system measured tree canopy structure
and adjusted control nozzles based on the height and width of the target trees. There were
acceptable variations in coverage inside the tree canopies, with coefficient of variations
(CV) ranging from 52% to 79%, 27% to 53%, 11% to 33% in the X, Y, and Z directions,
respectively. However, the main restriction of this study was that spray volume, spray
coverage, and uniformity inside canopies diminished as the movement speed increased
from 3.2 to 6.4 km·h−1, because the necessary spray output surpassed the nozzle limit
at a higher speed. The authors concluded that the measurement of different canopy fo-
liage structures and densities need to be accounted for when evaluating the variable rate
sprayer performance.
Another major limitation in using a laser sensor in this attempt was that it required two
sensors for real-time detection of trees on both sides of the tree row due to the maximum
radial scanning range of 180◦ in the laboratory-based evaluation. Considering the scanning
limitations, Liu and Zhu [12] measured the dimensions of target surfaces with complex
shapes and sizes using a 270◦ radial range laser sensor to develop a variable rate sprayer.
The surface/boundary of the trees and distance between the sensor and object were
measured. The performance was better during laboratory-scale tests with artificial trees
compared to field-grown ornamental trees. However, the study did not consider the canopy
volume of the trees, which is very important to accurately measure the tree architecture,
with this being a major limitation of this study.

Sensors 2021, 21, 3262 15 of 29
Table 5. Summary of laser sensor used in precision spraying in orchards.
Crops Detected Chemical Saving Limitations References
Apple and peach
Tree canopy
foliage volume
52.4% for apple and 34%
for peach
Canopy density characteristics
were not considered, resulting in
more chemical savings in smaller
trees compared to larger and
denser trees
[126]
Peach
Tree canopy
volume
50%, 40%, and 13% at bloom, pit
hardening, and final
swell, respectively
Spray coverage was not good at
the final swell
[16]
Apple
Tree canopy
foliage volume
Two year average of
60.5% by volume
Only trees with small canopies
were tested
[128]
Apple
Tree height,
width, volume,
foliage density
47% to 73% at the growth stages
of leafing, half-foliage,
and full foliage
Uniform chemical spray
coverage and deposition were
reported along with the canopy
axes of depth, width, and height
[49]
Apple
Tree canopy
height, width,
and foliage
density
Average of 68% to 90% on the
ground, 70% to 92% around tree
canopies, and 70% to 100% of
airborne spray
The results were not consistent,
and variations were reported
between half-foliage and
full-foliage stages
[125]
Apple Canopy volume Approximately 46%
Experiments did not consider
different growth stages
[129]
Crab-apple
Tree size, shape,
and leaf density
Spray coverage on the foliage of
trees was 19.86% ± 3.0%
Experiments were only
conducted in nursery
field conditions
[130]
Maple
Canopy density
and tree shape
Spray area coverage was
increased by 30% to 55%
Spray coverage was higher on
the front side as compared to the
backside position
[131]
Artificial and
ornamental trees
Shape and
size of trees
Did not spray
Performed better with artificial
trees than with ornamental trees
[12]
Apple Tree canopies
Reduced pesticide costs
by 60–67%
This study only compared the
economics of variable-rate and
constant-rate sprayers
[127]
Using the same sensor configuration as Chen et al. [124], apple tree canopy character-
istics including height, width, volume, foliage density, and occurrence were measured to
control nozzles for real-time orchard spraying [49,125]. Three different phenological stages
(e.g., leafing, half-foliage, and full-foliage) were tested. Among the three growth stages,
the precision sprayer provided more consistent results on spray coverage and deposition
during the leafing stage compared to the half-foliage and full-foliage stages. A few years
later, Chen et al. [125] utilized the same laser-guided variable-rate spraying system for
controlling insects and diseases in ornamental nurseries. The developed system provided
equivalent or greater control of insect and disease as compared to the conventional sprayer.
Boatwright et al. [16] compared a LiDAR-based variable rate sprayer system with
a conventional spraying approach for disease and pest control management in a peach
orchard. They reported the LiDAR-based precision sprayer could have the same amount of
control with chemical savings ranging from 13% to 50% at various phenological stages (i.e.,
bloom, pit hardening, and final swell) of the trees. The spray coverage was increased at the
bloom (50.13%) and pit hardening (26.67%) stages, but no improvement was noticed in the
final swell stage.
In the recent study, Chen et al. [126] conducted a comparative analysis between
intelligent sprayers using a laser sensor and constant-rate conventional sprayer for chem-
ical usage and pest control. The pest severity of the apple trees were rated, including
codling moth, powdery mildew, scab, and oriental fruit moth. This study reported similar
findings, where the pest control performance was also equivalent to or greater than the
conventional method.

Sensors 2021, 21, 3262 16 of 29
Manandhar et al. [127] conducted an economic analysis between laser-guided variable-
rate sprayers and conventional constant-rate sprayers for spraying in the apple orchards.
They concluded a potential pesticide cost savings between $1420 and $1750 ha−1 is possible
along with reductions of spraying time by 27–32% and labor and fuel by 28%.
Apart from the chemical saving, Shalal et al. [123] detected apple tree trunks using a
laser scanner sensor and achieved 96.64% of detection accuracy. The study revealed the
laser scanner sensors are capable of providing reliable angles and width of tree trunks and
information on nontree objects. Overall, the results of the above studies were satisfactory,
but the extrapolation of these results to trees with different structures is not easy.
Challenges are also associated with laser sensing, especially when vibrations from
terrain variability are presented during scanning for tree canopy data. Inconsistencies
of terrain could cause deviations of the angular orientation of the LiDAR sensor during
examining/scanning [112], which could be corrected using a positioning sensor such as
inertial measurement unit (IMU) sensor. These types of sensors acquire huge datasets
during scanning of the trees that require a high level of analysis and interpretation. They
do not work well in areas where there are high sun angles or huge reflections due to the
dependency of the principle of reflection. In addition, the LiDAR pulses may not be able to
penetrate through dense canopies, especially in forests, and can provide incomplete data,
which should be considered for future sprayer development.
3.3. Conclusions for Sensor-Based Precision Sprayers
Estimating canopy size is a challenge because of the confounding structures and irregular
shapes of trees, and variable environmental conditions in fields. Problems associated with
camera vision sensors in field conditions include variable light, and distorted or blurred im-
ages due to vibrations and wind direction and velocity [15], which can easily influence the
performances of the system. Consequently, the major drawback of ultrasonic sensors is the
large angle of divergence of ultrasonic waves that also needs to be calibrated. In addition, very
uneven fields generate inconsistent data [60]. Apart from vision and ultrasonic sensors, the
spectral and infrared sensors are also not feasible to detect tree canopy structure information
in tree fruit orchards due to being highly sensitive to outdoor illumination variations and
environmental conditions [60], and are not commonly used in precision spraying. Compar-
ative to other sensing systems, a laser scanner sensor could be a viable option to measure
canopy structure characteristics aimed at precision spraying in the orchards. Environmental
conditions do not influence laser sensors and can provide more accurate detection of tree
canopy structure [117,125]. The advantages and disadvantages of different sensing systems
used in agriculture for variable-rate spraying are summarized in Table 6.
Although several groups of researchers have developed prototypes to change the ap-
plication flow rate based on canopy structural parameters from different sensors, precision
spraying in tree fruit orchards needs further development to deal with excessive chemical
usages, improper spray depositions, and losses from chemical drift [16]. Real-time preci-
sion orchard sprayer development is a challenging endeavor due to outside, uncontrollable
environmental conditions. A laser sensor could be a solution to the problems associated
with accurate canopy structure information measurement. Still, the temperature, humidity,
wind speed and direction, and land topography have direct effects upon spray deposition
to the target that needs to be accounted for. For example, high temperature and lower
humidity can cause faster evaporation of the spray drift before reaching the target.

Sensors 2021, 21, 3262 17 of 29
Table 6. Advantages and disadvantages of the different types of sensor applications for precision spraying (modified from [60]).
Sensors Pros Cons
Camera sensors
• Provide information about crop diseases, pests,
weeds, and nutrient deficiencies
• Ability to measure tree canopy area
• Less expensive
• Highly sensitive to the illumination conditions
• 3D reconstruction of tree canopies is
very difficult
• Weather conditions have significantly affected
the sensor performance
Ultrasonic sensors
• Robustness and low price
• Capable of determining tree canopy
structure characteristics
• Relatively easy to implement
• Limited resolution and accuracy
of the measurements
• Detection distance and environmental
conditions can affect sensing accuracy
• Require multiple sensors to sense
plant structure
LiDAR sensors
• Independent of environmental conditions
• Rich data acquisition capability
• High speed of measurement
• Provide high resolution of tree canopy
structure characteristics
• Plant data, for example, height, width, volume,
leaf area index, and canopy density, can be
acquired with adequate precision
• Tractor bouncing can affect data acquisition
which requires correction
• Delicate moving part inside the sensor
4. Other Factors Affecting Performance in Spraying
The effectiveness and quality of spraying depends on many factors, including spray
deposition, drift, leaf absorption, and coverage volume. Spray penetration and coverage
are greatly influenced by tree height, tree spread, and the continuity of canopies along
rows which may easily reduce the effectiveness of the crop protection operation [132]. In
this section, we discuss additional factors that affect spraying performance.
4.1. Sprayer Specifications
4.1.1. Travel Speed
Several studies have analyzed the effect of forward speed on spray deposition and
drift [93,133]. Salyani [134] reported an inverse relationship between deposition and travel
speed, and deposit uniformity and travel speed. Others reported a non-significant effect
of travel speed on spray deposition for variable-rate sprayers using ultrasonic sensors
for canopy detection [93,111,135], although the uniformity of deposition decreased as the
speed increased due to bent and distortion of air-jet [133]. Whitney et al. [136] reported an
enhanced deposition on the upper leaf surface with increasing forward speed of air blast
sprayers in citrus, but no significant increase in the deposition was reported on the lower
leaf surface. The results did not correspond to other studies, which may be due to the depth
of the sample collection as the higher canopy density results in less spray deposition. As
the distance from the sprayer increases, the amount of deposition and uniformity decreases.
The spray penetration into the canopy is influenced by the interaction of wind velocity and
sprayer ground speed as it affects the air jet generated by the sprayer [135] and reduces
the spray penetration and uniformity with increasing travel speed. However, all these
reported studies indicated that both the penetration and uniformity are low at the higher
travel speed; therefore, the travel speed should be considered for applications where the
spray uniformity and canopy depth is critical.
4.1.2. Distance
The distance between sprayer and canopy is another important parameter that influ-
ences the amount of spray drift [137]. Celen et al. [138] reported that increasing the travel
speed and sprayer-canopy distance increases the spray drift, causing less deposition on
the target. The droplet size of chemicals is important in understanding potential spray
drift. Although smaller droplets can potentially enhance canopy coverage, they are more

Sensors 2021, 21, 3262 18 of 29
prone to drift to non-target areas [138]. Derksen and Gray [139] reported the influence of
the distance between the spray outlet and canopy on spray deposition and drift using air
blast sprayers. Increasing the distance between the sprayer and the target canopy resulted
in decreased deposition levels in trees. Wandkar et al. [133] reported that deposition on
guava trees was significantly affected by canopy depths at different travel speeds. This
was due to the position of the canopies of the trees, with maximum spray deposited on
the upper leaf surface. Keeping the nozzles close to vegetation can reduce the potential of
spray drift without affecting the uniformity of the coverage and spray pattern [140].
4.1.3. Mechanical Fan and Air-Flow Control
The mechanical system performing the spraying operation also affects the spray
deposition and off-target losses in orchard trees. Salyani [134] reported an increase in
spray deposition on targets in an open area using an axial fan air blast sprayer at a higher
travel speed. Steinke et al. [141] used tower air blast sprayers to compare the effect of
different fans on spray deposits and reported a more uniform deposition using crossflow
fans than a conventional axial fan. Similarly, using an air blast sprayer with horizontal
delivery resulted in increased spray deposition and low off-target drift compared to the
axial flow air system [142]. Pergher and Gubiani [143] reported a high spray deposition
(54–57%) in vineyards with a combination of lower air flow rate and lower spray volume.
Wandkar et al. [133] reported that spray deposition increases with increasing air jet velocity
in the dense canopy, while Van de Zande et al. [144] reported higher losses to deposition
on the ground with air-assisted sprayers as compared to conventional boom sprayers.
4.1.4. Nozzles
The spray drift is also influenced by the type, size, and pressure of the nozzles used for
spraying. Spray nozzles should be selected by following the required spray volume [143],
nozzle type, and pressure [145] to improve the coverage and penetration into the canopy.
Wandkar et al. [133] compared two different spray nozzles (hollow cone and flat fan nozzles)
and concluded that the nozzle with smaller droplet size (hollow cone nozzle) shows higher
deposition. Studies conducted by Zhu et al. [146] reported a higher spray deposition on the
lower part of the canopy due to a greater percentage of large size droplets produced by the
air-assisted nozzle as compared to hollow-cone nozzles, while [145] conducted a field study
using a crossflow orchard sprayer and reported the drift reduction from air-inclusion nozzles
compared to hollow-cone nozzles is significant beyond 8 meters from the last row of the
orchard. Considering the effects of nozzles in spray drift losses, a nozzle classification system
was developed to identify the drift reduction potential of spray nozzles used in fruit crop
spraying [147]. The study reported the highest drift reduction of 97% could be obtained with
a nozzle (Albuz TVI 80025) based on V100 (volume of drops smaller than 100 µm) when
sprayed at 7-bar spray pressure. The suggested threshold nozzles for these drift reduction
classes in orchard spraying are, respectively: TeeJet DG8002 (drift reduction of 62%), Albuz AVI
80015 (drift reduction of 74%), and Albuz TVI 80025 (drift reduction of 97%), all at 7-bar spray
pressure, as well as Lechler ID 9001 (drift reduction of 91%) at 5-bar pressure. Another study
revealed that the V100 achieved a high correlation (up to R2 = 0.948) with the drift potential
tested with the wind tunnel for hollow-cone nozzles [148].
4.2. Meteorological Condition Effects
4.2.1. Wind Speed and Direction
Among the atmospheric factors, the wind speed and direction have tremendous effects
on the spray depositions causing off-target agrochemical losses to the non-target receptors,
including damage to the neighboring crops, water, and animals. The rate of off-target
losses depends on the speed of the wind and direction of the wind flow (Figure 5).

Sensors 2021, 21, 3262 19 of 29
surface were 11%, 15%, and 23% for the full leaf, intermediate, and dormant stages, re-
spectively, while spraying from 5-m apart using a crossflow fan sprayer. The functions
and its parameters for the three growth stages are: dormant: Y = 38.797 × 10−0.104X; interme-
diate: Y = 26.928 × 10−0.124X; full leaf: Y = 19.036 × 10−0.118X; where Y is spray drift deposition
(% of sprayed volume) at distance X m from the last tree row.
(a) (b)
Figure 5. Effect of wind speed (a) and direction (b) in spraying using CFD analysis [149] (used
with permission).
4.2.2. Temperature
Although the effects of temperature on spraying are region-specific (e.g., mainly oc-
cur in temperate/hot region), there is a slight deviation of the spray drift potential ob-
served by the researchers due to evaporation [151,152]. Water is the most used liquid for
mixing with agrochemicals to be applied in crop protection applications. Due to the char-
acteristics of water, the surface tension of water decreases significantly with increasing
temperature, which results in the decreasing size of the spray droplet. Holterman [151]
reported that high air temperature could cause evaporation of spray drift and make them
drift-prone by lessening the droplet size using a flat fan nozzle. Downer et al. [153] made
a similar conclusion where a decrease of the spray droplet size with increasing tempera-
ture was also reported, and, meanwhile, the authors observed the surface tension of all
spray liquids was also decreasing. To access the effect of temperature, Miller and Tuck
[154] used a flat fan and an air induction nozzle and noticed the temperature had a direct
effect upon the spray droplet size. Despite the temperature effect being slight compared
to the other factors, the effect could be significantly impacted by hot regions, especially in
the summer months.
4.2.3. Relative Humidity
Humidity is another region-specific factor that affects parameters by decreasing
spray droplet size, especially in the lower humidity regions [151]. Humidity, along with
high air temperature, affects the evaporation of liquid droplets [151,155]. Hanna [155] re-
ported the evaporation rate of spray droplets is comparatively faster when the humidity
level is low, and air temperature is high. The reduced droplets are more susceptible to off-
target movement with prevailing winds as they become entrapped in ambient air currents
[155] (Figure 6). Studies conducted to compute the ideal condition for spraying consider-
ing different meteorological conditions. An absolute humidity of 8 g·kg−1 along with a 16
°C air temperature and a wind speed of 3 m·s−1 are considered as ideal meteorological
conditions [152].
Figure 5. Effect of wind speed (a) and direction (b) in spraying using CFD analysis [149] (used
with permission).
In general, all types (class-based on sizes) of droplets have the chance of being off-
target depositions, but the small droplets have the highest possibility of the off-target
movement. Endalew et al. [149] utilized a computational fluid dynamics (CFD) based
simulation model to evaluate the effect of wind speed and direction on sprayer airflow.
A reduction of 2 m·s−1 airflow was reported from a two-fan air-assisted sprayer when
there was a crosswind speed of 5 m·s−1 with the direction of 90◦ that could significantly
affect spray deposition. In addition, a new model of bystander and resident exposure was
developed to measure spray drift deposition from orchard applications [150]. Observations
were made in three growth stages (dormant (BBCH 0-60), intermediate (BBCH 61-73
and 93-0), and full leaf (BBCH 74-92)) of the apple trees. The spray drift depositions on
soil surface were 11%, 15%, and 23% for the full leaf, intermediate, and dormant stages,
respectively, while spraying from 5-m apart using a crossflow fan sprayer. The functions
and its parameters for the three growth stages are: dormant: Y = 38.797 × 10−0.104X;
intermediate: Y = 26.928 × 10−0.124X; full leaf: Y = 19.036 × 10−0.118X; where Y is spray drift
deposition (% of sprayed volume) at distance X m from the last tree row.
4.2.2. Temperature
Although the effects of temperature on spraying are region-specific (e.g., mainly occur
in temperate/hot region), there is a slight deviation of the spray drift potential observed
by the researchers due to evaporation [151,152]. Water is the most used liquid for mixing
with agrochemicals to be applied in crop protection applications. Due to the characteristics
of water, the surface tension of water decreases significantly with increasing temperature,
which results in the decreasing size of the spray droplet. Holterman [151] reported that
high air temperature could cause evaporation of spray drift and make them drift-prone
by lessening the droplet size using a flat fan nozzle. Downer et al. [153] made a similar
conclusion where a decrease of the spray droplet size with increasing temperature was also
reported, and, meanwhile, the authors observed the surface tension of all spray liquids was
also decreasing. To access the effect of temperature, Miller and Tuck [154] used a flat fan
and an air induction nozzle and noticed the temperature had a direct effect upon the spray
droplet size. Despite the temperature effect being slight compared to the other factors, the
effect could be significantly impacted by hot regions, especially in the summer months.
4.2.3. Relative Humidity
Humidity is another region-specific factor that affects parameters by decreasing spray
droplet size, especially in the lower humidity regions [151]. Humidity, along with high
air temperature, affects the evaporation of liquid droplets [151,155]. Hanna [155] reported
the evaporation rate of spray droplets is comparatively faster when the humidity level is
low, and air temperature is high. The reduced droplets are more susceptible to off-target

Sensors 2021, 21, 3262 20 of 29
movement with prevailing winds as they become entrapped in ambient air currents [155]
(Figure 6). Studies conducted to compute the ideal condition for spraying considering
different meteorological conditions. An absolute humidity of 8 g·kg−1 along with a 16 ◦C
air temperature and a wind speed of 3 m·s−1 are considered as ideal meteorological
conditions [152].
Figure 6. Effect of humidity and temperature on spray droplet size and movement.
4.3. Interaction of Pesticides and Leaves
4.3.1. Droplet Size
Several factors influence spray coverage, including droplet size, carrier rate, and for-
mulation [137]. A different group of researchers worked in laboratories to demonstrate
the effect of droplet size on the spray’s biological efficacy and reported improved insect
control from using smaller droplet sizes [156,157]. Salyani and Cromwell [7] reported re-
duced spray drift in a laboratory study using drift retardants, and further studies agreed
as the median diameter of spray increases using drift retardants [158]. However, extensive
care and time should be considered to properly blend drift retardants with spray carriers
[159].
4.3.2. Retention
Retention of spray droplets on leaf surfaces depends both on the characteristics of the
surface and of the liquid and is critical for effective spraying operation. Spray retention is
significantly reduced when the velocity of spray droplets is higher due to high nozzle
pressure [160]. The droplet also tends to bounce when applied at higher pressure and ve-
locity. The retention of spray deposits on the surface of horizontal and vertical canopy
surfaces is crucial. Feng et al. [161] reported retention efficiency with the application of
fine, coarse, and medium-size droplets as 47%, 38%, and 37%, respectively. Dorr et al.
[160] reported that the droplet size over 400 micrometers is likely to shatter off the sur-
faces, which explains why high spray deposition was reported at a higher pressure. The
authors explained it might be because the velocity is high at higher pleasure, which re-
sulted in high deposition. Spillman [162] noticed the highest deposition efficiency on
leaves for droplets size greater than 250 micrometers, while small size droplets (20–50
micrometers) have better deposition efficacy on the vertical surface, including lower leaf
surface.
5. Discussion and Future Directions
This review has discussed the current methods of spraying in orchards, machine vi-
sion applications for precision spraying, implementation of sensor-based precision spray-
ing systems in orchards, and limitations of different sensors and corresponding chal-
lenges. The tree fruit industry has gained considerable attention due to its high usage of
agrochemicals in orchards. In the last few decades, various type of spraying systems, in-
cluding handguns, boom sprayers, polar jets, tunnel air blast sprayers, and so on have
been tested in orchards to try and reduce chemical usage; however, none of them was
fully successful and satisfactory due to non-uniform tree canopy structures and sprayer
limitations.
Figure 6. Effect of humidity and temperature on spray droplet size and movement.
4.3. Interaction of Pesticides and Leaves
4.3.1. Droplet Size
Several factors influence spray coverage, including droplet size, carrier rate, and for-
mulation [137]. A different group of researchers worked in laboratories to demonstrate the
effect of droplet size on the spray’s biological efficacy and reported improved insect control
from using smaller droplet sizes [156,157]. Salyani and Cromwell [7] reported reduced
spray drift in a laboratory study using drift retardants, and further studies agreed as the
median diameter of spray increases using drift retardants [158]. However, extensive care
and time should be considered to properly blend drift retardants with spray carriers [159].
4.3.2. Retention
Retention of spray droplets on leaf surfaces depends both on the characteristics of the
surface and of the liquid and is critical for effective spraying operation. Spray retention
is significantly reduced when the velocity of spray droplets is higher due to high nozzle
pressure [160]. The droplet also tends to bounce when applied at higher pressure and
velocity. The retention of spray deposits on the surface of horizontal and vertical canopy
surfaces is crucial. Feng et al. [161] reported retention efficiency with the application of
fine, coarse, and medium-size droplets as 47%, 38%, and 37%, respectively. Dorr et al. [160]
reported that the droplet size over 400 micrometers is likely to shatter off the surfaces,
which explains why high spray deposition was reported at a higher pressure. The authors
explained it might be because the velocity is high at higher pleasure, which resulted in
high deposition. Spillman [162] noticed the highest deposition efficiency on leaves for
droplets size greater than 250 micrometers, while small size droplets (20–50 micrometers)
have better deposition efficacy on the vertical surface, including lower leaf surface.
5. Discussion and Future Directions
This review has discussed the current methods of spraying in orchards, machine vision
applications for precision spraying, implementation of sensor-based precision spraying
systems in orchards, and limitations of different sensors and corresponding challenges.
The tree fruit industry has gained considerable attention due to its high usage of agro-
chemicals in orchards. In the last few decades, various type of spraying systems, including
handguns, boom sprayers, polar jets, tunnel air blast sprayers, and so on have been tested
in orchards to try and reduce chemical usage; however, none of them was fully successful
and satisfactory due to non-uniform tree canopy structures and sprayer limitations.

Sensors 2021, 21, 3262 21 of 29
Although various machine vision approaches have proved to be very useful and success-
ful in weed detection, crop stress monitoring and yield prediction, etc., challenges still exist,
especially in outdoor field conditions. Among the different types of sensors used in machine
vision applications, camera sensors are more susceptible to outdoor lighting and weather con-
ditions. The camera vision approaches might be best suited for spot spraying (spray based on
site-specific disease or pest pressure) rather variable-rate spraying in the tree fruits. Recent de-
velopments of multispectral and hyperspectral cameras offer narrower spectral bands that can
penetrate into the canopies and accurately measure the conditions (diseases or pest pressures
and stresses because of nutrients) of trees. However, longer data processing time (particularly
for hyperspectral cameras) is a major limitation. Current inventions of deep learning-based
artificial intelligence systems resulted in good canopy detections and segmentations. Semantic
and instance segmentation based deep learning models, including SegNet, U-Net, DeepLab v3,
mask-region-based convolutional neural networks (RCNN), and so on, could be the best fit for
canopy area segmentation; however, more efforts and extensive investigations are expected
to develop an effective and robust system for precision spraying. Apart from camera sensors,
ultrasound and laser scanner sensors can overcome light illumination problems usage sound
waves and electromagnetic signals, respectively, as inputs to measure detection distance and
gather canopy structure parameters. As compared to ultrasonic and camera sensors, laser
scanner sensors are more accurate and reliable in collecting crop structure information, are not
affected by weather conditions, and can guide the sprayer unit efficiently and effectively to
apply pesticides on target.
The application of laser scanners has risen during the last decade in multiple agri-
culture applications, from automatic tree height measurement to site-specific pesticide
application. However, challenges are still presented, especially in site-specific or variable-
rate spraying in tree fruit orchards. Accurate canopy information measurements require
automatic adjustment of laser sensor orientation during data acquisition, especially in
conditions of different sloping and uneven terrains. Therefore, a real-time terrain mon-
itoring system may need to be incorporated along with a sensing system for precision
spraying. Terrain monitoring systems can be developed using a positioning sensor and a
real-time kinematic global positioning system (RTK-GPS). An inertial measurement unit
(IMU) sensor can be incorporated into the spraying system to monitor the terrain con-
ditions in real-time and adjust the laser orientation accordingly. Studies reported IMU
sensors could minimize positioning errors of the laser from terrain variabilities [96,112].
The RTK-GPS will locate the uneven terrain position and guide the spraying system to
follow the co-ordinate where the necessary adjustment is needed for accurate canopy
information. Few studies have considered uneven terrain information for correcting tree
canopy information [163,164], so more studies need to be conducted to implement real-time
correction of tree canopies for accurate spray deposition.
Laser scanner sensors only help with measuring canopy structure information, but
a successful spraying operation requires a series of tasks to be completed accurately,
including maintaining constant travel speed, adjustments of the fan speed at the sprayer
unit, assessment of real-time weather conditions, prediction of droplet sizes and some other
factors. Auto-guidance systems that automatically steer the sprayer throughout the orchard
blocks could help maintain constant travel speeds. Crossflow fan sprayers reduce off-target
spray deposition because they have more appropriate nozzle positions and uniform air
delivery. An automatic damper system can be added at the sprayer fan’s air inlet (back
of the sprayer) to control the airflow based on tree canopy information (Figure 7). A
mathematical model will need to be developed to calculate the airflow required for sending
the spray droplet to the desired canopies.

Sensors 2021, 21, 3262 22 of 29
Figure 7. Position of damper installation for precision sprayer airflow control.
Real-time weather assessment is also important to reduce off-target deposition. Wind
can easily blow spray from targeted to non-targeted regions. The real-time control of noz-
zles based on weather data is not currently practical during spray operation. The future
sprayer also needs to include a droplet size prediction model to determine the diameter
of droplets based on the characteristics of the tree canopies during spray operations. Elec-
trostatic sprayers have provided improved deposition of specific droplet sizes and may
be a good aid to develop next-generation sprayer systems [165]. Electrostatic sprayers ap-
ply a positive charge to liquids as they pass through the nozzle. The positively charged
liquids are attracted to negatively charged surfaces, which allows for efficient coating of
hard nonporous surfaces. A 44% increase in mean deposition using the electrostatic sys-
tem reported by Pascuzzi and Cerruto [166]. Some other factors including canopy condi-
tions and spray droplet retention depend heavily on the tree canopies and cannot always
be addressed by the spraying unit. However, the nozzle effect can be resolved by match-
ing appropriate nozzles with electronically controlled valves.
The future of precision spraying systems will focus on integrating a suite of robotic
and sensor-guided spraying systems with fruit tree growth and structure information to
ensure accurate dose of pesticide delivery and reduce off-target deposition. A wide vari-
ety of sensor-based spraying technologies are available to measure the amount of spray
volume needed; however, it is also important to assess the durability of these technologies
so that a variety of sensors can be added at reasonable price for the farmers. Concurrently
with sensor development, site-specific disease, pests, and stress monitoring using tech-
nologies such as deep learning and remote sensing will improve the functioning of current
sensor-based spraying systems. Fusing/integrating different sensors could help to add
more decision-making features for more precise pesticide applications in tree fruits. Fu-
ture spraying systems must also handle diverse agrochemical formulations beyond that
of the standard aqueous mixtures currently used. Although tractor-based spraying sys-
tems are mostly used for agrochemical applications, an alternative UAV based application
method could displace traditional sprayers. UAVs have been tested in the field crops for
disease scouting, stress monitoring, and spraying, and its preliminary results for spraying
is showing promise. But most of the current UAV based spraying technologies may not
be suitable for spraying in large scale tree fruit orchards due to their limited payloads and
variable spray deposition capability. More research and development into UAV swarms
and large capability UAV similar to unmanned helicopters could result in more effective
system for commercial/large scale fruit orchards. Detection of disease and stress, pest
pressure are other avenues for their use in tree fruits production besides variable-rate
spraying. Therefore, the integration of multiple sensing and control systems will be the
key to using advanced precision sprayers in the future.
6. Conclusions
The excessive use of agrochemicals by conventional sprayers has gained attention
among orchard growers, practitioners, and ecologists. Serious efforts are required to make
Figure 7. Position of damper installation for precision sprayer airflow control.
Real-time weather assessment is also important to reduce off-target deposition. Wind
can easily blow spray from targeted to non-targeted regions. The real-time control of
nozzles based on weather data is not currently practical during spray operation. The future
sprayer also needs to include a droplet size prediction model to determine the diameter
of droplets based on the characteristics of the tree canopies during spray operations.
Electrostatic sprayers have provided improved deposition of specific droplet sizes and
may be a good aid to develop next-generation sprayer systems [165]. Electrostatic sprayers
apply a positive charge to liquids as they pass through the nozzle. The positively charged
liquids are attracted to negatively charged surfaces, which allows for efficient coating of
hard nonporous surfaces. A 44% increase in mean deposition using the electrostatic system
reported by Pascuzzi and Cerruto [166]. Some other factors including canopy conditions
and spray droplet retention depend heavily on the tree canopies and cannot always be
addressed by the spraying unit. However, the nozzle effect can be resolved by matching
appropriate nozzles with electronically controlled valves.
The future of precision spraying systems will focus on integrating a suite of robotic
and sensor-guided spraying systems with fruit tree growth and structure information to
ensure accurate dose of pesticide delivery and reduce off-target deposition. A wide variety
of sensor-based spraying technologies are available to measure the amount of spray volume
needed; however, it is also important to assess the durability of these technologies so that
a variety of sensors can be added at reasonable price for the farmers. Concurrently with
sensor development, site-specific disease, pests, and stress monitoring using technologies
such as deep learning and remote sensing will improve the functioning of current sensor-
based spraying systems. Fusing/integrating different sensors could help to add more
decision-making features for more precise pesticide applications in tree fruits. Future
spraying systems must also handle diverse agrochemical formulations beyond that of the
standard aqueous mixtures currently used. Although tractor-based spraying systems are
mostly used for agrochemical applications, an alternative UAV based application method
could displace traditional sprayers. UAVs have been tested in the field crops for disease
scouting, stress monitoring, and spraying, and its preliminary results for spraying is
showing promise. But most of the current UAV based spraying technologies may not be
suitable for spraying in large scale tree fruit orchards due to their limited payloads and
variable spray deposition capability. More research and development into UAV swarms
and large capability UAV similar to unmanned helicopters could result in more effective
system for commercial/large scale fruit orchards. Detection of disease and stress, pest
pressure are other avenues for their use in tree fruits production besides variable-rate
spraying. Therefore, the integration of multiple sensing and control systems will be the
key to using advanced precision sprayers in the future.
6. Conclusions
The excessive use of agrochemicals by conventional sprayers has gained attention
among orchard growers, practitioners, and ecologists. Serious efforts are required to make
an advanced precision orchard spraying system commercially available in an economically

Sensors 2021, 21, 3262 23 of 29
and environmentally sound manner. Considering the precision, accuracy, and numerous
factors of using different sensors in outdoor environments, we conclude with the following
guidelines for variable-rate spraying in the tree fruit orchards:
• The canopy geometry, canopy density, leaf area index, and leaf area density are
essential for variable-rate spraying and could be measured accurately with high
precision using ultrasonic and LiDAR sensors;
• The camera sensors-based sprayer can be a good aid for detecting position of the
canopies and spot spraying; however, their performance is inferior due to environ-
mental and sensor limitations;
• The sprayer with ultrasonic sensors can have high precision in the orchard where the
trees are discontinuously planted or trees with low canopy density and less variation
among sections. These kinds of sensors can be useful for measuring average target
canopy characteristics;
• The LiDAR sensors guided sprayer could be suitable for many types of orchards
(low, medium, or high canopy density) and provide canopy details regardless of the
environmental conditions with comparatively high reliability and accuracy;
• Use of LiDAR sensors must need advance algorithms and microprocessors to over-
come the complex filtering problem and to process large amounts of information;
• Fusion of different advanced sensors, application of new algorithms such as deep
learning, and new research methodologies should be considered to develop the next
generation advance precision sprayer for tree fruit orchards.
Author Contributions: Conceptualization, M.S.M. and L.H.; writing—original draft preparation, M.S.M.
and A.Z.; writing—review and editing, L.H. and P.M.; supervision, L.H.; project administration, L.H.;
funding acquisition, L.H. All authors have read and agreed to the published version of the manuscript.
Funding: This study was supported in part by United States Department of Agriculture (USDA)’s
National Institute of Food and Agriculture (NIFA) Federal Appropriations under Project PEN04653
and Accession No. 1016510, a USDA NIFA Crop Protection and Pest Management Program (CPPM)
competitive grant (Award No. 2019-70006-30440), and Northeast Sustainable Agriculture Research
and Education (SARE) Graduate Student Grant GNE20-234-34268.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments: Authors would like to thank Heping Zhu (Agricultural Engineer, USDA-ARS)
for providing his valuable suggestions and guidance to improve this article.
Conflicts of Interest: The authors declare no conflict of interest.
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