Research and development in agricultural robotics: A perspective of digital farming

July, 2018 Int J Agric & Biol Eng Open Access at https://www.ijabe.org Vol. 11 No.4 1
Research and development in agricultural robotics:
A perspective of digital farming
Redmond Ramin Shamshiri1,2,3*
, Cornelia Weltzien2
, Ibrahim A. Hameed3
, Ian J. Yule4
,
Tony E. Grift5
, Siva K. Balasundram1
, Lenka Pitonakova6
, Desa Ahmad7
, Girish Chowdhary5
(1. Department of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia;
2. Leibniz Institute for Agricultural Engineering and Bioeconomy, Max-Eyth-Allee 100, 14469 Potsdam-Bornim, Germany; 3. Dept. of ICT
and Natural Sciences, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology
(NTNU), Larsgårdsveien 2, NO-6009 Ålesund, Norway; 4. New Zealand Centre for Precision Agriculture (NZCPA), School of Agriculture
and Environment, Massey University, Private Bag 11 222, Palmerston North 4442, New Zealand; 5. Department of Agricultural and
Biological Engineering, University of Illinois at Urbana Champaign, 1304 West Pennsylvania Avenue Urbana, IL 61801, USA;
6. Department of Computer Science, University of Bristol, Bristol, United Kingdom;
7. Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia)
Abstract: Digital farming is the practice of modern technologies such as sensors, robotics, and data analysis for shifting from
tedious operations to continuously automated processes. This paper reviews some of the latest achievements in agricultural
robotics, specifically those that are used for autonomous weed control, field scouting, and harvesting. Object identification,
task planning algorithms, digitalization and optimization of sensors are highlighted as some of the facing challenges in the
context of digital farming. The concepts of multi-robots, human-robot collaboration, and environment reconstruction from
aerial images and ground-based sensors for the creation of virtual farms were highlighted as some of the gateways of digital
farming. It was shown that one of the trends and research focuses in agricultural field robotics is towards building a swarm of
small scale robots and drones that collaborate together to optimize farming inputs and reveal denied or concealed information.
For the case of robotic harvesting, an autonomous framework with several simple axis manipulators can be faster and more
efficient than the currently adapted professional expensive manipulators. While robots are becoming the inseparable parts of
the modern farms, our conclusion is that it is not realistic to expect an entirely automated farming system in the future.
Keywords: agricultural robotics, precision agriculture, virtual orchards, digital agriculture, simulation software, multi-robots
DOI: 10.25165/j.ijabe.20181104.4278
Citation: Shamshiri R R, Weltzien C, Hameed I A, Yule I J, Grift T E, Balasundram S K, et al. Research and development in
agricultural robotics: A perspective of digital farming. Int J Agric & Biol Eng, 2018; 11(4): 1–14.
1 Introduction
Modern farms are expected to produce more yields with higher
quality at lower expenses in a sustainable way that is less
Received date: 2018-03-28 Accepted date: 2018-07-05
Biographies: Cornelia Weltzien, PhD, Professor, research interests: mechanical
engineering, control systems and agricultural engineering. Email:
CWeltzien@atb-potsdam.de; Ibrahim A. Hameed, PhD, Associate Professor,
research interests: machine learning, AI, optimization and robotics. Email:
ibib@ntnu.no; Ian J. Yule, PhD, Professor, President of the International
Society of Precision Agriculture, research interests: digital agriculture, system
modeling and optimization, remote sensing. Email: I.J.Yule@massey.ac.nz;
Tony E. Grift, PhD, Associate Professor, research interests: Agricultural
Robotics, Advanced machinery for biosystems applications, Automation and
control. Email: grift@illinois.edu; Siva K. Balasundram, PhD, Associate
Professor, research interests: precision agriculture, information system and
technology. Email: siva@upm.edu.my; Lenka Pitonakova, PhD, research
interests: swarm robotics, simulation of complex systems, neural networks,
unsupervised learning. Email: contact@lenkaspace.net; Desa Ahmad, PhD,
Professor, research interests: soil machine mechanics, agricultural machinery
engineering, agricultural mechanization, controlled environment and smart
farming. Email: desa@upm.edu.my; Girish Chowdhary, PhD, Assistant
Professor, research interests: Intelligent systems, field-robotics, multiple aerial
vehicles. Email: girishc@illinois.edu.
*Corresponding Author: Redmond Ramin Shamshiri, PhD, research interests:
control systems and dynamics, simulation and modeling. Department of
Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia,
43400, Serdang, Selangor, Malaysia. Tel: +60-3894-68472, Fax: +60-
386567099, Email: raminshamshiri@upm.edu.my.
dependent on the labor force. Implementation of digital farming
and site-specific precision management are some of the possible
responses to this expectation, which depends not only on the sensor
technology but the continuous collection of field data that is only
feasible through proper utilization of agricultural robots.
Agricultural scientists, farmers, and growers are also facing the
challenge of producing more food from less land in a sustainable
way to meet the demands of the predicted 9.8 billion populations in
2050[1]
. That is equivalent of feeding a newly added city of
200 000 people every day. Integration of digital tools, sensors,
and control technologies has accelerated design and developments
of agricultural robotics, demonstrating significant potentials and
benefits in modern farming. These evolutions range from
digitizing plants and fields by collecting accurate and detailed
temporal and spatial information in a timely manner, to
accomplishing complicated nonlinear control tasks for robot
navigation. Autonomous guided tractors and farm machinery
equipped with local and global sensors for operating in row-crops
and orchards have already become mature. Examples include the
John Deere iTEC Pro (Deere & Company, Moline, Illinois) which
uses Global Navigation Satellite System for steering control, and
the Claas autonomous navigation (Harsewinkel, Ostwestfalen-
Lippe, Germany) which offers Cam Pilot steering and 3D computer
vision in addition to the GPS-based control to follow features on
the ground. Agricultural field robots and manipulators have
become an important part in different aspects of digital farming[2]

2 July, 2018 Int J Agric & Biol Eng Open Access at https://www.ijabe.org Vol. 11 No.4
and precision agriculture[3]
. With the advances in controls theory,
applications of these robots in digital farming have shown growing
interest towards automation, changing the traditional field activists
to high-tech industrial tasks that are attracting investors,
professional engineers, and companies. While many are still in
the prototype phase, these robots are now capable of performing
various farming operations, including crop scouting[4]
, pest and
weed control[5]
, harvesting[6-10]
, targeted spraying[11,12]
,
pruning[13,14]
, milking[15,16]
, Phenotyping[17,18]
, and sorting[19]
.
Unlike the industrial case, these applications can be extremely
challenging to be fully automated. An agricultural robot is
subjected to an extremely dynamic environment, and yet expected to
touch, sense, or manipulate the crop and the surroundings in a
precise manner which makes it necessary to have the minimal
amount of impact while increasing efficiency[20]
. Although
industrial robotic platform with precision accuracy and speed are
available, their application in agriculture is limited due to what we
refer to as unstructured environments and uncertain tasks which
impose great challenges. For example, the demand for off-season
cultivation of fruits and vegetables require different aspects of
automation and robotics in closed-field plant production
environments like greenhouses[21]
. A field robot with spraying,
de-leafing, and harvesting manipulator and end-effector for such
tasks in a dynamic, complex, and uncertain environment should
take into account the different arrangements of plant sizes and
shapes, stems, branches, leaves, fruit color, texture, obstacles, and
weather influences in order to operate efficiently in the real world
condition. In the case of harvesting for example, the sensing
mechanism has to identify the ripeness of fruits in the presence of
various disturbances in an unpredicted heterogeneous environment,
while the actuation mechanism should perform motion and path
planning to navigate inside the plant system or tree canopy with
minimum collisions for grasping and removing the soft fruit
delicately. This is by far more challenging compared to an
industrial robot in charge of picking and placing a solid bolt in an
assembly line.
The organization of this paper is as follow: In Section 2 we
have provided a literature review on the research and development
in agricultural robotics followed by separated discussions focused
on weed control, field scouting, and harvesting robots. Section 3
highlights the perspective of agricultural robotics and the
opportunities for digital farming and virtual orchards. Section 4
extends our discussion on the challenges of digitalization,
automation, and optimization of robotics for precision agriculture.
A summary of findings and conclusions are presented in section 5.
2 Research and development in agricultural robotics
Research works on agricultural robotics cover a wide range of
applications, from automated harvesting using professional
manipulators that are integrated with custom designed mobile
platforms and innovative grippers such as the one shown in Figure
1, or autonomous targeted spraying for pest control in commercial
greenhouses[22]
, to optimum manipulator design for autonomous
de-leafing process of cucumber plants[23]
, and simultaneous
localization and mapping techniques for plant trimming[24]
. Most
of the published literatures in this context are focused on (i)
vision-based control, advanced image processing techniques, and
gripper design for automated harvesting of valuable fruits (see for
example the published literatures on sweet pepper[7,25-28]
, oil
palm[29-31]
, mango[32]
, cucumber[23,33-38]
, almond[39,40]
, apple[41-43]
,
strawberry[44-46]
, cherry fruit[47]
, citrus[48–50]
, vineyard[51-53]
, and
tomato[54-57]
), or (ii) navigation algorithms and robust machine vision
systems for development of field robots that can be used in yield
estimation[42,58,59]
, thinning[60]
, weeding and targeted spraying[61-64]
,
seedling and transplanting[65,66]
, delicate handling of sensitive
flowers[67,68]
, and multipurpose autonomous field navigation
robots[18,69-75]
. In addition to these, several virtual
experimentation frameworks have been developed for agricultural
robots. An example includes the work of [76] in which a generic
high-level functionality was provided for easier and faster
development of agricultural robots. In another attempt, a
customized software platform called ForboMind[77]
was introduced
to support field robots for precision agriculture task with the
objective to promote reusability of robotic components.
ForboMind is open-source, and support projects of varying size and
complexity, facilitate collaboration for modularity, extensibility,
and scalability. In order to experiment with vision sensors and
agricultural robots[7]
, created a completely simulated environment
in V-REP (Coppelia Robotics)[78]
, ROS[79]
, and MATLAB
(Mathworks, Natick, MA, USA) for improvement of plant/fruit
scanning and visual servoing task through an easy testing and
debugging of control algorithms with zero damage risk to the real
robot and to the actual equipment. Example solutions addressing
robotic harvesting included eye-in-hand look-and-move
configuration for visual servo control[49,80-82]
, optimal manipulator
design and control[29,38]
, end-effector and gripper design[8,83]
,
stability tests for robot performance analysis in the dense obstacle
environments[84]
, motion planning algorithms[85]
, and orchard
architecture design for optimal harvesting robot[6]
. Improvements
in vision-based control system[7,48,49,86,87]
have enabled several
applications of robotic manipulators for greenhouse and orchard
tasks and have contributed to the decrease in workload and labor’s
fatigue while improving the efficiency and safety of the operations.
These achievements were considered a challenge in the earlier
agricultural robotics works[88-90]
.
Agricultural field robots[91]
on the other hand contribute to
increasing the reliability of operations, improved soil health, and
improved yield. They are generally equipped with two or
multiple sensors and cameras for navigation control, simultaneous
localization and mapping, and path planning algorithms[92-94]
.
Some of the earlier attempts for developing agricultural field robots
prototypes can be found in the works of [95-98]. The automated
harvesting platform shown in Figure 1 is one of the most recent
achievements in the field of agricultural robotics. It was
introduced by the SWEEPER EU H2020 project consortium –
(www.sweeper-robot.eu) on July 4, 2018. It is an assembly of an
autonomous mobile platform with Fanuc LRMate 200iD robot
manipulator (Fanuc America Corporation, Rochester Hills, MI)
holding an end-effector and catching device for fruit harvesting.
The ultimate goal of the Sweeper project is to put the first working
sweet pepper harvesting robot on the market. Using the camera
system mounted on the end-effector, the SWEEPER scans plants
looking slightly upwards for detecting mature fruits (the robot
observe the bottom part of the peppers to determine the fruit
maturity). The camera and sensors setup is completely
independent of the surrounding light conditions and provide
information about color images and distance maps that are used for
fruit detection, localization, and maturity classification. The
SWEEPER robot has been trained to detect obstacles such as leaves
and plant stems in the images. The training process was
accelerated using simulated artificial pepper plant models and deep

July, 2018 Shamshiri R R, et al. Research and development in agricultural robotics: A perspective of digital farming Vol. 11 No.4 3
learning network algorithms. Once the robot detects a pepper
fruit, information about its location is used to perform path
planning for the robotic arm trajectory. Because of the limited
moving space between the planting rows calculation of this
trajectory can be very complex. The robot then employs visual
servo control to reach the peduncle of the spotted peppers. The
robot camera takes images from different angles so that the arm
approaches the pepper in such a direction that the stem is always on
the back side of the pepper. A small cutting tool is positioned just
above the pepper which cuts the peduncle while the cutting tool is
moving downward. This separates the pepper from the plant’s
stem and drops it into a catching device which is moved toward the
pepper bin by the robotic arm. It is notable that the SWEEPER
can only harvest fruits that are located on the front side of the
plants and stems. A conveyer belt is said to be added to the robot in
order to convey harvested peppers to a standard pepper trolley.
Multiple robots and trolley will be parts of a fully automated
post-harvest logistic management system. The robot will exploit
its harvesting skill in full in a single stem row cropping system.
The most suitable yellow existing variety was used during the
SWEEPER test. According to the project website, for single row
growing system, the performance of SWEEPER evaluated with
only fruits that were on the front side of stems was respectively
62% and 31% in the modified and commercial crop. In general,
SWEEPER has a success rate of 49% in harvesting ripe fruits with
modified crop, and only 20% with the commercial (current
greenhouse growing) system. The average time to harvest one
fruit with SWEEPER is between 18 and 25 seconds compromising
4.73 s for platform movement, 3.71 s for fruit localization, 3.02 s
for obstacle localization, 4.03 s for visual servoing, 2.22 s for fruit
detaching, and 7.77 s for dropping fruit in container (data extracted
from SWEEPER website). The SWEEPER project team has
announced in their website that they have also achieved a harvest
time of less than 15 seconds (excluding platform movement) in
laboratory experiments. It is expected that results of projects like
this will serve as input for the development of a new fully
optimized and automated fruit production system for the
greenhouse horticulture sector.
Source: Sweeper EU H2020 project consortium – www.sweeper-robot.eu.
Figure 1 SWEEPER robot in action: The world first fully automated sweet pepper harvesting platform
For the purpose of this paper we provide a general review of
the recent advances in agricultural robotics, with focus on those
that employ high-tech sensors, artificial intelligence, machine
learning, and simulation environments for (i) weed control and
targeted spraying, (ii) field scouting and data collection, and (iii)
automated harvesting. We then extend our discussion to introduce
some of the most widely used simulation software and virtual
platforms that can be adapted to accelerate the design of
agricultural robots, improve operational performances, and evaluate
control capabilities of the actual hardware.
2.1 Weed control and targeted spraying robots
One of the main aspects of agricultural robotics is concerned
with the substitution of the human workforce by field robots or
mechanized systems that can handle the tasks more accurately and
uniformly at a lower cost and higher efficiency[6,99-103]
. Weed
control and precise spraying are perhaps the most demanded
applications for agricultural field robots. In this regard, targeted
spraying[104]
with robots for weed control application has shown
acceptable results and reduced herbicide use to as little as 5%-10%
compared to blanket spraying[105]
. While still not fully
commercialized, various promising technologies for weed robots
have been introduced and implemented over the past 10 years as
the results of interdisciplinary collaborative projects between
different international research groups and companies. Some of
the well-known names that are actively involved in the research
and development for various types of weed control robots are the
Wageningen University and Research Center (The Netherlands),
Queensland University of Technology, the University of Sydney,
Blue River Technologies (Sunnyvale, CA, USA), Switzerland’s
ecoRobotix (Yverdon-les-Bains, Switzerland), and France’s Naio
Technologies (Escalquens, France). For example a flexible
multipurpose farming and weeding robot platform named
BoniRob[18,106]
(shown in Figure 2a) was developed as a joint
project between the University of Osnabrueck, the DeepField
Robotics start-up, Robert Bosch company, and the machine
manufacturer Amazonen-Werker. The available time, labor,
equipment, costs, and types of weeds and the areas infested need
to be considered when planning a weed control program. For
such a robot to be efficient, it should be able to not only substitute
the tedious manual weed removal task, but also decreases the use
of spraying agrochemical and pesticide on the field. Figure 2
shows: (a) BoniRob[18,106]
: an integrated multipurpose farming
robotic platform for row crops weed control developed by
interdisciplinary teams which is also capable of creating details
map of the field, (b) AgBot II[107]
: an innovate field robot
prototype developed by the Queensland University of Technology
for autonomous fertilizer application, weed detection and
classification, and mechanical or chemical weed control, (c)
Autonome Roboter[108]
: a research effort robot developed by
Osnabrück University of Applied Sciences for weed control, (d)
Tertill[109]
: a fully autonomous solar powered compact robot
developed by FranklinRobotics for weed cutting, (e) Hortibot[110]
:
a robot developed by the Faculty of Agricultural Sciences at the
University of Aarhus for transporting and attaching a variety of
weed detection and control tools such as cameras, herbicide and
spraying booms, (f) Kongskilde Robotti[111]
: a robotic platform
equipped with drive belt operating based on the FroboMind
software[77]
that can be connected to different modules and

implements for automated and semi-automated mechanical weed
control, precision seeding, furrow opening and cleanings, (g)
RIPPA[112]
: a solar-powered Robot for Intelligent Perception and
Precision Application developed by the Australian Centre for
Field Robotics at Sydney University, and (h) spray robot
developed by HollandGreenmachine for smart chemical
application in greenhouses. Some of these robots can reduce
weed chemical use by 80%-90%[18,107,112]
.
a. BoniRob[18,106]
b. AgBot II[107]
c. Autonome Roboter[108]
d. Tertill[109]
Source: Deepfield Robotics Source: Queensland University of Technology Source: Osnabrück University Source: franklinrobotics.com
e. Hortibot[110]
f. Kongskilde Robotti[111]
g. RIPPA[112]
h. Spray robot
Image credit: technologyreview.com Image credit: conpleks.com Source: The University of Sydney Source: Hollandgreenmachine
Figure 2 Example of weed control and targeted spraying robots
In order to apply chemical directly to the weed’s vascular
tissue, a direct chemical application end effector is required to cut
the weed’s stem and spread the chemical on the cut surface. An
example of such an application can be found in [113] where a
proto-type weed control robot was developed to spray weeds in
cotton plants in the seed line. A real-time intelligent weed control
system was introduced in [114] for selective herbicide application
to in-row weeds using machine vision and chemical application.
A mini–robot to perform spraying activities based on machine
vision and fuzzy logic has been described in [115,116]. More
examples of autonomous vehicle robot for spraying the weeds can
be found in [117–119] and [90] and [114]. Development of an
autonomous weeding machine requires a vision system capable of
detecting and locating the position of the crop. Such a vision
system should be able to recognize the accurate position of the
plant stem and protects it during the weed control[120]
. A
near-ground image capturing and processing technique to detect
broad-leaved weeds in cereal crops under actual field conditions
has been reported in the work of [121]. Here the researchers
proposed a method that uses color information to discriminate
between vegetation and background, whilst shape analysis
techniques were applied to distinguish between crop and weeds.
Shape features of the radish plant and weed were investigated by
[122]. They proposed a machine vision system using a charge
coupled device camera for the weed detection in a radish farm
resulting 92% success rate of recognition for radish and 98% for
weeds. A combined method of color and shape features for sugar
beet weed segmentation was proposed by [98] with a 90% success
rate in classification. This rate increased to 96% by adding two
shape features. Another approach extracted a correlation between
the three main color components R, G and B which constitute
weeds and sugar beet color classes by means of discriminant
analysis[123]
. Their method resulted in different classification
success rates between 77 and 98%. The segmentation of weeds
and soybean seedlings by CCD images in the field was studied by
[124]. Texture features of weed species have been applied for
distinguishing weed species with grass and broadleaf classification
accuracies of 93% and 85%, respectively[125]
. Textural image
analysis was used to detect weeds in the grass[126]
. Gabor wavelet
features of NIR images of apples were extracted for quality
inspection and used as input to kernel PCA[127]
. Kernel PCA first
maps the nonlinear features to linear space and then PCA is applied
to separate the image Gabor wavelet (5 scales and 8 orientations)
combined with kernel PCA had the highest recognition rate
(90.5%). Spray robots for weed control have been developed with
vertical spray booms that increase the deposition in the
canopy[128-130]
. Some of the emerging technologies are the
self-propelled vehicles such as Fumimatic® (IDM S.L, Almería,
Spain) and Tizona (Carretillas Amate S.L., Almería, Spain), or
autonomous field robots such as Fitorobot (Universidad de Almería,
Cadia S.L., Almería, Spain) that have been designed specifically to
navigate inside fields that has loose soil and operate in situations
where a large number of obstacles are present[130]
. Some of these
robots are based on inductive sensors for following metal pipes that
are buried in the soil. Studies that reports autonomous robot
navigation inside greenhouse environments are slim[116,118,131,132]
.
A fixed—position weed robot was presented by [133] which is
interfaced to a standard belt—conveyor displacement system and
provides the robot with pallets containing the crops. Artificial
neural networks have also been used by many researchers to
discriminate weeds with machine vision[134,135]
. For example,
BoniRob[18,106]
uses AI to differentiate between weeds and plants
and then mechanically destroys the detected weeds using a
custom-built mechanism called “ramming death rod”. Different
control modules of BoniRob are connected by Ethernet and
communicate using TCP/IP. This platform has 16 degrees of
freedom (DOF) realized by different electro-motors and hydraulic
cylinder actuation. Each of the 4 wheels is driven by separate
motors and can be steered independently (motor controllers are
connected by a CAN bus). These reviews indicate that a fully
commercial robotic platform for the elimination of weeds has not
been realized yet. In addition, most of the research works in the

area of robotic weed control are applicable prior to the plant growth
or in some cases when the main plant height is between 0.2-0.3 m.
2.2 Field scouting and data collection robots
Field scouting robots face various interdisciplinary challenges
for providing reliable data and measurements that can be used and
processed by precision agriculture and crop models. Other than
the challenges of inherent physical and biological variability
involved with farm fields and orchards, scouting robot platforms
are expected to be flexible, multipurpose and affordable to be
considered viable for use in commercial scale. If successfully
integrated and implemented, these robots can play a key role in
reducing production cost, increasing productivity, quality, and
enabling customized plant and crop treatments. Development of
scouting robots for the purpose of data collection and modern
farming incorporates extensive use of advanced sensors for
precision agriculture[136,137]
in order to generate valuable results
while performing automatic and accurate navigation control,
manipulator control, obstacle avoidance, and three-dimensional
environment reconstructions. For example, an autonomous field
survey mobile robot platform with custom manipulator and gripper
was proposed[138]
to carry imaging sensors and GPS devises for
autonomous navigation and data collection inside greenhouses and
open-field cultivation environments (Figure 3a-3c). Various
multi-spectral imaging devices and LiDAR sensors are reported to
have been installed and used with modified mobile robot platforms
for automated monitoring and building reconstructed 3D point
clouds for generating computer images of trees and plants[69,139-141]
such as those shown in Figure 3d.
Actual Simulated Actual Simulated
a. A prototype surveillance field
robot[138], A
daptiveAgroTech.com
b. OSCAR field survey
robot, Inspectorbots.com
c. Husky UGV for field scouting and
3D mapping, Clearpathrobotics.com
d. Point cloud and detected maize plants [69]
, and 3D
point clouds of vineyard created by VinBotRobotnik
Automation (www.robotnik.eu)[140]
Figure 3 Examples of a prototype and professional field robots for scanning and 3D reconstruction of plants and environment
Some of the most advanced robotic technology for automated
field scouting and data collection are shown in Figure 4 including
(a) Trimbot2020[142]
, an outdoor robot based on a commercial
Bosch Indigo lawn mower platform and Kinova robotic arm for
automatic bush trimming and rose pruning, (b) Wall-Ye[143]
, a
prototype vineyard robot for mapping, pruning, and possibly
harvesting the grapes (wall-ye.com), (c) Ladybird[144,145]
, an
autonomous multipurpose farm robot for surveillance, mapping,
classification and detection for different vegetables, (d)
MARS[146,147]
: the mobile agricultural robot swarms are small and
stream-lined mobile robot units that have minimum soil
compaction and energy consumption and aim at optimizing plant
specific precision agriculture, (e) SMP S4: a surveillance robot for
bird and pest control developed by SMP Robotics
(smprobotics.com), (f) Vine agent, a robot equipped with advanced
sensors and artificial intelligence to monitor the field for plant’s
health assessment developed at the Universitat Politècnica de
València, (g) HV-100 Nursery Bot, a light weight robot developed
by Harvest Automation for moving of plants and potted trees in
greenhouses and small orchards developed by Harvest Automation
(harvestai.com/), (h) VinBot[59,148]
: an all-terrain mobile robot with
advanced sensors for autonomous image acquisition and 3D data
collection from vineyar for yield estimation and information
sharing, (i) Mantis, a flexible general purpose robotic data
collection platform equipped with RADAR, liDAR, panospheric,
stereovision, and thermal cameras[32]
, and (j) GRAPE, a Ground
Robot for vineyard monitoring and ProtEction funded by the
European Union’s for smart autonomous navigation, plant
detection and health monitoring, and manipulation of small
objects[149]
.
a. TrimBot[142]
b. Wall-Ye vinyard robot c. Lady bird[144]
j. MARS[146,147]
e. SMP S4
trimbot2020.org wall-ye.com Univ. of Sydney echord.eu/mars smprobotics.com
f. VineRobot g. HV-100 Nursery Bot h. VinBot[59,148]
i. Mantis [32]
d. GRAPE
vinerobot.eu harvestai.com vinbot.eu/ Univ. of Sydney grape-project.eu
Figure 4 Examples of general purpose robots for field scouting and data collection

2.3 Harvesting robots
Traditional harvesting of fruits and vegetables for fresh market
is a labor-intensive task that demands shifting from tedious manual
operation to a continuously automated harvesting. Increasing the
efficiency and reducing labor dependents of harvesting will ensure
high-tech food production yield and competitiveness. In spite of
the advances in agricultural robotics, million tons of fruits and
vegetables are still hand-picked every year in open-fields and
greenhouses. Other than the high labor cost, the availability of the
skilled workforce that accepts repetitive tasks in the harsh field
conditions impose uncertainties and timeliness costs. For robotic
harvesting to be cost-effective, fruit yield needs to be maximized to
compensate the additional automation costs. This leads to
growing the plants at higher densities which make it even harder
for an autonomous robot to simultaneously detect the fruit, localize
and harvest it. In the case of sweet pepper fruit, with an estimated
yield of 1.9 million tons/year in Europe, reports indicate that while
an average time of 6 seconds per fruit is required for automated
harvesting, the available technology has only achieved a success
rate of 33% with an average picking time of 94 seconds per fruit[25]
.
For cucumber harvesting, a cycle time of 10 seconds was proven to
be economically feasible[38]
. Only in Washington State, 15-
18 billion apple fruits are harvested manually every year. An
estimated 3 million tons of apples is reported to have been
produced in Poland in 2015[150]
, out of which one-third are delicate
fruits and are less resistant to bruising from mass harvester
machines. Also in Florida, where the current marketable yield of
sweet pepper fruits in open-field cultivation is 1.6 to 3.0 with a
potential yield of 4 lb/ft2
in passive ventilated greenhouses[151]
,
manual harvesting is still the only solution. Therefore,
development of an automated robotic harvesting should be
considered as an alternative method to address the associated labor
shortage costs and timeliness. A fully automated robotic harvester
will contribute to solving some of the today’s major grower issues,
such as labor costs, labor availability, food safety and quality. It
also plays an essential role in improving the interactions between
human, machine, and plants[131]
. For example, the prevention of
musculoskeletal disorders in manual harvesting operations in Dutch
greenhouses has motivated various researchers for replacement of
human labor by automatons robot for picking cucumber[33]
and
sweet pepper[7]
fruits. A functional model was then introduced[23]
in the field test of an autonomous robot for de-leafing cucumber
plants grown in a high-wire cultivation system. Field results
showed that the de-leafing robot spent an average time of
140 seconds for two leaves plants, which was 35 times longer than
manual leaf picking per plant[23]
.
Research and development in robotic harvesting date back to
the 1980s, with Japan, The Netherlands, and the USA as the
pioneer countries. The first studies used simple monochrome
cameras for fruit detection inside the canopy[152]
. Other than the
visible light RGB cameras[41,153]
and the ultrasonic radar sensors
that are commonly used for object detection due to their affordable
cost[154]
, advances in the sensing and imaging technology have led
to the employment of sophisticated devices such as infrared[47]
,
thermal[155]
, hyperspectral cameras[156]
, LiDAR[32,39,40,157]
, or
combination of multi-sensors[158]
that are adopted with novel
vision-based techniques for extracting spatial information from the
images for fruit detection, recognition, localization, and tracking.
A common approach in fruit detection and counting[153]
is by using
a single viewpoint, as in the case of a cucumber harvesting robot[23]
,
or multiple viewpoints [32]
with additional sensing from one or
multiple vision sensors that are not located on the robot[159]
.
Examples of the recent achievements include automatic fruit
recognition from multiple images[160]
or based on the fusion of
color and 3D feature[161]
, multi-template matching algorithm[162]
,
symmetry analysis[163]
, combined color distance method and
RGB-D data analysis for apples[164]
and sweet-peppers[41]
, stereo
vision for apple detection[165,166]
, and the use of convolutional
neural networks[167]
and deep learning algorithms for fruit detection
and obstacle avoidance in extremely dense foliage[54,168]
. Some of
the challenges to be addressed in designing of a complete robotic
harvesting are the simultaneous localization of fruit and
environment mapping, path planning algorithms, and the number of
detectable and harvestable fruits in different plant density
conditions. Significant contributions have been made by various
research groups to address these challenges, however there is
currently no report of a commercial robotic harvesting for fresh
fruit market[169]
, mainly due to the extremely variable
heterogeneous working condition and the complex and unpredicted
tasks involved with different fruit and plant scenario. The
function of a harvesting robot can be separated into three main
sections as sensing (i.e., fruit recognition), planning (i.e.,
hand-and-eye coordination) and acting (i.e., end-effector
mechanism for fruit grasping)[170]
.
Theoretical and applied research on robotic harvesting of fruits
and vegetable are huge. Figure 5 shows some of the efforts that
resulted in building actual robotic harvesting platforms, including
(a) Harvey[28]
: an autonomous mobile robot platform with UR5
manipulator for harvesting sweet peppers grown in greenhouses
and other protect cultivation systems, (b) the CROPS harvesting
platform for sweet pepper[27,171]
, (c) the SWEEPER platform
(developed by the Sweeper EU H2020 project consortium,
www.sweeper-robot.eu) with a Fanuc LRMate 200iD robot
manipulator (Fanuc America Corporation, Rochester Hills, MI) and
a custom-built gripper and catching mechanism for sweet pepper
harvesting, (d) the Energid robotic citrus picking system (Bedford,
MA), (e) the citrus harvesting robot[48,49,155]
developed at the
University of Florida which uses a custom built gripper mounted
on the Robotics Research manipulator model 1207 (Cincinnati,
Ohio), (f) the DogTooth strawberry robot (Great Shelford,
Cambridge, UK), (g) the Shibuya Seiki robot that can harvest
strawberry fruits every 8 seconds, (h) a tomato harvesting robot
from Suzhou Botian Automation Technology Co., Ltd (Jiangsu,
Suzhou, China), (i) a cucumber harvesting robot developed at the
Wageningen University and Research Center[35,38]
, (j) an apple
harvesting robot[172]
with custom built manipulator mounted on top
of a modified crawler mobile robot, (k) one of the first
manipulators developed for the CROPS project[171]
and modified
for apple harvesting, (l) a linear actuator robotic system for apple
picking developed by ffrobotics (Gesher HaEts 12, Israel), (m) a
vacuum mechanism robot for apple picking from
AbundantRobotics (Hayward, CA, USA), (n) the UR5 manipulator
with a soft robotic universal gripper for apple harvesting developed
at the University of Sydney, and, (n) an apple catching prototype
robot[173-175]
developed at the Wachington State University. Most
of these projects have used eye-in-hand look-and-move
configuration in their visual servo control. Other than the issues
with frame transformation, this solution is not promising if the fruit
is heavily occluded by the high-density plant leaves[176]
.
Obviously, the final robot prototype needs to be relatively faster for
mass-harvest, with an affordable cost for greenhouse growers.
Swarms of simple robots with multiple low-cost cameras and

innovative soft robotic grippers[177]
, or human-robot collaboration
are the research topics to solve the facing challenges in robotic
harvesting that current technology cannot overcome. These
approaches can significantly improve the processing time of
multiple fruit detection in the high-density plants, and provide
ground truth results over time for machine learning algorithms
based on human-operators experience. In fact, a promising
solution to efficient robotic harvesting is not through a single robot
manipulator. Results of simulation studies have revealed that
single arm robots for rapid harvesting are still far beyond
realization, and failed mainly due to the “sensing and moving”
action in high vegetation density. In this approach, even if the
fruit localization is accurate, and the robot control calculates an
optimum trajectory to reach the fruit without receiving additional
sensing feedback from the camera, the moment it enters into the
dense plant canopy it disrupts the exact location of the target fruit.
a. Harvey[28]
b. CROPS[27,171]
c. SWEEPER d. Energid citrus picking system e. Citrus robot[48,49,155]
Queensland Univ. of Technology crops-robots.eu sweeper-robot.eu Energid technologies University of Florida
f. DogTooth g. Shibuya Seiki h. Tomato harvesting robot i. cucumber robot[35,38]
j. Apple harvesting robot[172]
www.dogtooth.tech shibuya-sss.co.jp szbotian.com.cn Wageningen UR
k. Apple harvesting[178]
l. Apple picker m. Apple picking vacuum n. UR5 apple robot o. Apple catching[173-175]
crops-robots.eu FFRobotics.com abundantrobotics.com Univ. of Sydney Washington State University
Figure 5 Examples of harvesting robots for different fruits
3 Agricultural robotics and digital farming
Agricultural robotics is a promising solution for digital
farming and for handling the problems of workforce shortage and
declining profitability. Initial tests with one of the most recent
technologies available for automated harvesting (the Harvey[28]
robot) has already shown a success rate of 65% and detachment
rate of 90% for sweet pepper harvesting in real planting scenario
where no leaves and occluded fruits were trimmed or removed.
Field agent robots that autonomously monitor and collect data
empower growers with real-time detailed information about their
crops and farms, revealing upstream images for making
data-driven decisions. Agricultural robotic is taking farming
practices to a new phase by becoming smarter, detecting sources
of variability in the field, consuming less energy, and adapting
their performances for more flexible tasks. They have become an
integral part of the big picture in the future production of
vegetable and crops, i.e., growing plants in space or development
of robotized plant factories for producing vegetables in Antarctica.
The trend in food production is towards automated farming
techniques, compact Agri-cubes, and cultivation systems that have
the minimum human interface where skilled workforce are being
replaced with robotic arms and mobile platforms. In this context
digital farming have integrated new concepts and advanced
technologies into a single framework for providing farmers and
stakeholders with a fast and reliable method of real-time
observations at the plant level (i.e., field data collection and crop
monitoring) and acting at a more precise scale (i.e., diagnostics,
strategic decision-making, and implementing). Digital farming is
about collecting high-resolution field and weather data using
ground-based or aerial based sensors, transmitting these data into a
central advisory unit, interpreting and extracting information, and
providing decisions and actions to the farmers, field robots, or
agro-industries. Examples include thermal-RGB imaging
system[179]
for monitoring of plant and soil for health assessment,
creation of information maps (i.e., yield and density maps), and
data sharing. Implementation of digital farming practices result
in a sustainable, efficient, and stable production with a significant
increase in yield. Some of the technologies involved in digital
farming include the Internet of Thing[180]
, big data analysis[181]
,
smart sensors[182]
, GPS and GIS, ICT[183]
, wireless sensor
networks[184,185]
, UAV[186-188]
, cloud computing[189-191]
, simulation
software[192-195]
, mapping applications[196,197]
, virtual farms[198-200]
,
mobile devices[201-204]
, and robotics. A conceptual illustrating of
digital farming and its relationship with agricultural robotics is
provided in Figure 6, showing that the collected data by the robot

agents are sent to a cloud advisory center for decision makings.
The actions are then implemented quickly and accurately by the
use of robots or other automated machinery, sending operational
updates and notification feedbacks to the farmers and
agro-industry sections. This system of computer-to-robot
communication combined with the sophisticated simulation
software, analytics applications, and data sharing platforms offers
a much smoother control over farming operations. In addition, it
provides farmers with details of historical field data for improving
their performances and optimizing crop yields for specific plots, or
even developing new business models.
Source: www.AdaptiveAgroTech.com
Figure 6 A conceptual illustration of digital farming and virtual orchards with emphasize on the role of agricultural robotics
A key consideration before utilization of field robots in
large-scale farms is to take into account the cost-benefit analysis
for selecting the number and size of the robots that are required for
a specific operation. Digital farming can address this by having
simulated robots work in the 3D reconstructed field, orchards, or
plantations that have been created using UAV imagery and
photogrammetry software. With this approach, even people with
limited experience or knowledge about crop production, data
collection, and analytical methods could connect their farms to a
network, share their field information, and receive prescriptions.
Using digital farming, growers can collect valuable information
about their fields that were previously ignored or used to be
measured by the use of traditional scouting methods. For example,
detailed measurements about height and size of each tree in a citrus
orchard, nutrient stress, the required time for robot navigation
inside the orchard, estimated time for robotic harvesting of a tree,
and much more can be extracted from a virtual orchard. While
this integration might still seem too ambitious to be widespread in
many regions, it can serve as a prophetic awareness for a perceptive
outlook, offering new insights that enhance the ability for a modern
farming system. Developments of a new generation of
agricultural robots that can easily and safely cooperate to
accomplish agricultural tasks become necessary. Heavy tractors
and machinery used today compacts the soil, which over time
severely deteriorates the fertility of the soil. This is a significant
threat to soil in Europe. Compacted soils require more than a
decade of expensive treatment to recover its fertility. The
problem can be solved by replacing heavy tractors with a number
of smaller vehicles that can treat crop fields just as well and
without compacting of the soil. However, that scenario requires a
human supervisor/operator for each vehicle that is very expensive.
A technology is then required to enable a single farmer to
supervise and operate a team of these automated vehicles. This
includes the development of a mission control center and
intelligent coverage path planning algorithms to enable team
members to communicate and cooperate, and solve a range of
agricultural tasks in a safe and efficient way. One of the topics
that have been proposed by many researchers for a long time is
the exotic concept of Multiple Robots that can work together to
accomplish a specific farming task. The idea is to employ
artificial intelligence and genetic algorithms where multiple
robots are programmed to collaborate with each other and form
an ecosystem. This approach becomes even more useful when
robots begin learning from each other and improve their
performance over time. For example, a swarm of robots can
contribute to the creation of nutrient maps by collecting soil
samples and communicating with a cloud advisory center for
executing proper action on the go. The efficiency of this
process may not be great in the beginning, but the performances
can be improved over time by having deep learning algorithms
that emphasize the so-called good-behavior and punish the
bad-behavior of each robot. These robots have great
advantageous for digital farming. For example, a heterogeneous
Multi-Robot system compromising a ground mobile platform and

an aerial vehicle (Figure 7) for mapping environmental variables
of greenhouses has been simulated in the Unity3D 5.2.1 game
engine[205]
. This robot can measure the temperature, humidity,
luminosity and carbon dioxide concentration in the ground and at
different heights. Some of the relevant challenges in developing
multi-robot sensory systems are mission planning and task
allocation, obstacle avoidance, the guidance, navigation and
control of robots in different farming scenarios.
a. Air-ground Multi-Robot platform and its simulation in Unity3D[205]
b. Project MARS[146,147]
c. A Swarm of Nano Quadrotors
Source: TheDmel Youtube channel
Figure 7 Examples of Multi-Robot systems for digital farming
4 Challenges of robotics for precision agriculture:
Digitalization, Automation, and Optimization
After 20 years of research in precision agriculture there are
nowadays many types of sensors for recording agronomically
relevant parameters, as well as many farm management systems.
Electronically controlled machines and robots are state of the art.
In fact, technology is now capable of automating cyber-physical
systems by networking between different machines. This is what
we call “agriculture 4.0”. However, it still cannot be claimed that
precision agriculture has been widely established in crop
production. Why not? Data alone are not enough. Automatic
data recording only helps farm results where the analysis of the
collected material takes less time and allows more profit to be
made compared with good management decision based on gut
feeling and experience. Today, the largest portion of added value
deriving from the new technology lies with the machinery and not
the agricultural products. For instance, futures’ trading on the
commodity market has a much faster and more direct influence on
value development of agricultural products than does, e.g., the
quality of the product, or its yield, being increased in single-figure
percentages through the application of site-specific management
techniques. There remains the advantage of time savings.
Hereby, the task of agricultural robotic engineering development is
the creation of intelligent and simple to operate, so-called “smart”
systems. We call smart products those that appear cleverer than
the user in that they deliver answers even before the question has
been asked. An example: so-called fitness bracelets that record
and analyze the wearer’s movements. The smartness of the
equipment lies in the analysis of the values. Step count and heart
frequency are below average. This finding leads to a treatment
recommendation: exercise more! But the user still has to carry out
these recommendations. The second example, this time from
precision agriculture. Easily the most successful crop plant
sensors systems are those, that analyze, recommend, and then apply
a treatment in one go, such as the so-called N-Sensors. Although
the analytical procedure within the system is highly complex, such
sensors are very easy to operate. In contrast to that, for instance
yield mapping is an off-line approach which requires additional
processing steps analyzing the data on the PC. On top of
everything else, the yield information gained from one harvest can
only be set to use in the next growing season, representing a
long-term investment with much manual input and benefits that are
difficult to assess.
Challenges for sensor development and agricultural robotic
technology lie in the required high temporal and spatial resolution
data which are very different and difficult to measure parameters
under most unfavorable conditions. The aim of new analysis
methods is to combine the data and to fuse the different
information layers in order to derive new knowledge.
Additionally, automation of the data collection tasks is a
requirement in the development of “smart sensor” systems for
agricultural application in the sense that the decision making is
embedded in the sensor so that the results are directly applicable to
the robot for carrying out precise management actions. But what
exactly has the fitness bracelet mentioned above to do with the
N-Sensor as far as content is concerned? Both sensors analyze data
and process the material up to the point where recommendations
for direct action can be deduced from the result. Additionally,
both analytics are based on indicators not directly related with the
actual target values. The “fitness” of the plant can be efficiently
assessed through foliage chlorophyll content or green color. But
where the cause of the problem is not poor nitrogen supply but
instead lack of moisture, then the system must have this additional
information available. In this respect, intuitive interaction
between man and robot is necessary, a point that also represents a
great development challenge for sensors and automation
technology in crop production. The more we appreciate the
importance of the comprehension of detailed agronomic
relationships, the greater the need for information towards a better
understanding of these relationships. The more information
available, the deeper the understanding and this requires, in turn,
more data collection. The situation is therefore a loop within
which, especially in recent years, more and more data was collected
and increasingly intensified agronomic knowledge has been
developed. Hereby, however, the practical application of directly
usable agronomic knowledge has stagnated. Nowadays it still
requires a considerable mass of statistics and software expertise for
comprehensive application of precision agricultural technology.
For further development of smart sensors, relevant information
must be integrated into multi-causal decision-making systems in
order to generate knowledge. The targets are complex systems
that are easy to operate solutions with systemic, comprehensive and
transparent concepts, with good “usability” and simple application.
There must also be a way for practical experience to flow into these
integrated systems so that farmers, with the help of the technology,
can develop their expertise further. A core theme in the
development of Ag-robotics decision support systems is the step
from data storage over information retrieval to knowledge
management involving large amounts of data. Currently,
possibilities for the analysis of agricultural data and sensor data
fusion are being expanded through the application of multivariate

statistical methods and machine learning techniques. Hereby, the
system limits are increasingly expanded and nowadays holistic
concepts for complete added-value networks are already in focus,
whereby the mobile transmission of data is a basis technology for
the establishment of fully integrated systems enabling real-time
data fusion from different sources.
What we have discussed here are knowledge management and
intelligent systems. But, with all this “high-tech”, are we able to
concentrate clearly on our target? The crux of all the technical
developments is creating a more efficient crop production.
Automation and networking should serve the systemic control of
the agronomic processes, not vice versa. This is the environment
in which the Leibniz Research Alliances’ “Sustainable Food
Production and Healthy Nutrition” innovation initiative “Food and
Agriculture 4.0” focuses on the agricultural production process -
intelligently connected, of course. The aim of the initiative is the
interdisciplinary development of process technology basics for
Agriculture 4.0. Where knowledge-based decision making shall
ensure the satisfaction of social demands as well as individual
producer’s and consumer’s requirements, in terms of yields and
profits, while still taking into account the local, spatial,
environmental heterogeneities as well as global climate phenomena.
For this purpose the research goal is to develop on one hand models
of the agricultural production processes, adjusted to meet the
specific conditions, and on the other hand automation technologies
with which the processes shall be controlled such that the natural
resources can be retained or even improved and at the same time
the product quality will be maintained. The interoperability and
digital networking of agriculture will enable new process control
systems and new sales models such as online slurry sales points,
exchange platforms where data is traded for advice or online direct
marketing. However, even with Agriculture 4.0, only what is
sown can be driven home from the field. For instance, the
weather risk will not be any the less, although the harvesting
window might be better positioned by setting information
technology to use. Hereby we finish with the summary that even
Agriculture 4.0 will show only modest results if we do not take
care that some of the value added through the new technologies is
actually being associated with the agricultural products.
5 Conclusions
Research efforts for development of agricultural robots that
can effectively perform tedious field tasks have grown significantly
in the past decade. With the exception of milking robots that were
invented in the Netherlands, robotics has not reached a commercial
scale for agricultural applications. With the decrease of the
workforce and the increase of production cost, research areas on
robotic weeding and harvesting have received more and more
attention in the recent years, however the fastest available
prototype robots for weeding and harvesting are not even close to
being able to compete with the human operator. For the case of
picking valuable fruits using robots, the technology is now
becoming closer to a commercial product with the emerging of the
SWEEPER. For other fruits such as citrus and apples that can be
mass harvested for juice industry, modifications of the existing
mechanical harvesting systems with some robot functionalities may
be more promising than using single robot system. Increasing the
speed and accuracy of robots for farming applications are the main
issues to be addressed for generalization of robotics systems,
however, compared to the industrial and military cases, the lack of
abundant research funding and budgets in agriculture has
decelerated this process. For the case of robot harvesting,
improving sensing (fruit detection), acting (manipulator movement,
fruit attachment, detaching, and collecting), and growing system
(leave pruning and plant reshaping) are suggested to increase the
efficiency. It should be noted that development of an affordable
and effective agriculture robot requires a multidisciplinary
collaboration in several areas such as horticultural engineering,
computer science, mechatronics, dynamic control, deep learning
and intelligent systems, sensors and instrumentation, software
design, system integration, and crop management. We
highlighted some of the facing challenges in the context of utilizing
sensors and robotics for precision agriculture and digital farming as:
object identification, task planning algorithms, digitalization, and
optimization of sensors. It was also mentioned that for an
autonomous framework to successfully execute farming tasks,
research focus should be toward developing simple manipulators
and multi-robot systems. This is in fact one of the academic
trends and research focuses in agricultural robotics for building a
swarm of small-scale robots and drones that collaborate together to
optimize farming inputs and reveal denied or concealed
information. As of the conclusion, some forms of human-robot
collaboration as well as modification of the crop breeding and
planting systems in fields and greenhouses might be necessary to
solve the challenges of agricultural robotics that cannot yet be
automated. For example, in a collaborative harvesting system
using human-and-robot, any fruit that is missed by the robot vision
will be spotted by the human on a touchscreen interface.
Alternatively, the entire robot sensing and acting mechanism can be
performed by a human operator in a virtual environment.
Nevertheless, an agricultural robot must be economically viable
which means it must sense fast, calculate fast, and act fast to
respond to the variability of the environment.
Acknowledgements
The first author would like to express his appreciations to
Professor Salah Sukkarieh at the University of Sydney, Professor
Cornelia Weltzien and Professor Manuela Zude at the Leibniz
Institute for Agricultural Engineering and Bioeconomy, and Dr.
Jochen Hemming at the Wageningen UR for their insightful
meetings, lab demonstrations, and group discussions during his
visits. We also extend our deep appreciations to Dr. Wang
Yingkuan of the Chinese Academy of Agricultural Engineering and
his professional team at the International Journal of Agricultural
and Biological Engineering for reviewing the manuscript draft and
the editorial works. The consultation supports and assistance on
the economic and viability assessment of agricultural robotics
provided by Dr. Mary Sh, Ms. Mojgan, and Dr. Fatima Kalantari at
AdaptiveAgroTech are duly acknowledged.
Disclaimer
Mention of commercial products, services, trade or brand
names, organizations, or research studies in this publication does
not imply endorsement by the authors, nor discrimination against
similar products, services, trade or brand names, organizations, or
research studies not mentioned.
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