Fruit Picking Robots: A Detailed Technological Literature Review

This literature review examines the advancements in fruit picking robot technology, highlighting various approaches and studies in the field. It discusses the use of computer vision systems for fruit recognition, robotic manipulators for harvesting, and automated systems for fruit handling. The review covers research on optimizing fruit extraction techniques, near-minimum-time task scheduling, and the development of autonomous machines for fruit gathering. It also includes insights from conferences on the design and implementation of fruit picking robots, emphasizing the integration of IoT, path planning algorithms, and machine vision for efficient and automated harvesting processes. The reviewed articles and conference papers collectively demonstrate the potential of robotics to reduce labor costs, improve harvesting efficiency, and enhance the overall management of fruit farms, while also addressing the challenges and future directions in this rapidly evolving area.

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Running Head: FRUIT COLLECTION 0
Fruit picking robots
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Table of Contents
Introduction...........................................................................................................................................2
Literature review...............................................................................................................................2
Article 1.........................................................................................................................................2
Article 2.........................................................................................................................................3
Article 3.........................................................................................................................................3
Conference 1.................................................................................................................................4
Conference 2.................................................................................................................................5
Conference 3.................................................................................................................................6
References.............................................................................................................................................7

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Introduction
Technology has achieved different milestones and continually developing day by day [2].
The robotics inventors offer to the agriculturalists the chance to significantly decrease the
prices of manual labour for picking [2]. The machines can substitute the seasonal physical
effort or even everlasting workers on lands. The robot practises computer image systems to
recognise and trace fruits in the tree [3]. The equipment applied is not precisely intended for
farming. The similar machinery can be applied in a wide variety of productions, but for
nowadays they are spending it into the farming. For example The FF Robotics establishment
design and generate a robot capable to decrease the costs of agrarians by substituting the
reaping employment [4]. The robot is named FF Robot. Similar many other automatons, FF
robot apply advanced duplicate processing procedures and systems for harvesting the fruits or
vegetables from the plant or tree. The image treating systems can notice damaged, unhealthy
or unripe fruits. The clutching hand can be effortlessly adapted to best choice of different
kinds of fruits. This sorts the robot obtainable for dissimilar reaping periods [3].
Literature review
Article 1
Wei, Jia, Lan, Li, Zeng, & Wang, (2014), Conducted a study on fruit picking robots. In this
paper, a programmed extraction technique of fruit substances under complex farming
background for visualisation scheme in fruit collecting robot is planned. A fruit gathering
robot scheme is composed by numerous blocks, such as visualisation sensor, force system,
and visualization system. Continuously, the fruit gathering robot system handling different
types of fruits. Robot system, visualisation sensor is perfect for robot scheme due to its little
cost, extensive availability, great data content and data rate. Removing target info about the
world is critical to operating successfully and making visualisation an interesting sensor.
Numbers of trial results show that the abstraction method grounded on the refining Otsu

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adaptive inception algorithm spending a new story in OHTA colour planetary can distinguish
suitable fruits from the complex farming background. The presentation of the technique was
established on real-world appearance with contrast to certain other methods [5]
Article 2
A near least time undertaking scheduling procedure for fruit-picking machines taking to pick
fruitlets at N given sites is offered in this article [6]. For the assumed kinematic and inertial
limits of the manipulator, the process controls the near-optimal order of fruit positions by
which the arm must pass and catches the near-minimum-time trail amid these points. The
order of gestures was gained by resolving the wandering salesman difficulty using the
detachment sideways the geodesics in the schemer's inertia place, between each two fruit
sites, as the price to be minimalized. The planned algorithm was practical to describe the
gestures of a citrus-picking machine and was verified for a tubular robot on fruit location data
composed from twenty trees. Important reduction in the obligatory computing period was
attained by dividing the capacity covering the fruits into sub sizes and approximating the
geodesic detachment rather than computing it [6].
Article 3
Another study conducted by Muscato, Prestifilippo, Abbate, & Rizzuto, (2005) on orange
picking robots. The purpose of this article was to build a profitable farming management for
fruit gathering and supervision deprived of human interference. The method of this study
included Describing a research movement including completely autonomous machines for
fruit gathering and treatment crates.
Results
Numerous examinations have been achieved in order to assess the heftiness of the regulator
scheme and the efficiency of the gathering machines. The mechanism system is actual
healthy, even if the clutching device interrelates with the twigs of the tree. The outcomes

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develop level-headedly, though, if the tree is clipped suitably. To assess the net output of the
fruit collector, seeing the unplanned nature of the delivery of the fruitlet, an arithmetical
examination of the gathering method was accomplished [7]. The mean harvesting time was
calculated not comprising the perusing time and the touching time, which actually are not
portion of the gathering practice. The outcomes achieved illustrate that with approximately
further motorized alterations and the growth of more healthy and additional adaptive
mechanism systems, it would be conceivable to attain a robot scheme with a robust economic
result on fruit gathering. They have discussed out the huge complications characteristic in this
kind of effort and assumed signs of resolutions that they deliberate to be the finest or
maximum inexpensive. Bearing in attention the conceivable commercial influence, the
mechanisms used are overall obtainable commercially, thus restraining prices though
promising a great degree of dependability [7].
Conference 1
Li, Zhou, Yang, & Zheng, (2016) published an report in the Proceedings of the 2016 Global
Conference on Computer Engineering, Info Science & Application Technology (ICCIA) on
Project and Understanding of fruit Gathering Robot Grounded on IOT. This conference
conducted on projects the brainy fruit machine picker on the source of fruit implanting
environment and real picking procedure as well as the mechanism visual equipment’s
investigation on fruit selection- process to attain the intelligent selection- method in
cultivation making. This automaton is established by mechanical unit and electrical brainy
control scheme. The motorised section comprises the mobile transporter, machine arm,
picker, cross slider, and an electrical intellectual control structure mostly consists of regulator
computer, servo motorised drive, dual CCD camera, the sensor regulator unit, data gaining
card, GPIB card, gesture card, Lithium battery control box, GPIB card and control structure
[8]. The motorised unit is the hardware stage for brainy robot collector, and it expresses the

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compact foundation for prepared fruit credit and gathering of the mechanical device. It attains
robot picker auto regulator by combined in each constituent with dissimilar purposes. The
moveable carrier is the centre of brainy robot, and it is likewise the podium for other
adjusting acquisition schemes. The signal attainment, motor energy, sensor unit as well as
intellectual control scheme of the smart robot picker request the mobile transporter, and it
accepts crawler travelling technique to familiarise to the plantation ground, through large
contacting superficial and great stressed part on the ground, and offers less force to it [8].
It plans the brainy mobile fruit automaton picker founded on mechanism visual equipment in
accord to the fruitage necessity of ripe the fruit from the agriculturalist. With addition of
engine visual skill and automation machinery, this machine structures simple motorised
structure, supple coordination, rapid drive which could evade the barrier positively with rapid
target acknowledgement as well as great ability. It full fills the motorised intellect, and
mechanisation unmanned dispensation for agriculture gathering effort, which has the hopeful
future [8]
Conference 2
Baur, Schütz, Pfaff, Buschmann, & Ulbrich, (2014) published a report in International
conference of farming engineering on path preparation for the fruit gathering manipulator. In
their influence they addressed the path preparation task for a segmental agricultural schemer
with terminated kinematics. They described two methods for mechanically making a path in
the schemer workstation: (1) a heuristic method with short supplies on sensor info and (2) a
possible field method. Since the atmosphere in farming applications is frequently acquiescent
only main problems (i.e. stalks and fruits) are occupied into consideration.
In this conference they gave an overall overview on the preparation system for the collecting
schemer in the CROPS assignment. Two dissimilar approaches to design an effort space

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route, namely an experiential and a possible field method were labelled. The outcomes of the
organiser in a creation scene with single fruit and single stem (consistent to the sweet pepper
use) were exposed and deliberated. For a solitary free upright plant, the experiential planner
was positively tested in their laboratory demo which will be beneficial in fruit picking [9].
Conference 3
In 2015 IEEE Global Conference on the Robotics and Automation (ICRA), Schuetz, Baur,
Pfaff, Buschmann, & Ulbrich, (2015) published an report. Stimulated by manufacturing
making, a clear technique is to practice machine schemers in conservatories and farmsteads.
To feat the possible of redundant schemers in specific, progressive gesture preparation
systems are desirable. While reaping, a new route for each fruit has to be prearranged
They assessed their algorithm by different iterations, scheming time and advantage by 79
fruit sites dignified in the conservatory. Therefore they show that their proposed technique is
modified to the necessities and the atmosphere for pepper gathering and allows the practise of
optimal routes for an actual application [10].
Wide examinations with 79 fruits have been evaluated statistically concerning computational
period, desired iterations, achievement rate and advantage. Additional developments
particularly regarding time period parametrization along with the study on probabilistic
approaches for discovering a feasible early answer are in the emphasis of their work [10]

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References
[1] Y., Edan, T., Flash, U. M., I., Peiper, Shmulevich, Y. and Sarig, “Near-minimum-time task
planning for fruit-picking robots”, IEEE transactions on robotics and automation, Vol. 7 no.
1991, pp. 48-56.
[2] G. S., Huang, X. S., Chen, and C. L Chang, “Development of dual robotic arm system based
on binocular vision In 2013”, CACS International Automatic Control Conference (CACS), pp. 97-
102. IEEE.
[3] S., Hayashi, K., Shigematsu, S., Yamamoto, K., Kobayashi, Y., Kohno, J., Kamata and M.
Kurita, “Evaluation of a strawberry-harvesting robot in a field test”,Biosystems engineering, vol.
105 no. 2, 2010, pp. 160-171.
[4] X. Y., TANG, and T. Z. ZHANG, “ Robotics for fruit and vegetable harvesting: a review
[J]. Robot, vol.27 no.1, 2005, pp. 90-96.
[5] X., Wei, K., Jia, J., Lan, Y., Li, Y., Zeng, and C. Wang, “Automatic method of fruit object
extraction under complex agricultural background for vision system of fruit picking robot”, Optik-
International Journal for Light and Electron Optics, vol. 125 no.19, 2014, pp. 5684-5689.
[6] W., Ji, D., Zhao, F., Cheng, B., Xu, Y., Zhang, and J., Wang, “Automatic recognition vision
system guided for apple harvesting robot”, Computers and Electrical Engineering, vol.38 no. 5,
2013, pp.1186-1195.
[7] G., Muscato, M., Prestifilippo, N., Abbate, and I. Rizzuto, “A prototype of an orange picking
robot: past history, the new robot and experimental results”, Industrial Robot: An International
Journal, Vol. 32 No.2, 2005, pp.128-138.

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[8] B., Li, A.,Zhou, C., Yang, and S., Zheng, “The Design and Realization of Fruit Harvesting
Robot Based on IOT”, In 2016 International Conference on Computer Engineering, Information
Science and Application Technology (ICCIA 2016), 2016, Atlantis Press.
[9] J., Baur X, C., J., Pfaff, T., Buschmann, Buschmann, and H, Ulbrich, “ Path planning for a
fruit picking manipulator.” In International Conference of Agricultural Engineering, 2014.
[10] C., Schuetz, J., Baur, J., Pfaff, T., Buschmann, and H. Ulbrich, “ Evaluation of a direct
optimization method for trajectory planning of a 9-DOF redundant fruit-picking
manipulator” ,In 2015 IEEE International Conference on Robotics and Automation (ICRA), 2015,
pp. 2660-2666. IEEE.