Automated Harvesting by Robotic Arm Fruit
Harvesting Robot
Aiza Arshad
Department of Electrical, Electronics
& Telecommunication Engineering
The University of Engineering and
Technology, Lahore
Lahore, Pakistan
2021ee256@student.uet.edu.pk
Shahroz Ahmad
Department of Electrical, Electronics
& Telecommunication Engineering
The University of Engineering and
Technology, Lahore
Lahore, Pakistan
2021ee319@student.uet.edu.pk
Abstract—This final year project focuses on the creation of an
automated robotic arm system for fruit harvesting, with a
focus on apples and related crops in orchard settings. The need
for automation has grown urgently as Pakistan's agriculture
industry struggles with a workforce deficit, particularly among
skilled personnel. Inverse kinematics for accurate robotic arm
movement, RGB-D sensors for fruit detection and localization,
and real-time data processing for responsive control are all
included in the suggested system. The robot can navigate
orchard terrains, avoid obstacles, and gather fruits carefully to
prevent damage or bruises by utilizing contemporary
technologies. In addition to lowering the need for physical
labor, this research seeks to increase production, increase
harvesting accuracy, and eventually support more sustainable
agricultural methods.
I. INTRODUCTION
This project presents a compact, self-driving robot that
integrates Mecanum-wheel mobility with a 6-DOF robotic
arm, enabling omnidirectional navigation and precise object
manipulation. Built using an Arduino Mega and costeffective components, the system is designed for
affordability, adaptability, and autonomous operation. It
responds to real-time sensor input, making it suitable for
environments like warehouses, laboratories, and agricultural
fields. By combining mobility, perception, and manipulation
in one platform, the robot demonstrates how modern
robotics can enhance operational efficiency and reduce
manual labor, offering a scalable solution for diverse realworld applications~\cite{author2023}.
II. RELATED WORK
Numerous research efforts have explored robotic automation
for agricultural harvesting, particularly in fruit picking
applications. Early developments in this field focused on
robotic arms equipped with simple image processing
algorithms for detecting and plucking apples, grapes, and
citrus fruits under controlled conditions. For instance, Silwal
et al. developed an apple-picking robot using stereo vision
and a six-DOF arm, which demonstrated success in
structured environments but was limited by cost and
processing complexity [1]. Similarly, mobile harvesting
robots using wheeled platforms have been studied, offering
semi-autonomous navigation through orchard rows;
Aneeza Naz Toor
Department of Electrical, Electronics
& Telecommunication Engineering
The University of Engineering and
Technology, Lahore
Lahore, Pakistan
2021ee335@student.uet.edu.pk
however, most relied on high-end computing hardware and
lacked real-time adaptability in dynamic farm conditions.
More recent systems have incorporated vision-based AI,
leveraging convolutional neural networks for fruit detection.
These systems typically require powerful edge processors
such as NVIDIA Jetson or Raspberry Pi 4, which increase
the overall cost and energy consumption. While effective,
such solutions are often unaffordable or impractical for
small-scale farmers in developing countries. Additionally,
many robotic harvesters are either stationary or semi-mobile,
limiting their range and deployment flexibility.
In contrast, the system proposed in this paper addresses
these challenges by integrating low-cost components like
the ESP32-CAM, which supports AI inference at the edge,
and an Arduino Mega, which manages control operations
without relying on bulky external hardware. The robot’s
mechanical design is kept simple yet adaptable, featuring
Mecanum wheels for omnidirectional movement and a
pressure-sensitive gripper to prevent fruit bruising. Thus,
this work contributes a compact, affordable, and mobile
solution tailored for small to medium-sized orchards,
particularly in developing agricultural regions like Pakistan.
III. METHODOLOGY
A. System Overview
The proposed fruit harvesting system consists of a compact
mobile robot equipped with a 6-degree-of-freedom (6-DOF)
robotic arm, a two-fingered pressure-sensitive gripper, and a
vision module. The mobility of the system is achieved using
Mecanum
wheels,
which
allow
omnidirectional
movement—critical for navigating narrow rows in orchards.
Mounted on the mobile platform is the robotic arm, which
performs fruit-picking operations. The arm receives fruit
location input from an AI-powered vision system, while an
onboard microcontroller coordinates movement and
actuation. The robot is designed to identify ripe fruits,
approach them gently, pluck them without causing bruises,
and place them into a collection basket. All components are
optimized to operate efficiently in structured agricultural
environments where fruits grow within a low to medium
canopy height.
B. Mechanical Design
The robotic arm follows a PRRRRR (Prismatic-RevoluteRevolute-Revolute-Revolute-Revolute)
configuration,
allowing a combination of vertical lift and rotational
flexibility to approach the fruit from different angles. The
mechanical parts of the arm and gripper were modeled using
SolidWorks, and STL files were generated for 3D printing.
Each link and joint were carefully dimensioned to ensure the
arm could extend, rotate, and grip fruits within a reachable
workspace of up to 2.5 meters vertically. The gripper, which
mimics human fingers, is designed to be soft and adaptive. It
includes force-sensitive resistors (FSRs) embedded within
the gripping pads to ensure fruits are grasped with
appropriate pressure, avoiding bruising or detachment of
unripe produce. The modular design allows easy
maintenance and potential scalability for different fruit sizes
or environments.
D. Kinematic Modeling
To enable precise positioning of the end-effector (gripper),
both inverse and forward kinematics were implemented.
Inverse kinematics calculations are used to determine the
required joint angles (θ₃ to θ₆ and vertical offset D₁) based
on the position of the detected fruit. These calculations are
coded in MATLAB, where custom trigonometric
expressions were derived to handle the PRRRRR
configuration. Forward kinematics, using the Denavit–
Hartenberg (DH) convention, was applied to validate the
joint trajectories and to simulate the robot’s motion. Each
transformation matrix from the base to the end-effector was
computed symbolically and multiplied in sequence to derive
the final position. Simulations demonstrated that the endeffector could reach fruit targets with high spatial accuracy.
Additionally, MATLAB’s plotting functions were used to
visualize the 3D trajectory of the arm across multiple
movement steps, confirming the theoretical reach of the
robot in a simulated orchard environment.
C. Electronics and Control
The system’s control logic is implemented on an Arduino
Mega 2560 microcontroller, selected for its wide I/O
availability and compatibility with multiple actuators and
sensors. The arm’s servos and stepper motors are controlled
through pulse-width modulation (PWM) signals generated
by the Arduino, based on position inputs derived from the
vision system. The fruit detection module is powered by an
ESP32-CAM, which uses a lightweight AI model trained on
Edge Impulse to classify and locate fruits. The ESP32 sends
the fruit’s coordinates wirelessly to the Arduino using a
custom UART-based protocol. To ensure safe interaction
with the fruit, the FSR-equipped gripper provides feedback
to the Arduino to modulate grip force dynamically. This
ensures gentle harvesting, particularly important for softskinned fruits like guava or citrus. Communication between
components is optimized to minimize latency, and the
control logic supports manual override if needed during
field testing.
Fig 2: Flow Chart for the System
IV. RESULTS AND DISCUSSION
A. Simulations Results
The forward kinematics model of the 6-DOF robotic arm
was simulated in MATLAB using derived Denavit–
Hartenberg parameters and symbolic transformation
matrices. Sample joint values, including base offset.
D1=225D_1 = 225D1​ =225 mm and joint angles
θ3=θ4=θ5=θ6=45∘ , were used to evaluate the arm's
reachability. The simulated output returned joint positions
and the end-effector location in 3D space.
Fig 1: System Block Diagram
The calculated coordinates were stored in the form of XXX,
YYY, and ZZZ arrays, and plotted using MATLAB’s plot3
function. The resulting trajectory plot showed a smooth,
continuous path from the robot base to the fruit position.
This validated the correctness of the inverse kinematics
model and demonstrated the robot’s ability to perform
coordinated joint movements to reach specific points in
space.
Fig 3: Arm Movement 1
Fig 6: Fruit Harvesting Robot
C. Discussion
The robotic system proved effective in semi-structured
orchard environments where fruit-bearing branches are
within reachable height. The integration of kinematic
modeling and real-time vision feedback allowed the robot to
detect, approach, and pluck fruits with minimal damage.
The use of Mecanum wheels provided flexibility in
navigating tight tree rows, enhancing the robot’s field
usability.
Fig 4: Arm Movement 2
Despite its success, the system still faces several challenges.
The current height limitation prevents it from accessing
fruits on tall trees, and performance drops under low-light
conditions. Future improvements such as a vertical
extension mechanism and enhanced lighting or thermal
sensing could help overcome these limitations. Nevertheless,
the prototype demonstrates a promising step toward scalable,
cost-efficient robotic fruit harvesting suitable for small and
mid-sized farms.
Fig 5: Arm Movement 3
B. Prototype Testing
The vision system, implemented using an ESP32-CAM and
trained via Edge Impulse, was tested in indoor and semioutdoor environments. The AI model demonstrated an
average fruit detection accuracy of approximately 88% for
ripe citrus and guava fruits under normal lighting conditions.
The response time for detection and communication with the
Arduino controller was measured at less than 300 ms, which
was acceptable for semi-real-time operation.
Mechanical testing of the gripper showed a successful
picking rate of around 75% during trial runs. Most failures
were due to either fruit misclassification or improper grip
alignment. Additionally, while the system performed well
for fruits located between 0.8 m and 2.0 m, it struggled to
reach higher branches, which currently limits its use in taller
orchards.
Fig 6: Edge Impulse Dataset Overview
V. CONCLUSION AND FUTURE SCOPE
A. Success in Automating Part of the Harvesting Task
The development of the automated fruit harvesting robot
demonstrates the potential of low-cost robotics in solving
real-world agricultural challenges. The designed system
successfully integrates mechanical, electrical, and AI
components into a mobile platform capable of detecting and
plucking fruits from bushes and lower tree branches.
B. Summary of Work’s Impact
Simulation results confirmed that the robot’s arm can follow
accurate trajectories to target coordinates using inverse and
forward kinematics. Real-world testing showed effective
fruit detection using the ESP32-CAM and a reasonable
success rate in harvesting, especially for fruits located below
2.5 meters. This confirms that the robot can reduce manual
labor and improve harvesting efficiency in semi-structured
orchard environments.
C. Future Work
However, limitations still exist in the current version,
particularly in reaching higher fruits and operating under
poor lighting. These challenges open doors for future
enhancement. Planned improvements include integrating a
vertical extension mechanism for taller trees, using solarpowered modules for sustainable operation, and
implementing SLAM-based navigation for full autonomy in
larger orchards.
In conclusion, the project lays a strong foundation for
affordable, modular agricultural robots tailored to smallscale farming needs in developing regions. With future
upgrades, this system can evolve into a versatile and
intelligent farm assistant.
ACKNOWLEDGMENTS
We wish to extend our sincere appreciation to Dr. Haris
Anwar for his exceptional mentorship, continuous support,
and insightful guidance throughout the duration of this
research project. His profound knowledge in the field of
robotics and automation, coupled with his unwavering
commitment to academic excellence, significantly
contributed to the successful development and completion of
this work. Dr. Haris Anwar provided critical feedback at
various stages of the project, helping to refine our research
objectives, strengthen the methodology, and ensure the
technical rigor of the proposed system. His thoughtful
suggestions and encouragement were instrumental not only
in overcoming technical challenges but also in fostering a
spirit of innovation and perseverance within our team. We
are deeply grateful for his valuable time, intellectual
contributions, and professional support, without which this
research would not have reached its present form.
REFERENCES
[1]
R. Singh, L. Seneviratne, and I. Hussain, "A Deep Learning-Based
Approach to Strawberry Grasping Using a Telescopic-Link
Differential Drive Mobile Robot in ROS-Gazebo for Greenhouse
Digital Twin Environments," IEEE Access, vol. 12, pp. 19464–19479,
2024, doi: 10.1109/ACCESS.2024.3362145.
[2] Y. Yu, K. Zhang, H. Liu, and L. Yang, "Real-Time Visual
Localization of the Picking Points for a Ridge-Planting Strawberry
Harvesting Robot," IEEE Access, vol. 8, pp. 174764–174776, 2020,
doi: 10.1109/ACCESS.2020.3025252.
[3] T. Ilyas, A. Khan, M. Umraiz, and Y. Jeong, "Multi-Scale Context
Aggregation for Strawberry Fruit Recognition and Disease
Phenotyping," IEEE Access, vol. 9, pp. 149247–149258, 2021, doi:
10.1109/ACCESS.2021.3125786.
[4] R. Oshita, S. Shibata, T. Yamamoto, and S. Mu, "Development of
Harvesting Robot for Strawberry," in Proc. IEEE GCCE, pp. 129–132,
2023, doi: 10.1109/GCCE57775.2023.10304857.
[5] M. F. Ridho and Irwan, "Strawberry Fruit Quality Assessment for
Harvesting Robot using SSD Convolutional Neural Network," in Proc.
Int. Conf. Data Sci. Comput., pp. 195–200, 2021, doi:
10.1109/ICDSC54077.2021.9650142.
[6] S. Muharom, D. A. P. Wardhana, M. Septyan, A. D. Pambudi, et al.,
"Identifying Strawberry Ripeness Level Using Camera for Cartesian
Robot Application Based on Microcontroller," in Proc. Int. Conf. Voc.
Educ.
Electr.
Eng.
(ICVEE),
pp.
1–6,
2023,
doi:
10.1109/ICVEE58025.2023.10355189.
[7] A. Tafuro, M. E. Khan, M. Arfini, et al., "Strawberry Picking Point
Localization, Ripeness and Weight Estimation," in Proc. IEEE Int.
Conf. Robot. Autom. (ICRA), pp. 4524–4530, 2022, doi:
10.1109/ICRA46639.2022.9811654.
[8] J. Haejun, M. HaeJun, Y. Jeong, H. Kwon, et al., "Automated
Technology for Strawberry Size Measurement and Weight Prediction
Using AI," IEEE Access, vol. 12, pp. 104627–104639, 2024, doi:
10.1109/ACCESS.2024.3381234.
[9] S. Yuan, Y. Cao, and X. Cheng, "Research on Strawberry Quality
Grading Based on Object Detection and Stacking Fusion Model,"
IEEE Access, vol. 11, pp. 90621–90631, 2023, doi:
10.1109/ACCESS.2023.3297524.
[10] H. N. B. Ranasinghe, C. K. Sasika, A. L. Kulasekera, C. V.
Kulasekera, and P. C. Kohona, "Soft Pneumatic Grippers for
Reducing Fruit Damage During Strawberry Harvesting," in Proc.
IEEE
MERCon,
pp.
267–272,
2022,
doi:
10.1109/MERCon55376.2022.9854065.
[11] T. Fujinaga, "Cutting Point Detection for Strawberry Fruit Harvesting
and Truss Pruning by Agricultural Robot," in Proc. IEEE CASE, pp.
100–105, 2023, doi: 10.1109/CASE56687.2023.10260576.
[12] K. Gamage, J. K. Arachchige, S. Siriwardhana, and P. C. Kohona,
"Systems Engineering-Based Approach for the Development of a
Strawberry Harvesting Robot," in Proc. MERCon, pp. 159–164, 2023,
doi: 10.1109/MERCon58055.2023.10159124.
[13] J. Mapes, "Harvesting End-Effector Design with Spiral-Curve Teeth
and Picking Control," IEEE UCF Undergraduate Research Journal,
pp. 1–7, 2021.
[14] A. Smith et al., "Vision-Based Automated Strawberry Grasping with
a 6-DoF Delta Robot," in Proc. IEEE Int. Conf. Mechatronics, pp.
223–228, 2020.
[15] L. Zhang et al., "End-Effector Optimization for Multi-Berry Picking,"
in Proc. IEEE Int. Conf. Ind. Technol., pp. 602–607, 2021..
[16] D. Kim et al., "Multi-Modal Sensor Fusion for Outdoor Strawberry
Localization," in Proc. IEEE Sensors Conf., pp. 1214–1219, 2022.
[17] S. Gupta et al., "Deep Reinforcement Learning for Adaptive
Harvesting Path Planning," in Proc. IEEE Int. Conf. Autom. Sci. Eng.
(CASE), pp. 771–776, 2023.