Design and Development of a Neural Network-Based End-Effector for Disease Detection in Plants with 7-DOF Robot Integration

This study presents the design and development of an intelligent end-effector integrated
into a custom 7-degree-of-freedom (DOF) robotic arm for monitoring the health status of
tomato plants during their growth stages. The robotic system combines five rotational and
two prismatic joints, enabling both horizontal reach and vertical adaptability to inspect
plants of varying heights without repositioning the robot’s base. The integrated vision
module employs a YOLOv5 neural network trained with 7864 images of tomato leaves,
including both healthy and diseased samples. Image preprocessing included normalization
and data augmentation to enhance robustness under natural lighting conditions. The
optimized model achieved a detection accuracy of 90.2% and a mean average precision
(mAP) of 92.3%, demonstrating high reliability in real-time disease classification. The
end-effector, fabricated using additive manufacturing, incorporates a Raspberry Pi 4 for
onboard processing, allowing autonomous operation in agricultural environments. The
experimental results validate the feasibility of combining a custom 7-DOF robotic structure
with a deep learning-based detector for continuous plant monitoring. This research
contributes to the field of agricultural robotics by providing a flexible and precise platform
capable of early disease detection in dynamic cultivation conditions, promoting sustainable
and data-driven crop management.