Vision-Based Positioning and Control of a Robotic Manipulator for Tomato Harvesting

Automated harvesting of tomatoes requires reliable integration of machine vision, distance measurement, and robotic-manipulator control to achieve accurate and repeatable product positioning. This study presents a vision-based positioning and control system for a robotic manipulator designed for automatic tomato harvesting. The proposed system integrates a webcam, Raspberry Pi 4 for image processing, a microcontroller for control, an HC-SR04 ultrasonic sensor for distance measurement, and servo motors for manipulator actuation. Image-based tomato coordinates are used to generate the required angular positions of the manipulator joints, while distance information is used to support the approach motion toward the detected product. A closed-loop control concept based on the comparison between commanded and actual angular positions is also considered. Experimental evaluation was performed using red and green tomatoes to assess detection, harvesting, and complete work-cycle times. The detection and harvesting times were 4.932 s for the red tomato and 5.276 s for the green tomato, while the complete cycle times were 9.676 and 10.586 s, respectively. For an image resolution of 320 × 180 pixels, an example object position of x = 15 pixels and y = 42 pixels corresponded to servo angles of 115° for the base joint and 100° for the first joint. The results demonstrate the feasibility of integrating visual perception, distance measurement, and manipulator motion control using a relatively simple hardware architecture. The proposed approach provides a basis for further development of accurate, repeatable, and energy-efficient robotic tomato harvesting systems.

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