Design and implementation of a seed potato cutting robot using deep learning and delta robotic system with accuracy and speed for automated processing of agricultural products

Potatoes, along with rice and soy, are among the most widely consumed staple crops worldwide. Seed potatoes are traditionally manually cut, affecting the consistency and efficiency of the process given ever-increasing demand. To address this problem, we developed and evaluated an automated potato cutting robot system. The system employs a Potato Orientation Detection You Only Look Once (POD-YOLO) deep learning model to identify the pose, boundaries, and key eye locations of seed potatoes. Intelligent cutting path planning is achieved through a strategy that combines clustering analysis with objective function optimization, and cutting is performed by a Delta parallel robot. Precise visual guidance is enabled through camera-robot calibration based on a homography matrix. Performance evaluation reveals that static visual guidance positioning errors are mostly within ±0.5 mm. The selected cutting strategy demonstrates strong performance in terms of cutting uniformity and coverage rate. A maximum cutting success rate of 85 % is achieved for round potatoes, and the system’s average cycle time is approximately 2.14 s, resulting in a throughput of about 418.8 kg/h, roughly three times that of a skilled manual labor. While the results validate the technical feasibility of the system, several challenges remain, including incomplete visual data due to a single viewpoint, dynamic positioning errors from the conveyor, and limitations of using a single-cutting tool. This research presents a comprehensive solution and empirical evidence, highlighting directions for optimization including multi-sensor fusion, dynamic error compensation, and advanced cutting mechanisms. The source codes are at: https://github.com/Jie-Huangi/seed-potato-cutting-robot.