Autonomous Tomato Harvesting With Top–Down Fusion Network for Limited Data

Abstract

Using robots for tomato truss harvesting represents a promising approach to agricultural production. However, incomplete acquisition of perception information and clumsy operations often result in low harvest success rates or crop damage. To address this issue, we designed a new method for tomato truss perception, an autonomous harvesting method, and a novel circular rotary cutting end-effector. The robot performs object detection and keypoint detection on tomato trusses using the proposed Top-down Fusion Network, making decisions on suitable targets for harvesting based on phenotyping and pose estimation. The designed end-effector moves gradually from the bottom up to wrap around the tomato truss, cutting the peduncle to complete the harvest. Experiments conducted in real-world scenarios for robotic perception and autonomous harvesting of tomato trusses show that the proposed method increases accuracy by up to 11.42% and 22.29% for complete and limited dataset conditions, compared to baseline models. Furthermore, we have implemented an automatic tomato harvesting system based on TDFNet, which reaches an average harvest success rate of 89.58% in the greenhouse.