Partition harvesting of a column-comb litchi harvester based on 3D clustering

Abstract

Accurate recognition of litchi fruits in orchard environments and acquisition of their coordinate position information are key for realizing successful harvesting using litchi harvesters. However, the existing detection methods are often aimed at large and relatively sparse fruits and thus are inappropriate for small and densely distributed litchi fruits. Therefore, at present, litchi fruit are typically manually harvested, resulting in a low harvesting efficiency that cannot meet the needs of growers. To improve the efficiency of litchi harvesting, this study proposes a column-comb litchi harvesting method based on K-means 3D clustering partitioning, which includes four main steps: (1) Litchi image acquisition and labeling methods are developed. (2) An improved version of the present YOLOv3-tiny network model structure is developed named the YOLOv3-tiny-Litchi network model, and the litchi fruit detection results of five kinds of neural networks, namely, YOLOv3-tiny, YOLOv3-tiny-Litchi, YOLOv4, YOLOv5x and Faster R-CNN, are compared. (3) A depth camera is used to obtain the 3D coordinates of litchi fruits, and the K-means clustering algorithm is used to divide the litchi harvesting area to obtain the optimal partitioning results. (4) Field experiments on litchi harvesting are reported. The experimental results show that the improved YOLOv3-tiny-Litchi model can recognize litchi fruits more accurately; the recall rate is 78.99%, the precision rate is 87.43%, and the F1 score is 0.83. The results of 3D clustering partitioning show that when K is equal to 6, the optimal harvesting rate is 90.03%, which satisfies the theoretical requirements of litchi harvesters. The field experiments show that when the theoretical number of partitions is 6, the average harvesting rate of the litchi harvester is 91.15% and the recognition rate is 88.39%, and the harvesting efficiency of mechanical partition harvesting is 1.4 times that of mechanical harvesting without partitioning and 2 times that of manual harvesting, essentially meeting the operating requirements. These research results can serve as a reference for the mechanization of litchi harvesting.