A novel green apple segmentation algorithm based on ensemble U-Net under complex orchard environment

Abstract

Fruit segmentation is a critical step for fruit recognition, and key to the efficiency of a robotic vision system for harvesting and accurate orchard yield estimation. Due to the unstructured characteristics of orchard environment, the segmentation performance of traditional segmentation algorithms is insufficient for handling green target fruits in a complex environment. Deep learning algorithms bring a fresh perspective to target segmentation. This study proposes an ensemble U-Net segmentation model suitable for small sample datasets. Edge structures are designed by integrating residual blocks and gated convolutions to obtain the boundary semantic information of the target image; atrous convolutions are applied to resolve the contradiction between the resolution of the feature map and the receiving field, retain more multiscale context information and achieve target fruit segmentation. An atrous spatial pyramid pooling (ASPP) structure is applied to merge the edge features and the high-level features of U-Net. The experimental results show that the proposed method effectively improves the segmentation accuracy of the target fruit and the generalization ability of the model. The proposed method extends the application scope of the harvesting robot and orchard yield measurements, thereby providing a theoretical reference for other fruit and vegetable target fruit segmentation efforts.