PLYS-Longan: A Picking Point Localization Model for Longan in Natural Environments

Longan is an important economic fruit in tropical and subtropical regions, whose harvesting primarily relies on manual labor. Automated longan harvesting is key to improving the industry’s economic benefits but faces core challenges: mature pericarp is highly similar in color to fruiting mother branches, plus dense branches and severe leaf occlusion, leading to difficult cluster detection and fruiting branch segmentation. Herein, we propose a picking point localization method named PLYS-Longan integrating three customized core modules: Dynamic Convolution, Convolutional Gated Linear Unit (CGLU), and Dynamic Hyperbolic Tangent Activation (DYT) are introduced into YOLongan module to enhance the model’s ability to detect longan clusters. For SELongan module, Depthwise Over-parameterized Convolution (DO-Conv) and Ultra-light Subspace Attention (ULSA) are adopted to improve main branch segmentation precision. The PCLongan module then performs morphological erosion on the segmentation masks and calculates centroids to precisely determine the picking points. Experimental results show that the improved model achieves a mAP@50 of 90.1% (3.3% higher than baseline model) in object detection and a mIoU of 77.24% (1.75% improvement) in semantic segmentation, outperforming the various model significantly. This study provides an efficient and robust solution for longan picking point localization, laying a solid foundation for subsequent automated harvesting.