Development of an Optimized YOLO-PP-Based Cherry Tomato Detection System for Autonomous Precision Harvesting

作     者：Qin, Xiayang Cao, Jingxing Zhang, Yonghong Dong, Tiantian Cao, Haixiao 

作者机构：Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Peoples R China Wuxi Siasun Robot & Automat Co Ltd Wuxi 214101 Peoples R China 

出 版 物：《PROCESSES》 (Process.)

年 卷 期：2025年第13卷第2期

页      面：353-353页

核心收录：

基　　金：National Natural Science Foundation of China Science and Technology Development Fund Project of Wuxi [N20221002] Postgraduate Research & Practice Innovation Program of Jiangsu Province [SJCX24_0466] 42175157 42475151 

主　　题：keypoint detection YOLO v8 tomato detection facility agriculture attention mechanism deep learning 

摘      要：An accurate and efficient detection method for harvesting is crucial for the development of automated harvesting robots in short-cycle, high-yield facility tomato cultivation environments. This study focuses on cherry tomatoes, which grow in clusters, and addresses the complexity and reduced detection speed associated with the current multi-step processes that combine target detection with segmentation and traditional image processing for clustered fruits. We propose YOLO-Picking Point (YOLO-PP), an improved cherry tomato picking point detection network designed to efficiently and accurately identify stem keypoints on embedded devices. YOLO-PP employs a C2FET module with an EfficientViT branch, utilizing parallel dual-path feature extraction to enhance detection performance in dense scenes. Additionally, we designed and implemented a Spatial Pyramid Squeeze Pooling (SPSP) module to extract fine features and capture multi-scale spatial information. Furthermore, a new loss function based on Inner-CIoU was developed specifically for keypoint tasks to further improve detection *** model was tested on a real greenhouse cherry tomato dataset, achieving an accuracy of 95.81%, a recall rate of 98.86%, and mean Average Precision (mAP) scores of 99.18% and 98.87% for mAP50 and mAP50-95, respectively. Compared to the DEKR, YOLO-Pose, and YOLOv8-Pose models, the mAP value of the YOLO-PP model improved by 16.94%, 10.83%, and 0.81%, respectively. The proposed algorithm has been implemented on NVIDIA Jetson edge computing devices, equipped with a human-computer interaction interface. The results demonstrate that the proposed Improved Picking Point Detection Network exhibits excellent performance and achieves real-time accurate detection of cherry tomato harvesting tasks in facility agriculture.