Lightweight SCC-YOLO for Winter Jujube Detection and 3D Localization with Cross-Platform Deployment Evaluation

Meng Zhou, Yaohua Hu (), Anxiang Huang, Yiwen Chen, Xing Tong, Mengfei Liu and Yunxiao Pan
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Meng Zhou: School of Optoelectronic Engineering and Mechanical Engineering, Zhejiang A&F University, Hangzhou 311300, China
Yaohua Hu: School of Optoelectronic Engineering and Mechanical Engineering, Zhejiang A&F University, Hangzhou 311300, China
Anxiang Huang: School of Optoelectronic Engineering and Mechanical Engineering, Zhejiang A&F University, Hangzhou 311300, China
Yiwen Chen: School of Optoelectronic Engineering and Mechanical Engineering, Zhejiang A&F University, Hangzhou 311300, China
Xing Tong: School of Optoelectronic Engineering and Mechanical Engineering, Zhejiang A&F University, Hangzhou 311300, China
Mengfei Liu: School of Optoelectronic Engineering and Mechanical Engineering, Zhejiang A&F University, Hangzhou 311300, China
Yunxiao Pan: School of Optoelectronic Engineering and Mechanical Engineering, Zhejiang A&F University, Hangzhou 311300, China

Agriculture, 2025, vol. 15, issue 19, 1-24

Abstract: Harvesting winter jujubes is a key step in production, yet traditional manual approaches are labor-intensive and inefficient. To overcome these challenges, we propose SCC-YOLO, a lightweight method for winter jujube detection, 3D localization, and cross-platform deployment, aiming to support intelligent harvesting. In this study, RGB-D cameras were integrated with an improved YOLOv11 network optimized by ShuffleNetV2, CBAM, and a redesigned C2f_WTConv module, which enables joint spatial–frequency feature modeling and enhances small-object detection in complex orchard conditions. The model was trained on a diversified dataset with extensive augmentation to ensure robustness. In addition, the original localization loss was replaced with DIoU to improve bounding box regression accuracy. A robotic harvesting system was developed, and an Eye-to-Hand calibration-based 3D localization pipeline was implemented to map fruit coordinates to the robot workspace for accurate picking. To validate engineering applicability, the SCC-YOLO model was deployed on both desktop (PyTorch and ONNX Runtime) and mobile (NCNN with Vulkan+FP16) platforms, and FPS, latency, and stability were comparatively analyzed. Experimental results showed that SCC-YOLO improved mAP by 5.6% over YOLOv11, significantly enhanced detection precision and robustness, and achieved real-time performance on mobile devices while maintaining peak throughput on high-performance desktops. Field and laboratory tests confirmed the system’s effectiveness for detection, localization, and harvesting efficiency, demonstrating its adaptability to diverse deployment environments and its potential for broader agricultural applications.

Keywords: winter jujube; SCC-YOLO; 3D localization; lightweight detection; cross-platform deployment (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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