用AI精准识别地上的板栗，助力低成本自动采摘
Detection of On-Ground Chestnuts Using Artificial Intelligence Toward Automated Picking
- 采用29种先进实时检测模型，系统评估板栗识别效果
- YOLOv12m在复杂环境下实现95.1%的检测准确率
- 公开数据集与代码，适合农业自动化研究者使用
传统机械化板栗采摘对小农户成本过高、选择性差且易损伤果实。精确可靠的地面板栗检测是发展低成本视觉引导自动化采摘技术的关键。然而，复杂环境中的阴影、光照变化及杂草、落叶、石块等干扰物使检测难以实现，长期未被解决。本研究采集了319张果园地面板栗图像，共标注6524个板栗。系统评估了29种前沿实时目标检测模型，包括14个YOLO（v1-v13）和15个RT-DETR（v1-v4）系列不同规模模型。实验表明，YOLOv12m在所有模型中达到最高[email protected]为95.1%，而RT-DETRv2-R101为该系列最准确模型，[email protected]达91.1%；在mAP@[0.5:0.95]指标下，YOLOv11x表现最优，达80.1%。所有模型均具备实时检测潜力，且YOLO系列在精度与推理速度上均优于RT-DETR，更适合车载部署。本研究的数据集与代码已公开于https://github.com/AgFood-Sensing-and-Intelligence-Lab/ChestnutDetection。
原文摘要 · Abstract (English)
Traditional mechanized chestnut harvesting is too costly for small producers, non-selective, and prone to damaging nuts. Accurate, reliable detection of chestnuts on the orchard floor is crucial for developing low-cost, vision-guided automated harvesting technology. However, developing a reliable chestnut detection system faces challenges in complex environments with shading, varying natural light conditions, and interference from weeds, fallen leaves, stones, and other foreign on-ground objects, which have remained unaddressed. This study collected 319 images of chestnuts on the orchard floor, containing 6524 annotated chestnuts. A comprehensive set of 29 state-of-the-art real-time object detectors, including 14 in the YOLO (v11-13) and 15 in the RT-DETR (v1-v4) families at varied model scales, was systematically evaluated through replicated modeling experiments for chestnut detection. Experimental results show that the YOLOv12m model achieves the best [email protected] of 95.1% among all the evaluated models, while the RT-DETRv2-R101 was the most accurate variant among RT-DETR models, with [email protected] of 91.1%. In terms of mAP@[0.5:0.95], the YOLOv11x model achieved the best accuracy of 80.1%. All models demonstrate significant potential for real-time chestnut detection, and YOLO models outperformed RT-DETR models in terms of both detection accuracy and inference, making them better suited for on-board deployment. Both the dataset and software programs in this study have been made publicly available at https://github.com/AgFood-Sensing-and-Intelligence-Lab/ChestnutDetection.
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