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 mAP@0.5 of 95.1% among all the evaluated models, while the RT-DETRv2-R101 was the most accurate variant among RT-DETR models, with mAP@0.5 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.

翻译：传统机械化板栗采收方式对小规模生产者而言成本过高，缺乏选择性且易损伤果实。为实现低成本、视觉引导的自动化采收技术，对果园地面板栗进行精确可靠的检测至关重要。然而，在复杂果园环境中开发可靠的板栗检测系统面临诸多挑战：包括树荫遮挡、自然光照条件变化，以及杂草、落叶、石块和其他地面异物的干扰，这些问题尚未得到有效解决。本研究采集了319张果园地面板栗图像，包含6524个标注板栗样本。通过重复建模实验，系统评估了29种先进实时目标检测器的性能，涵盖14个YOLO系列（v11-13）和15个RT-DETR系列（v1-v4）不同规模的模型。实验结果表明：在所有评估模型中，YOLOv12m模型取得最佳mAP@0.5指标（95.1%）；而RT-DETR模型中，RT-DETRv2-R101变体以91.1%的mAP@0.5成为最准确版本。在mAP@[0.5:0.95]指标上，YOLOv11x模型以80.1%的准确率表现最优。所有模型均展现出实时板栗检测的显著潜力，其中YOLO系列在检测精度和推理速度方面均优于RT-DETR系列，更适用于车载部署场景。本研究的完整数据集与软件程序已在https://github.com/AgFood-Sensing-and-Intelligence-Lab/ChestnutDetection 开源发布。