在非洲农田实拍数据上对比六种检测模型，RT-DETR表现最佳。
A Comparative Evaluation of Deep Learning Object Detection Models on a Real-World Multi-Plant Dataset from Africa
- 用真实非洲农田图像对比六种目标检测模型性能。
- RT-DETR在[email protected]:0.95达0.624，优于其他模型。
- YOLO系列训练快，适合实际农业场景应用。
计算机视觉在农业中展现出提升作物监测与精准农业的巨大潜力。然而，许多现有方法依赖受控数据集，无法充分反映非洲等欠发达地区的现实耕作条件。本研究基于在尼日利亚农场采集的真实世界数据集AgriAISeg（含3,382张芝麻、甘蓝和番茄图像，覆盖光照变化、遮挡和视角差异），对六种目标检测模型（YOLOv5、YOLOv8、YOLO11、YOLO26、Faster R-CNN、RT-DETR）进行了比较评估。使用精度、召回率、[email protected]和[email protected]:0.95进行性能评测。结果显示，RT-DETR整体表现最优，精度为0.768，[email protected]:0.95达0.624；YOLOv8和YOLO11也表现出强而稳定的性能。相反，Faster R-CNN总体[email protected]仅为0.466，表明其在复杂田间条件下效果较差。此外，基于YOLO的模型训练效率显著高于Faster R-CNN。研究证明，现代单阶段及基于Transformer的检测器在真实农业环境中更具可靠性与高效性。
原文摘要 · Abstract (English)
The application of computer vision in agriculture has shown significant potential for improving crop monitoring and precision farming. However, many existing approaches rely on controlled datasets that do not adequately represent realworld farming conditions, particularly in underrepresented regions such as Africa. This study presents a comparative evaluation of six object detection models YOLOv5, YOLOv8, YOLO11, YOLO26, Faster R-CNN, and RT-DETR using a real-world dataset, AgriAISeg 1 , collected manually from Nigerian farms. AgriAISeg comprises 3,382 images of sesame, cabbage, and tomato crops captured under varying environmental conditions, including changes in illumination, occlusion, and viewing perspectives. Models were trained, and performance was assessed using precision, recall, [email protected], and [email protected]:0.95. The results show that RT-DETR achieved the highest overall performance with a precision of 0.768 and [email protected]:0.95 of 0.624, while YOLOv8 and YOLO11 also demonstrated strong and consistent performance. In contrast, Faster R-CNN recorded significantly lower accuracy, with an overall [email protected] of 0.466, indicating reduced effectiveness under complex field conditions. In addition, YOLO-based models exhibited superior training efficiency compared to Faster R-CNN.These findings demonstrate that modern one-stage and transformer-based detectors provide more reliable and efficient solutions for plant detection in realworld agricultural environments.
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