非洲真实多植物数据集上深度学习目标检测模型的对比评估
A Comparative Evaluation of Deep Learning Object Detection Models on a Real-World Multi-Plant Dataset from Africa
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中文总结 AI 辅助
该研究针对非洲真实农业场景的多植物数据集，对比评估YOLO系列、Faster R-CNN、RT-DETR等6种目标检测模型，发现RT-DETR性能最优，YOLO系列训练效率更高，为农业植物检测提供了可靠方案。
中文摘要 AI 辅助
计算机视觉在农业中的应用在提升作物监测和精准农业方面展现出巨大潜力，但现有许多方法依赖的受控数据集无法充分代表真实农业条件，尤其是在非洲等代表性不足的地区。本研究使用从尼日利亚农场手动采集的真实世界数据集AgriAISeg 1，对六种目标检测模型YOLOv5、YOLOv8、YOLO11、YOLO26、Faster R-CNN和RT-DETR进行对比评估。AgriAISeg包含3382张芝麻、卷心菜和番茄作物的图像，这些图像是在不同环境条件下拍摄的，包括光照变化、遮挡和视角变化。对模型进行训练后，使用精确率、召回率、mAP@0.5和mAP@0.5:0.95评估性能。结果显示，RT-DETR的整体性能最高，精确率为0.768，mAP@0.5:0.95为0.624；YOLOv8和YOLO11也表现出强劲且稳定的性能。相比之下，Faster R-CNN的准确率明显较低，整体mAP@0.5为0.466，表明其在复杂田间条件下的有效性降低。此外，基于YOLO的模型相比Faster R-CNN表现出更优的训练效率。这些发现表明，现代单阶段检测器和基于Transformer的检测器为真实世界农业环境中的植物检测提供了更可靠、高效的解决方案。
英文摘要
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, mAP@0.5, and mAP@0.5:0.95. The results show that RT-DETR achieved the highest overall performance with a precision of 0.768 and mAP@0.5:0.95 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 mAP@0.5 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.
发表机构
- Bayero University（巴耶罗大学）
- Alpen-Adria-Universität Klagenfurt（克拉根福阿尔卑斯-亚得里亚大学）
- Gombe State University（贡贝州立大学）
- Aliko Dangote University of Science and Technology（阿里科·丹格特科技大学）
- Ahmadu Bello University（艾哈迈杜·贝洛大学）
- Federal University Dutse（杜塞联邦大学）
机构由 AI 辅助整理，请以论文原文为准。
