想看真实农田里哪个检测模型靠谱？这篇用非洲实地数据测了六个模型，RT-DETR和YOLO系赢了，Faster R-CNN掉队，结论直接。
一项研究在非洲尼日利亚农田采集的AgriAISeg数据集上，对比了YOLOv5、YOLOv8、YOLO11、YOLO26、Faster R-CNN和RT-DETR六种目标检测模型。数据集包含3,382张芝麻、卷心菜和番茄图像，覆盖光照变化、遮挡和视角差异等真实条件。RT-DETR表现最佳，精度达0.768，mAP@0.5:0.95为0.624；YOLOv8和YOLO11表现稳定。Faster R-CNN的mAP@0.5仅0.466，准确率明显偏低。YOLO系列模型训练效率也优于Faster R-CNN。
A Comparative Evaluation of Deep Learning Object Detection Models on a Real-World Multi-Plant Dataset from Africa
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.