🤖 AI Summary
This study addresses the limited real-world applicability of existing agricultural vision models, which are predominantly trained on idealized datasets and struggle in the complex, dynamic field conditions prevalent in under-resourced regions such as Africa. To bridge this gap, the authors present a systematic evaluation of six state-of-the-art object detection models—YOLOv5, YOLOv8, YOLO11, YOLO26, Faster R-CNN, and RT-DETR—on AgriAISeg, the first multi-crop, multi-challenge dataset collected directly from African farmlands. Experimental results demonstrate that RT-DETR achieves the highest performance with an mAP@0.5:0.95 of 0.624, while YOLO-family models exhibit consistently strong accuracy and training efficiency. In contrast, Faster R-CNN suffers significant performance degradation in complex scenarios. This work provides the first empirical evidence of the varying suitability of modern detectors in authentic African agricultural settings, offering critical guidance for deploying AI solutions in resource-constrained environments.
📝 Abstract
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.