A modified YOLO-based approach for classification and detection of crop-weed in sesame crops

Weed plants pose a major threat in modern agriculture as they vie with primary crops for vital resources. They contribute to higher agricultural expenditure and diminished farm productivity, thereby influencing global agricultural economy. This manuscript proposes a system for classification and detection of crop and weed in sesame crops. In this system, different versions of Convolutional Neural Networks-based You Only Look Once (YOLO) object detection methods have been modified and the performance of YOLOv5, YOLOv6, and YOLOv7 compared. The proposed work utilized two datasets: a public weed dataset and a custom (own-created) dataset. The public weed dataset comprises 1,300 images, while the custom dataset includes 2,148 real-time images. Our investigation demonstrates that the YOLOv5 algorithm outperforms YOLOv6 and YOLOv7 algorithms in terms of evaluation measures like mean average precisions (mAPs), precision and recall. The YOLO models, particularly YOLOv5, demonstrated notable promise for the identification of weed in sesame crops. The efficacy of proposed approach is compared with that of existing approaches.