Affiliations: R. C. Patel Institute of Technology, Shirpur, Maharashtra, India
Correspondence:
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Corresponding author: Sandip Sonawane, R. C. Patel Institute of Technology, Shirpur, Maharashtra 425405, India. E-mail: sandipsonawane2006@gmail.com.
Abstract: In the face of a growing global population, optimizing agricultural practices is crucial. One major challenge is weed infestation, which significantly reduces crop yields and increases production costs. This paper presents a novel system for weed-crop classification and image detection specifically designed for sesame fields. We leverage the capabilities of Convolutional Neural Networks (CNNs) by employing and comparing different modified YOLO based object detection models, including YOLOv8, YOLO NAS, and the recently released Gold YOLO. Our investigation utilizes two datasets: a publicly available weed image collection and a custom dataset we meticulously created containing sesame plants and various weed species commonly found in sesame fields. The custom dataset boasts a significant size of 2148 images, enriching the training process. Our findings reveal that the YOLOvv8 model surpasses both YOLO NAS and Gold YOLO in terms of key evaluation metrics like precision, recall and mean average precisions. This suggests that YOLOv8 demonstrates exceptional potential for real-time, on-field weed identification in sesame cultivation, promoting informed weed management strategies and ultimately contributing to improve agricultural yield.
