A comparative study of yolov8 and faster r-CNN in potato, apple & grape leaf disease detection for precision

Abstract

This study investigates the use of deep learning models, specifically YOLOv8 and Faster R-CNN, for real-time object identification and autonomous plant disease diagnosis. The research uses a dataset consisting of 38 examples from 11 distinct classes, which include various plant species such as apple, grape, and potato. Within these categories, there are specific classes representing healthy conditions and various diseases. This diverse dataset, featuring 3171 apple images, 4063 grape images, and 2852 potato images, supports comprehensive training and evaluation of machine learning models for disease identification. A statistical analysis of the subset reveals different distributions of images across the classes, highlighting the prevalence and significance of certain diseases within each plant category. The effectiveness of YOLOv8 and Faster R-CNN is assessed using performance metrics like Intersection over Union (IoU), saliency score, and inference time. Although specific numerical values are not provided, the data indicate that YOLOv8 performs well in terms of IoU and achieves a higher saliency score compared to Faster RCNN. Conversely, Faster R-CNN shows superior IoU performance but with a lower saliency score. Additionally, YOLOv8 demonstrates faster inference times, while Faster R-CNN has significantly longer inference times.By comparing these metrics, the study provides valuable insights into the strengths of each model, guiding the selection and optimization of deep learning architectures for plant disease diagnosis. The research also emphasizes the trade-offs between speed and accuracy inherent in object identification models, underscoring the importance of considering application-specific requirements. Overall, this study advances agricultural technology by exploring the potential of deep learning models in combating harmful plant diseases. It lays the groundwork for future advancements in autonomous plant disease diagnosis and contributes to global food security by enhancing diagnostic techniques.