Innovative Guava Leaf Spot Acuteness Assessment with YOLOv5 and Attention Model
Rishav Chandel, Sandeep Kumar Mogha, Shobhit, Prashant Singh, Purvansh Dongre
Abstract
The agriculture part of guava is very vulnerable to the leaf spot disease which results in low production and harmful farmer economy. We contribute a solid DL model for how guava leaf images are categorized through higher levels of disease acuteness in this study. The levels of leaves are: Level 1, 2, 3, and 4, the basis of our research is a database carefully organized and has 20,000 different pictures of Guava leaves taken in the various part or of the world. The ability of the model based on the YOLOv5 architecture and equipped with an attention block to achieve an overall accuracy of 96.48% proves itself the ability to distinguish diseases. Performance measures, like accuracy, recall, and F1-score present the overall accuracy and recall of the model. The classification tendencies of its help bring some light to confusion matrix. We benefit our evaluation when contrasting diagrams and a model of state-of-the-art comparison. Besides being valuable in identification of guava leaf spot, our work displays usage possibility in the management of the plant diseases.