Artificial Intelligence-based Detection of Fava Bean Rust Disease in Agricultural Settings: An Innovative Approach

The traditional methods used to identify plant diseases mostly rely on expert opinion, which causes long waits and enormous expenses in the control of crop field activities, especially given that majority infections now existence have tiny targets, occlusions, looks are similar those other diseases. To increase efficiency precision rust disease classification a fava bean field, new optimized multilayer deep learning model called YOLOv8 is suggested this study. 3296 images were collected from farm eastern Morocco for dataset. We labeled all data before training, evaluating, testing our model. results demonstrate developed using transfer has higher recognition than models, reaching 95.1%, can classify into three severity levels: healthy, moderate, critical. As performance indicators, needed standards mean Average Precision (mAP), recall, F1 score 93.7%, 90.3%, 92%, respectively. improved model's detection speed was 10.1 ms, sufficient real-time detection. This study first employ method find crops. Results encouraging supply opportunities research.