Attention-driven YOLOv11n-DeiT model for enhanced detection of tomato leaf diseases.

Then, a genetic metaheuristic algorithm was used to automatically select the most significant hyperparameters, further optimizing both processing time and accuracy.

After that, the tomato leaf disease detection applies the You Only Look Once version 11 Nano (YOLOv11n) model.

The YOLOv11n backbone is edited through a Data-efficient Image Transformer (DeiT) to improve the system's capacity for learning global contextual information and long-range dependencies.

Additionally, it achieved an average mAP@50 of 87%, mAP@50-95 of 48%, precision of 83.9%, recall of 70.3%, and F1-score of 76.4% on the TLDDCV dataset.