Lightweight vision transformer and ResNet-9 models for real-time plant disease detection and pest classification with SHAP explainability
This study develops a ViT–CNN model for real-time plant disease detection, achieving 97.4% accuracy. Using SHAP for interpretability, it balances accuracy and speed, supporting sustainable farming, early diagnosis, reduced chemical use, and improved food security.
