A deep learning-based plant disease classification using image recognition techniques

Abstract Plant diseases significantly threaten global food security, necessitating accurate and automated diagnostic systems. This work presents a modular deep learning framework for systematic benchmarking and comparative analysis of multiple architectures using the PlantVillage dataset. The framework integrates pretrained models, including DenseNet121, MobileNetV2, VGG16, and DeiT. To enhance generalization and robustness, the proposed system incorporates dynamic architectural adaptation, dropout regularization, and 5-fold cross-validation. Experimental results demonstrate that DenseNet121 achieved the best performance with a weighted precision of 0.989, recall of 0.987, F1-score of 0.988, and an overall accuracy of 99.95%. Furthermore, the four benchmarked pretrained models (DenseNet121, MobileNetV2, VGG16, and DeiT) attained a mean cross-validation accuracy of 99.41% (standard deviation 0.815) across these four models, indicating stable and competitive classification performance across diverse plant disease categories. The proposed framework emphasizes reproducibility and extensibility, providing a strong foundation for future integration of Explainable AI (XAI) techniques such as Grad-CAM and deployment through accessible platforms like Streamlit.