Attention-Based Deep Convolutional Neural Networks for Plant Disease Classification

Abstract

Abstract Plant diseases pose a significant threat to global food security and agricultural productivity. In this work, we propose a novel deep convolutional neural network (CNN) model enhanced with Squeeze-and-Excitation (SE) blocks and Attention Gates (AGs) for multi-class plant disease classification across five crops: apple, maize, grape, potato, and tomato. Leveraging a large image dataset and a comprehensive training regime, the proposed model achieves high performance across all metrics, including 99% accuracy, 0.99 F1-score, and strong specificity. Evaluation includes feature visualization and Grad-CAM interpretability. The model's robustness and interpretability make it a compelling solution for practical agricultural applications.

Code and data availability

The paper's plant disease classification experiments are built directly on the public PlantVillage Kaggle image dataset (21 classes across five crops), which is the paper-specific image input for its phenotyping measurements. No author analysis code, trained model checkpoints, or other paper-specific assets are stated.