Comparative Analysis of Deep Learning Models for Plant Disease Detection

The detection of plant diseases through deep learning models repre- sents a significant advancement in agricultural management. This study pro- vides a comprehensive accuracy comparison of four prominent deep learning models—Convolutional Neural Networks (CNN), AlexNet, DenseNet, and VGG16—for identifying plant diseases from leaf images. Leveraging the PlantVillage dataset, which includes over 11,254 images of healthy and dis- eased leaves, the research investigates the strengths and limitations of each model in terms of accuracy, feature extraction, and classification performance. DenseNet's densely connected architecture and VGG16's deep layers are high- lighted for their superior ability to handle complex patterns in diseased leaves. The study demonstrates that DenseNet achieves the highest accuracy, making it a viable solution for real-time disease detection in precision agriculture. By comparing these models, the research aims to guide the selection of the most effective deep learning approach for improving plant health monitoring.