Comparative Analysis of Deep Learning Models for Guava Leaf Disease Diagnosis

Guava leaf diseases significantly affect crop yield and quality, making timely detection essential for effective disease management. This project presents an end-to-end software system for automated guava leaf disease detection using deep learning and transfer learning techniques. Multiple pretrained convolutional neural network (CNN) architectures, including ResNet, AlexNet, VGG, SqueezeNet, DenseNet, Inception-v3, and EfficientNet, were adapted through feature extraction and trained on a guava leaf image dataset.

The system allows users to either capture an image using a camera or upload an existing leaf image through a software interface. The input image is processed and classified by the trained deep learning model, and the predicted disease class is displayed to the user. The dataset was divided into training, validation, and test sets to ensure robust performance evaluation, and final test accuracy was used to measure generalization on unseen data.

Experimental results demonstrate that transfer learning enables accurate and efficient guava leaf disease classification. Among the evaluated models, the best-performing architecture achieved an accuracy between 97% to 99%. Overall, the developed software provides a practical and user-friendly solution for real-world agricultural disease diagnosis.