Plant Disease Identification Using MobileNetV2 and InceptionV3

This research presents a method to enhance plant disease identification using Convolutional Neural Networks (CNNs) by leveraging a diverse dataset sourced from Kaggle, which contains 20,000 high-resolution images of healthy and diseased plants. The proposed model evaluates multiple CNN architectures, including ResNet50V2, MobileNetV2, InceptionV3, NASNetMobile, and an ensemble model combining MobileNetV2 and InceptionV3. The models were rigorously trained and assessed on their ability to accurately classify plant diseases. The results showed that ResNet50V2 achieved the highest accuracy at 92.26%, followed by the custom CNN at 88.22%, the ensemble model at 88.08%, MobileNetV2 at 87.93%, InceptionV3 at 82.26%, and NASNetMobile at 82.07%.