Comparative Analysis on Deep Learning Models for Plant Disease Detection

Plant disease identification is critical for lowering output losses and guaranteeing food security. This study looks at the use of four common transfer learning models for early plant disease detection: MobileNet, VGG19, AlexNet, and ResNet. The study compares their ability in properly distinguishing damaged plant leaves from healthy ones. The emphasis is on assessing their performance with minimal training data, which is a typical difficulty in agricultural datasets. The accuracy, precision, recall, and F1-score obtained by each model on diverse plant disease datasets are presented in the study. The paper lays out each model’s merits and limitations, underlining its fit for various circumstances.