Authors - Lakshmi Priya G G, Gokulakrishnan. V, Nithin Joel. J, Padmalakshmi Govindarajan Abstract - Potatoes are among the most widely farmed crops globally. Healthy potato plants are crucial for the large-scale production of potato-derived foods. However, a vari ety of leaf diseases can harm potato plants, with Early Blight and Late Blight being the most prevalent. In this investigation, we employed a dataset of 1500 photos comprising healthy, early, and late blight leaves. For the diagnosis of leaf diseases, we used a transfer learning-based Ensemble Modeling. We selected Effi cientNetB0, ResNet50, MobileNetv2, and VGG16 as transfer learning models, integrating logistic regression as a meta-classifier within the Ensemble Model. We have attained 99.4% accuracy in distinguishing disease-affected leaves from healthy potato leaves, which is better than most of the recent works. For the per formance measurements, we employed accuracy, precision, recall, and F1-score. To ensure the credibility of our technique, we have integrated explainable AI (Grad-CAM) for our models, which indicates which parts of the image play a vital role in our model’s performance.
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