Potato Leaf Disease Classification using PoLIVR Model: A Novel Approach

India is among the world’s largest producers of the potato. However, early blight and late blight caused by biotic stresses can result in considerable potato yield loss. Traditional methods of leaf disease detection are cumbersome, inefficient and take a long time. Timely and accurate classification of leaf disease can play a major role in increasing crop yield and agricultural sustainability.

This study proposes an advanced PoLIVR approach for potato leaf illness classification using VGG-19 and Random Forest. This research used potato leaf images from a publicly available dataset. The raw images are pre-processed using grey scaling, denoising and bilateral filtering. VGG-19 is utilized to extract features from the pre-processed output of the pictures.

The proposed PoLIVR approach achieved 95.59% accuracy for illness classification with 96.41%, 91.44% and 93.64% precision, recall and F1-score. The obtained AUC values for the three classes were higher than 0.90, which suggests a strong positive power. The proposed approach also has a higher performance than the compared state-of-art work. These results highlight the potential of PoLIVR approach for timely and accurate leaf illness classification. Overall, the study will also serve as a reference tool for potato grower industry workers to identify diseases in different agricultural settings and facilitate crop protection decision-making.