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Plant disease phenotyping using technology assures a promising step in sustainable agriculture. Solanum Tubrosum (Potato) is a highly cultivated vegetable plant, which is affected by fungal pathogens that leads to Early Blight and Late Blight diseases. Continuous monitoring of plant disease is a challenging task in identifying diseased leaves. This paper proposes a novel deep Convolutional Neural Network (CNN) for classifying the potato disease by reducing the computational time of learnable parameters using image phenotyping. A meta-heuristic algorithm known as the Whale Optimization Algorithm (WOA) is used in training to optimize the hyperparameters of the proposed CNN network. Potato disease is classified using optimized CNN called POT-Net, and the performance is analyzed using performance metrics: precision, recall, F1-score, and accuracy of each class. The performance POT-Net is compared with pre-trained DL and optimized algorithms using performance metrics and it is better than the state-of-the-art models, with an accuracy of 99.12%.

POT-Net: solanum tuberosum (Potato) leaves diseases classification using an optimized deep convolutional neural network