Field-Specific Deep Learning Solutions for Accurate Potato Leaf Disease Classification

This work employs a convolutional neural network (CNN) and deep learning to classify potato leaf diseases. The suggested methodology consists of preprocessing the leaf image data, utilizing that data to train a CNN model, and assessing the model's performance on a test set. The trial's findings show that the CNN model has an overall accuracy of 97.1% in correctly identifying two forms of potato leaf diseases: Healthy and Late Blight. The suggested approach might offer a dependable and effective way to diagnose potato infections, which is essential to ensuring food security and minimizing financial losses in agriculture. The program can correctly distinguish between various disease types even when there are serious infections present. The potential of deep learning techniques for classifying potato diseases is demonstrated by this work, which may aid in efficient and automated disease management in potato cultivation.