Potato Leaf Disease Classification using Deep Learning: A Convolutional
  Neural Network Approach
Utkarsh Yashwant Tambe A. Shobanadevi A. Shanthini Hsiu-Chun Hsu
In this study, a Convolutional Neural Network (CNN) is used to classify
potato leaf illnesses using Deep Learning. The suggested approach entails
preprocessing the leaf image data, training a CNN model on that data, and
assessing the model's success on a test set. The experimental findings show
that the CNN model, with an overall accuracy of 99.1%, is highly accurate in
identifying two kinds of potato leaf diseases, including Early Blight, Late
Blight, and Healthy. The suggested method may offer a trustworthy and effective
remedy for identifying potato diseases, which is essential for maintaining food
security and minimizing financial losses in agriculture. The model can
accurately recognize the various disease types even when there are severe
infections present. This work highlights the potential of deep learning methods
for categorizing potato diseases, which can help with effective and automated
disease management in potato farming.