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.

该研究使用卷积神经网络（CNN）通过深度学习对马铃薯叶病进行分类。实验结果显示，该 CNN 模型在识别早疫病、晚疫病和健康叶片三种马铃薯叶病时具有高达 99.1% 的整体准确率，可在严重感染情况下准确识别不同类型的病变，为马铃薯病害的有效管理和自动化防治提供了潜力。