<![CDATA[This research develops and implements an image classification method using the Residual Network (ResNet) architecture to identify potato leaf diseases, achieving an accuracy of around 97%. The dataset used consists of 2152 potato leaf images, categorized into three classes: early blight, late blight, and healthy. The selected model is ResNet-50, known for its ability to address the vanishing gradient problem, allowing for the training of very deep networks. The model training process involves data augmentation to enhance dataset diversity and prevent overfitting. Additionally, hyperparameter optimization was performed to maximize the model's performance. Evaluation of the model shows that ResNet-50 can achieve an accuracy of approximately 97% on the test data, indicating the model's high capability in accurately recognizing and classifying the condition of potato leaves. These results demonstrate the significant potential of using ResNet in plant disease image classification applications, which is crucial for decision-making in agricultural management. This research underscores the importance of deep network architectures and data augmentation techniques in improving the performance of deep learning models.]]>
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