<![CDATA[Apple plants have high economic value, but their productivity is often disrupted by leaf diseases that can reduce quality and yield. Apple leaf disease identification is still largely performed manually, which is prone to errors and requires specialized expertise. Therefore, a method is needed to improve the accuracy and efficiency of apple leaf disease classification. This study aims to enhance the accuracy of apple leaf disease classification by implementing the Convolutional Neural Network (CNN) architecture, specifically Inception V3. The method involves collecting images of infected apple leaves, data preprocessing, and model training and evaluation. The results show that the Inception V3 model achieved an accuracy of 96%, which is higher than previous methods. The main advantage of this architecture lies in its ability to capture features at multiple scales simultaneously, improving the model’s ability to recognize disease patterns more accurately. With these findings, this study contributes to the development of AI-based plant disease detection technology and provides a practical solution for farmers to enhance apple farming productivity.]]>
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