<![CDATA[Corn leaf diseases such as blight and rust can reduce crop yields if they are not detected at an early stage. In Penatahan Village, the process of identifying these diseases is still carried out manually through visual observation, which may lead to misidentification due to the similarity of symptoms between different diseases. Therefore, a technology-based system is needed to assist the identification process in a more objective and efficient manner. This study aims to classify corn leaf diseases using the Convolutional Neural Network (CNN) method based on digital leaf images. The dataset used consists of 319 images categorized into three classes: healthy, blight, and rust, with 80% of the data used for training and 20% for validation. The model was developed using a transfer learning approach with the MobileNetV2 architecture and evaluated using a confusion matrix. The experimental results indicate that the model achieved an accuracy of 92.19%, indicating that the CNN method is capable of effectively classifying corn leaf diseases. The developed system can be utilized as a tool to assist in the rapid and objective identification of corn leaf diseases.]]>
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