<![CDATA[Eggplant is a horticultural crop that is highly dependent on the health of its leaves to support growth and productivity. Leaf diseases can cause a significant reduction in crop yield if not detected early. This study aims to develop a leaf classification model for eggplant using the MobileNetV2 architecture to automatically detect leaf conditions. The model was trained using a public dataset of eggplant leaf images, with an 80% training and 20% validation data split. During the twenty-epoch training process, the model achieved a validation accuracy of 93%. The final model is stored in a lightweight format. The results of this study indicate that this approach is effective for detecting diseases in eggplant leaves and has the potential to support the implementation of responsive smart agriculture in the field.]]>
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