<![CDATA[Eggplant (Solanum melongena) is an important agricultural commodity with high economic value. However, various leaf diseases can hinder its growth and reduce crop yields. Therefore, rapid and accurate identification and classification of leaf diseases are crucial for improving agricultural productivity. This study proposes the use of the K-Nearest Neighbors (KNN) method for classifying eggplant leaf diseases based on image analysis. The model is developed using color histogram features extracted from leaf images as the basis for classification. This research involves collecting a dataset of eggplant leaf images with various disease categories, extracting color features using RGB and HSV color models, and implementing a KNN model with k=3k=3k=3. The model's performance is evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results show that the KNN model achieves an accuracy of approximately 87%, but challenges remain, such as dataset imbalance and misclassification of disease classes with similar color patterns. To improve accuracy, this study explores data augmentation techniques and optimizes the KNN model parameters. This research aims to enhance the effectiveness of KNN in detecting and classifying eggplant leaf diseases, ultimately assisting farmers in managing their crops more efficiently and effectively.]]>
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