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While humans can classify melons by sight, this process is highly inefficient for large quantities. Automated deep-learning agricultural systems offer solutions to reduce costs and increase productivity. Therefore, this study addresses the problem of reduced recognition accuracy caused by multiple textures in a single instance. Specifically, it proposes a multi-label classification method. Four models were trained on our dataset: a custom CNN, VGG16, InceptionV3, and a decision tree. Using voting aggregation techniques, we combined their strengths to produce multi-label outputs. Our method achieved impressive results, with an accuracy of 94%, a precision of 94%, and an F1 score of 93%. Additionally, this work introduced a specialized CNN model for melon rind recognition, further improving accuracy by integrating existing techniques with voting ensemble learning. 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