An Effective Learning Model for Multi-label Melon Classification based on Ensemble Learning

Melons are a popular fruit with various textures and types. Categorizing them before sale enables consumers make informed choices and enhances product appeal. 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. This advances in automated agriculture and inspires future research.