Mobilenetv2 Analysis in Classification Diseases On Mango Leaves - Simangunsong, Roy Candra; Muhathir (Universitas Medan Area, 2025)
This study aims to analyze the performance of the MobileNetV2 model in classifying diseases on mango leaves, consisting of three classes: capmodium, collectricu, and normal leaves. The dataset used contains 1500 images, with 80% allocated for training data, 10% for testing data, and 10% for validation data. The model was trained using a deep learning approach to identify mango leaf diseases based on the visual patterns present in each class. The results show that the MobileNetV2 model achieved an accuracy of 90%, a precision of 91%, a recall of 90%, and an F1-score of 89%. These findings highlight the potential of MobileNetV2 as an effective tool for automatically detecting mango leaf diseases. Therefore, this study is expected to contribute to the development of technology-based solutions in the agricultural sector, particularly in supporting farmers in identifying diseases quickly and accurately, thereby improving mango crop productivity.