<![CDATA[Cocoa is one of Indonesia’s leading commodities, but its productivity often declines due to pest and disease infestations. This study develops a lightweight, Transfer Learning-based MobileNetV2 model for automatic cocoa disease identification for real-time detection. The dataset consists of 405 images across four classes: black pod rot (108), healthy (106), Helopeltis (100), and pod borer (91), divided into 70% training, 10% validation, and 20% testing. During the training phase, data augmentation techniques—including RandomFlip, Random Rotation, RandomZoom, and RandomTranslation—were applied to increase the visual variation of the samples. The training configuration utilized the Adam optimizer, a batch size of 32, a random seed of 45, and early stopping. The study tested variations in learning rate (0.001, 0.0001) and epochs (30, 50, 70, 100), yielding the best performance at a learning rate of 0.0001 with 70 epochs. The model achieved a test accuracy of 92.86%, precision of 92.78%, recall of 92.86%, and an F1-score of 92.79%. These results demonstrate that MobileNetV2 is capable of consistently identifying disease characteristics, making it highly promising as the foundation for a fast and accurate field diagnostic application for cocoa farmers.]]>
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