Performance Comparasion of DenseNet-121 and MobileNetV2 for Cacao Fruit Disease Image Classification

Abstract

Introduction: Cacao fruit diseases significantly impact cocoa yield and quality in Indonesia. Early detection is critical, yet traditional methods are often time-consuming and error-prone due to the visual similarity of disease symptoms. This study aims to compare the performance of two Convolutional Neural Network (CNN) architectures—DenseNet-121 and MobileNetV2—for automated image classification of cacao fruit diseases. Methods: The dataset comprises 8000 images augmented from 2000 original photos taken in cocoa plantations in Bali, categorized into four classes: fruit rot, fruit-sucking pests, pod borers, and healthy fruits. Both CNN models were trained using the Adam optimizer, a learning rate of 0.001, and a dropout rate of 0.4. The input images were resized to 224×224 pixels. Evaluation metrics included accuracy, precision, recall, and F1-score. Results: DenseNet-121 outperformed MobileNetV2 across all metrics. DenseNet-121 achieved an accuracy of 94.50%, precision of 94.75%, recall of 94.25%, and F1-score of 94.50%. In comparison, MobileNetV2 reached an accuracy of 93.88%. Although MobileNetV2 offered faster training time and lower model complexity, DenseNet-121 demonstrated superior feature extraction and stability, supported by its deeper architecture and greater parameter capacity. Conclusions: DenseNet-121 is more effective than MobileNetV2 in classifying cacao fruit diseases, providing higher accuracy and robustness. Despite requiring more computational resources, it is better suited for developing a web-based cocoa disease detection tool to assist farmers in timely and accurate disease identification.