COMPARISON OF MOBILENETV2 AND RESNET50 PERFORMANCE IN CLASSIFICATION OF SHALLOT PLANT DISEASES

Abstract

Red onions (Allium ascalonicum L.) are a high-value agricultural commodity in Indonesia, but their productivity is often compromised by plant diseases, particularly yellow spot and leaf blight. Visual identification in the field is subjective, prone to error, and time-consuming. Using artificial intelligence, this problem is addressed with a Convolutional Neural Network (CNN) trained to identify disease types from images. For this purpose, this study applies transfer learning to two CNN architectures, MobileNetV2 and ResNet50, to classify red onion leaf diseases into three classes yellow spot, leaf curl, and healthy leaves. A dataset of 1,752 field-captured, pre-labeled images sourced from a public Roboflow repository was split into 70% for training, 20% for validation, and 10% for testing. Both models were trained using the Adam optimizer with a learning rate of 0.001 and a batch size of 32. Evaluation was conducted using accuracy, precision, recall, and F1-score metrics based on the confusion matrix. The results show that ResNet50 achieved a test accuracy of 93.33% and an average F1-score of 0.93, outperforming MobileNetV2, which achieved 90.26% accuracy and an average F1-score of 0.90. MobileNetV2 excels in computational efficiency, achieving faster training times in field implementation than ResNet50, which required longer training times for the 20 epoch run. However, in general, ResNet50 demonstrates better generalization, with a stable training curve, and outperforms MobileNetV2 in terms of accuracy, precision, recall, and F1-score for the classification of onion diseases based on leaf images.