Performance Comparison of MobileNetV2 and NASNetMobile Architectures in Soybean Leaf Disease Classification - I Gede Rian Lanang Oka; Anak Agung Gede Bagus Ariana; Wayan Sauri Peradhayana; Ni Luh Wiwik Sri Rahayu Ginantra; I Ketut Sutarwiyasa (yocto brain, 2025)
Soybean is one of the essential commodities in Indonesia, commonly used as a raw material for tofu and tempeh, making it highly sought after. However, soybean production has decreased by up to 30% due to disease attacks, necessitating preventive measures. This study aims to compare two Convolutional Neural Network (CNN) architectures, MobileNetV2 and NASNetMobile, in classifying soybean leaf diseases. The models were trained using a leaf image dataset collected directly from agricultural fields and categorized into five classes. The dataset underwent augmentation to increase its size, resulting in a total of 6,000 images, which were then split with an 80:10:10 ratio. The models were trained using the Adam optimizer with a learning rate of 0.001, optimized using ReduceLROnPlateau, and a dropout rate of 0.2 to prevent overfitting. Evaluation results using a confusion matrix indicated that MobileNetV2 performed better with an accuracy of 96.67%, precision of 96.70%, recall of 96.67%, and an F1-score of 96.68%, compared to NASNetMobile, which achieved an accuracy of 86.33%, precision of 86.91%, recall of 86.33%, and an F1-score of 86.40%.