Deep Learning-Based Soybean Leaf Disease Classification Using DenseNet121, Xception, and MobileNetV2
DOI:
https://doi.org/10.29207/resti.v9i6.6271Keywords:
data augmentation, DenseNet121, MobileNetV2, Xception, soybean leaf disease classificationAbstract
This study is driven by the challenge of soybean leaf diseases, which significantly reduce agricultural productivity and pose a threat to food security. To address this issue, we developed a deep learning–based classification model for soybean leaf disease detection, employing three prominent architectures: DenseNet121, Xception, and MobileNetV2. The dataset comprised 770 images representing six disease categories and one healthy category, which was expanded to 5,880 images using data augmentation techniques. The dataset was evaluated under three experimental scenarios with splits of 70% training, 10% validation, and 20% testing. Experimental results demonstrated that the DenseNet121 model, optimized with AdamW, achieved the highest accuracy at 90.14%, outperforming MobileNetV2 (85.48%) and Xception (65.37%). Moreover, DenseNet121 exhibited the most consistent performance in classifying the diverse categories of soybean leaf diseases.
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