Advancing Green AI: Efficient and Accurate Lightweight CNNs for Rice Leaf Disease Identification

• Main Authors: Saddami, Khairun, Nurdin, Yudha, Zahramita, Mutia, Safiruz, Muhammad Shahreeza
• Format: Journal Article
• Language: English
• Published: 03-08-2024
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Computer Vision and Pattern Recognition
• DOI: 10.48550/arxiv.2408.01752

Rice plays a vital role as a primary food source for over half of the world's
population, and its production is critical for global food security.
Nevertheless, rice cultivation is frequently affected by various diseases that
can severely decrease yield and quality. Therefore, early and accurate
detection of rice diseases is necessary to prevent their spread and minimize
crop losses. In this research, we explore three mobile-compatible CNN
architectures, namely ShuffleNet, MobileNetV2, and EfficientNet-B0, for rice
leaf disease classification. These models are selected due to their
compatibility with mobile devices, as they demand less computational power and
memory compared to other CNN models. To enhance the performance of the three
models, we added two fully connected layers separated by a dropout layer. We
used early stop creation to prevent the model from being overfiting. The
results of the study showed that the best performance was achieved by the
EfficientNet-B0 model with an accuracy of 99.8%. Meanwhile, MobileNetV2 and
ShuffleNet only achieved accuracies of 84.21% and 66.51%, respectively. This
study shows that EfficientNet-B0 when combined with the proposed layer and
early stop, can produce a high-accuracy model.
Keywords: rice leaf detection; green AI; smart agriculture; EfficientNet;