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

TL;DR

This study evaluates lightweight CNN models for rice leaf disease detection, demonstrating that EfficientNet-B0 achieves near-perfect accuracy on mobile devices, advancing practical green AI applications in agriculture.

Contribution

The paper introduces an optimized lightweight CNN architecture with added layers and early stopping, achieving high accuracy for rice disease classification on mobile-compatible models.

Findings

EfficientNet-B0 achieved 99.8% accuracy.

MobileNetV2 and ShuffleNet achieved 84.21% and 66.51% accuracy.

Proposed enhancements improve model performance and prevent overfitting.

Abstract

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;