Efficient Rice Leaf Disease Classification Using Enhanced CAE-CNN Architecture
DOI:
https://doi.org/10.38043/tiers.v6i2.7159Keywords:
Rice Leaf Disease, CAE, CNN, SE-Block, Lightweight Deep LearningAbstract
This study introduces an enhanced Convolutional Autoencoder–Convolutional Neural Network (CAE–CNN) model designed for efficient and accurate classification of rice leaf diseases. This study aims to develop an architecture that achieves high accuracy while maintaining computational efficiency, serving as an integrative and applicative technical innovation for rice disease detection. The proposed architecture integrates a Squeeze and Excitation Block (SE-Block), Global Max Pooling (GMP), and Separable Convolution to improve feature extraction while reducing the number of parameters and inference time. A total of 7,430 labeled images from five rice disease classes were used for model training and evaluation. The model was optimized using Optuna-based hyperparameter tuning and validated through an ablation and comparative analysis to assess the impact of each component. Experimental results show that the proposed model achieves 99.39% accuracy with only 85,859 parameters, a compact size of 0.28 MB, and inference time at 0.06657 ms/image with 15,213 FPS. These findings demonstrate that the proposed CAE–CNN effectively combines high accuracy and low computational cost, making it highly suitable for real-time and edge-based rice disease classification systems.
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Copyright (c) 2026 Destia Suhada, I Gede Pasek Suta Wijaya, Ida Bagus Ketut Widiartha, Minho Jo
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