Rice Disease Classification Using a Stacked Ensemble of Deep Convolutional Neural Networks

文献类型： 外文期刊

第一作者： Wang, Zhibin

作者： Wang, Zhibin;Wei, Yana;Zhang, Yunhe;Qiao, Xiaojun;Wei, Yana;Mu, Cuixia

作者机构：

关键词： rice diseases; sustainable agriculture; stacking; EfficientNet; ensemble learning; lightweight

期刊名称：SUSTAINABILITY （ 影响因子：3.3； 五年影响因子：3.6 ）

ISSN：

年卷期： 2025 年 17 卷 1 期

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收录情况： SCI

摘要： Rice is a staple food for almost half of the world's population, and the stability and sustainability of rice production plays a decisive role in food security. Diseases are a major cause of loss in rice crops. The timely discovery and control of diseases are important in reducing the use of pesticides, protecting the agricultural eco-environment, and improving the yield and quality of rice crops. Deep convolutional neural networks (DCNNs) have achieved great success in disease image classification. However, most models have complex network structures that frequently cause problems, such as redundant network parameters, low training efficiency, and high computational costs. To address this issue and improve the accuracy of rice disease classification, a lightweight deep convolutional neural network (DCNN) ensemble method for rice disease classification is proposed. First, a new lightweight DCNN model (called CG-EfficientNet), which is based on an attention mechanism and EfficientNet, was designed as the base learner. Second, CG-EfficientNet models with different optimization algorithms and network parameters were trained on rice disease datasets to generate seven different CG-EfficientNets, and a resampling strategy was used to enhance the diversity of the individual models. Then, the sequential least squares programming algorithm was used to calculate the weight of each base model. Finally, logistic regression was used as the meta-classifier for stacking. To verify the effectiveness, classification experiments were performed on five classes of rice tissue images: rice bacterial blight, rice kernel smut, rice false smut, rice brown spot, and healthy leaves. The accuracy of the proposed method was 96.10%, which is higher than the results of the classic CNN models VGG16, InceptionV3, ResNet101, and DenseNet201 and four integration methods. The experimental results show that the proposed method is not only capable of accurately identifying rice diseases but is also computationally efficient.

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