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Convolutional Rebalancing Network for the Classification of Large Imbalanced Rice Pest and Disease Datasets in the Field

文献类型： 外文期刊

作者： Yang, Guofeng 1 ; Chen, Guipeng 1 ; Li, Cong 1 ; Fu, Jiangfan 1 ; Guo, Yang 1 ; Liang, Hua 1 ;

作者机构： 1.Jiangxi Acad Agr Sci, Inst Agr Econ & Informat, Nanchang, Jiangxi, Peoples R China

2.Jiangxi Engn Res Ctr Informat Technol Agr, Nanchang, Jiangxi, Peoples R China

关键词： imbalanced dataset; convolutional neural network; image classification; feature fusion; rice pests and diseases

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：5.754； 五年影响因子：6.612 ）

ISSN： 1664-462X

年卷期： 2021 年 12 卷

页码：

收录情况： SCI

摘要： The accurate classification of crop pests and diseases is essential for their prevention and control. However, datasets of pest and disease images collected in the field usually exhibit long-tailed distributions with heavy category imbalance, posing great challenges for a deep recognition and classification model. This paper proposes a novel convolutional rebalancing network to classify rice pests and diseases from image datasets collected in the field. To improve the classification performance, the proposed network includes a convolutional rebalancing module, an image augmentation module, and a feature fusion module. In the convolutional rebalancing module, instance-balanced sampling is used to extract features of the images in the rice pest and disease dataset, while reversed sampling is used to improve feature extraction of the categories with fewer images in the dataset. Building on the convolutional rebalancing module, we design an image augmentation module to augment the training data effectively. To further enhance the classification performance, a feature fusion module fuses the image features learned by the convolutional rebalancing module and ensures that the feature extraction of the imbalanced dataset is more comprehensive. Extensive experiments in the large-scale imbalanced dataset of rice pests and diseases (18,391 images), publicly available plant image datasets (Flavia, Swedish Leaf, and UCI Leaf) and pest image datasets (SMALL and IP102) verify the robustness of the proposed network, and the results demonstrate its superior performance over state-of-the-art methods, with an accuracy of 97.58% on rice pest and disease image dataset. We conclude that the proposed network can provide an important tool for the intelligent control of rice pests and diseases in the field.

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