Yang Guofeng, Chen Guipeng, Li Cong, Fu Jiangfan, Guo Yang, Liang Hua
Institute of Agricultural Economics and Information, Jiangxi Academy of Agricultural Sciences, Nanchang, China.
Jiangxi Engineering Research Center for Information Technology in Agriculture, Nanchang, China.
Front Plant Sci. 2021 Jul 5;12:671134. doi: 10.3389/fpls.2021.671134. eCollection 2021.
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.
农作物病虫害的准确分类对其防治至关重要。然而，在田间收集的病虫害图像数据集通常呈现长尾分布且类别严重不平衡，这给深度识别和分类模型带来了巨大挑战。本文提出了一种新颖的卷积重平衡网络，用于对从田间收集的图像数据集中的水稻病虫害进行分类。为了提高分类性能，所提出的网络包括一个卷积重平衡模块、一个图像增强模块和一个特征融合模块。在卷积重平衡模块中，使用实例平衡采样来提取水稻病虫害数据集中图像的特征，同时使用反向采样来改进数据集中图像数量较少的类别的特征提取。基于卷积重平衡模块，我们设计了一个图像增强模块来有效地增强训练数据。为了进一步提高分类性能，一个特征融合模块融合了卷积重平衡模块学习到的图像特征，并确保对不平衡数据集的特征提取更加全面。在大规模水稻病虫害不平衡数据集（18391张图像）、公开可用的植物图像数据集（Flavia、瑞典树叶和UCI树叶）以及害虫图像数据集（SMALL和IP102）上进行的大量实验验证了所提出网络的鲁棒性，结果表明其性能优于现有方法，在水稻病虫害图像数据集上的准确率达到97.58%。我们得出结论，所提出的网络可以为田间水稻病虫害的智能防治提供一个重要工具。
