基于渐进式生成对抗网络的农作物病虫害细粒度分类

doi:10.13733/j.jcam.issn.2095-5553.2024.03.022

摘要/Abstract

摘要： 随着深度学习应用的普及和飞速发展，基于深度学习的图像识别方法广泛应用于农作物病虫害领域，但大部分的神经网络重视识别准确率的提高，却忽略神经网络庞大的参数计算量。为解决这个问题，基于渐进式生成对抗网络判别器模型和卷积注意力模块，提出一种改进的渐进式生成对抗网络判别器CPDM网络模型对农作物病虫害进行识别。通过对渐进式生成对抗网络判别器网络结构的调整，采用均衡学习率、像素级特征向量归一化和卷积注意力模块增强CPDM网络模型的特征提取能力，提高对真实图片的识别准确率。试验在PlantVillage数据集上进行，将该模型与VGG16、VGG19和ResNet18进行比较，得到TOP1准确率分别为99.06%、96.50%、96.65%、98.86%，分别提高2.56%、2.41%、0.2%，且参数量仅为8.2 M。试验证明提出的CPDM网络模型满足在保证分类准确率的基础上，有效控制神经网络参数计算量的目的。

关键词: 农作物病虫害, 渐进式生成对抗网络, 卷积注意力模块, 细粒度分类

Abstract: With the popularity and rapid development of deep learning applications, image recognition methods based on deep learning are widely used in the field of crop diseases and insect pests. However, most neural networks attach importance to the improvement of recognition accuracy, but ignore the huge parameter computation amount of neural networks. In order to solve this problem, based on the progressive growing of GANs discriminator model and convolutional attention module. an improved CPDM network model was proposed to identify crop pests and diseases. By adjusting the network structure of the progressive growing of GANs discriminator, the feature extraction capability of CPDM network model was enhanced by using balanced learning rate, pixellevel feature vector normalization and convolutional attention module, and the recognition accuracy of real images was improved. The experiment was carried out on the PlantVillage dataset, and compared with VGG16, VGG19 and ResNet18, the TOP1 accuracy was 99.06%, 96.50%, 96.65% and 98.86%, respectively, which was improved by 2.56%, 2.41% and 0.2%, respectively. And the number of parameters was only 8.2 M. The experimental results show that the proposed CPDM network model meets the purpose of effectively controlling the calculation amount of neural network parameters on the basis of ensuring the classification accuracy.

Key words: crop diseases and pests, progressive growing of GANs, CBAM, finegrained classification

中图分类号:

S435: TP391

邓昀, 冯琦尧, 牛照文, 康燕萍, . 基于渐进式生成对抗网络的农作物病虫害细粒度分类[J]. 中国农机化学报, 2024, 45(3): 156-162.

Deng Yun, , Feng Qiyao, , Niu Zhaowen, , Kang Yanping, . Finegrained classification of crop diseases and pests based on progressive growing of GANs[J]. Journal of Chinese Agricultural Mechanization, 2024, 45(3): 156-162.

参考文献

［1］　Barbedo J. Factors influencing the use of deep learning for plant disease recognition ［J］. Biosystems Engineering, 2018, 172: 84-91.
［2］　翟肇裕, 曹益飞, 徐焕良, 等. 农作物病虫害识别关键技术研究综述［J］. 农业机械学报, 2021, 52(7): 1-18.
Zhai Zhaoyu, Cao Yifei, Xu Huanliang, et al. Review of key techniques for crop disease and pest detection ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(7): 1-18.
［3］　Mohanty S P, Hughes D P, Salathe M. Using deep learning for imagebased plant disease detection ［J］. Frontiers in Plant Science, 2016, 7.
［4］　Lü M, Zhou G, He M, et al. Maize leaf disease identification based on feature enhancement and DMSRobust Alexnet ［J］. IEEE Access, 2020, 8: 57952-57966.
［5］　Pandian J A, Geetharamani G, Annette B. Data augmentation on plant leaf disease image dataset using image manipulation and deep learning techniques ［C］. 2019 IEEE 9th International Conference on Advanced Computing (IACC). IEEE, 2019: 199-204.
［6］　Zeng Q, Ma X, Cheng B, et al. Gansbased data augmentation for citrus disease severity detection using deep learning ［J］. IEEE Access, 2020, 8: 172882-172891.
［7］　项小东, 翟蔚, 黄言态, 等. 基于XceptionCEMs神经网络的植物病害识别［J］. 中国农机化学报, 2021, 42(8): 177-186.
Xiang Xiaodong, Zhai Wei, Huang Yantai, et al. Plant disease recognition based on XceptionCEMs neural network ［J］. Journal of Chinese Agricultural Mechanization, 2021, 42(8): 177-186.
［8］　王美华, 吴振鑫, 周祖光. 基于注意力改进CBAM的农作物病虫害细粒度识别研究［J］. 农业机械学报, 2021, 52(4): 239-247.
Wang Meihua, Wu Zhenxin, Zhou Zuguang. Finegrained identification of crop pests and diseases based on improved CBAM via attention ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(4): 239-247.
［9］　贾少鹏, 高红菊, 杭潇. 基于深度学习的农作物病虫害图像识别技术研究进展［J］. 农业机械学报, 2019, 50(S1): 313-317.
Jia Shaopeng, Gao Hongju, Hang Xiao. Research progress on image recognition technology of crop pests and diseases based on deep learning ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(S1): 313-317.
［10］　Karras T, Aila T, Laine S, et al. Progressive growing of gans for improved quality, stability, and variation ［J］. arXiv Preprint arXiv: 1710.10196, 2017.
［11］　Creswell A, White T, Dumoulin V, et al. Generative adversarial networks: An overview ［J］. IEEE Signal Processing Magazine, 2018, 35(1): 53-65.
［12］　金耀, 徐阳, 韩飞杨, 等. 基于深度学习语义分割的桥梁病害图像像素级识别方法［J］. 公路交通科技(应用技术版), 2020, 16(1): 183-188.
Jin Yao, Xu Yang, Han Feiyang, et al. Pixellevel recognition method for bridge disease images based on deep learning semantic segmentation ［J］. Journal of Highway and Transportation Research and Development (Application Technology Edition), 2020, 16(1): 183-188.
［13］　Woo S, Park J, Lee J Y, et al. CBAM: Convolutional block attention module ［C］. Proceedings of the European Conference on Computer Vision (ECCV), 2018: 3-19.