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GANs-Based Data Augmentation for Citrus Disease Severity Detection Using Deep Learning

文献类型： 外文期刊

作者： Zeng, Qingmao 1 ; Ma, Xinhui 2 ; Cheng, Baoping 3 ; Zhou, Erxun 4 ; Pang, Wei 5 ;

作者机构： 1.South China Agr Univ, Coll Math & Informat, Dept Math, Guangzhou 510642, Peoples R China

2.Univ Hull, Dept Comp Sci & Technol, Kingston Upon Hull HU6 7RX, N Humberside, England

3.Guangdong Acad Agr Sci, Guangdong Prov Key Lab High Technol Plant Protect, Guangzhou 510640, Peoples R China

4.South China Agr Univ, Guangdong Prov Key Lab Microbial Signals & Dis Co, Guangzhou 510642, Peoples R China

5.Heriot Watt Univ, Sch Math & Comp Sci, Edinburgh EH14 4AS, Midlothian, Scotland

关键词： Diseases; Training; Machine learning; Data models; Feature extraction; Computational modeling; Plants (biology); Citrus Huanglongbing; data augmentation; deep learning; generative adversarial networks; plant disease severity

期刊名称：IEEE ACCESS （ 影响因子：3.367； 五年影响因子：3.671 ）

ISSN： 2169-3536

年卷期： 2020 年 8 卷

页码：

收录情况： SCI

摘要： Recently, many Deep Learning models have been employed to classify different kinds of plant diseases, but very little work has been done for disease severity detection. However, it is more important to master the severities of plant diseases accurately and timely, as it helps to make effective decisions to protect the plants from being further infected and reduce financial loss. In this paper, based on the Huanglongbing (HLB)-infected leaf images obtained from PlantVillage and crowdAI, we created a dataset with 5,406 citrus leaf images infected by HLB. Then six different kinds of popular models were trained to perform the severity detection of citrus HLB with the goal to find which types of models are more suitable to detect HLB severity with the same training circumstance. The experimental results show that the Inception_v3 model with epochs=60 can achieve higher accuracy than that of other models for severity detection with an accuracy of 74.38% due to its highly computational efficiency and small number of parameters. Additionally, aiming for evaluating whether GANs-based data augmentation can contribute to improve the model learning performance, we adopted DCGANs (Deep Convolutional Generative Adversarial Networks) to augment the original training dataset up to two times itself. Finally, a new training dataset with 14,056 leaf images composed by the original training images and the augmented ones were used to train the Inception_v3 model. As a result, we achieved an accuracy of 92.60%, about 20% higher than that of the Inception_v3 model trained by the original training dataset, which suggested that the GANs-based data augmentation is very useful to improve the model learning performance.

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