A Data Augmentation Method Based on Generative Adversarial Networks for Grape Leaf Disease Identification

Liu, Bin; Tan, Cheng; Li, Shuqin; He, Jinrong; Wang, Hongyan

doi:10.1109/access.2020.2998839

Cited by 128 publications

(46 citation statements)

References 42 publications

(50 reference statements)

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“…In addition, dense connection was added to the generator and discriminator to enhance the information input of each layer and significantly improve the classification performance. Liu et al [36] added dense connections in GAN, and improved the loss function to generate grape leaves to achieve better recognition results in the same recognition network. However, the goal of GAN is to generate images that are as real as possible.…”

Section: Introductionmentioning

confidence: 99%

RAHC_GAN: A Data Augmentation Method for Tomato Leaf Disease Recognition

et al. 2021

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Accurate recognition of tomato diseases is of great significance for agricultural production. Sufficient and insufficient training data of supervised recognition neural network training are symmetry problems. A high precision neural network needs a large number of labeled data, and the difficulty of data sample acquisition is the main challenge to improving the performance of disease recognition. [l.]Moreover, the traditional data augmentation based on geometric transformation can obtain less information, and the generalization is not strong. In order to generate leaves with obvious disease feature and improve the performance of disease recognition, this paper analyzes and solves the problem of insufficient training samples in recognition network training, and proposes a new data augmentation method RAHC_GAN based on GAN, which is used to expand data and identify diseases. First, the proposed hidden variable is used to control the size of the disease area continuously, and the residual attention blocks are used to make the generated adversarial network pay more attention to the disease region in the leaf image, besides, a multi-scale discriminator is used to enrich the detailed texture of the generated image. Then, an expanded data set including original training set images and generated images by RAHC_GAN is established, which is used as the input of four kinds classification networks AlexNet, VGGNet, GoogLeNet and ResNet for performance evaluation. Experimental results show that RAHC_GAN can generate leaves with obvious disease feature, and the generated expanded data set can significantly improve the recognition performance of the classifier. After data augmentation, the recognition effect on the four classifiers is increased by 1.8%, 2.2%, 2.7%, and 0.4% respectively, which are higher than the comparison method. At the same time, the impact of expanded data with different ratio on the recognition performance was evaluated, and the method was extended to apple and grape diseased leaves. The proposed data augmentation method can simulate the distribution of tomato leaf diseases and improve the performance of disease recognition, and it may be extended to solve the problem of insufficient data in other plant research tasks.The tomato leaf data augmented by the traditional data augmentation methods based on geometric transformation usually contain less information, and the generalization is not strong. Therefore, a new data augmentation method, RAHC_GAN, based on generative adversarial networks is proposed in this paper, which is used to expand tomato leaf data and identify diseases. In this method, continuous hidden variables are added at the input of the generator, and the purpose is to continuously control the size of the generated disease area and to supplement the intra class information of the same disease. Additionally, the residual attention block is added to the generator to make it pay more attention to the disease region in the leaf image; a multi-scale discriminator is also used to enrich the detailed texture of the generated image and finally generate leaves with obvious disease features. Then, we use the images generated by RAHC_GAN and the original training images to build an expanded data set, which is used to train four kinds of recognition networks, AlexNet, VGGNet, GoogLeNet, and ResNet, and the performance is evaluated through the test set. Experimental results show that RAHC_GAN can generate leaves with obvious disease features, and the generated expanded data set can significantly improve the recognition performance of the classifier. Furthermore, the results of the apple, grape, and corn data set show that RAHC_GAN can also be used as a method to solve the problem of insufficient data in other plant research tasks.

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Section: Introductionmentioning

confidence: 99%

RAHC_GAN: A Data Augmentation Method for Tomato Leaf Disease Recognition

et al. 2021

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“…In the paper [34], four different kinds of grape leaf disease images were expanded by a novel Leaf GAN model. The experimental results showed that the Leaf GAN model could make the grape leaf disease images highlight the disease and generate enough grape leaf disease images.…”

Section: ) Generate Adversarial Network (Gans)mentioning

confidence: 99%

Plant Disease Detection and Classification by Deep Learning—A Review

Zhang

2021

IEEE Access

422

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Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition and pest range assessment, etc. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this paper, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this work will be a valuable resource for researchers who study the detection of plant diseases and insect pests. At the same time, we also discussed some of the current challenges and problems that need to be resolved. INDEX TERMSDeep learning, plant leaf disease detection, visualization, small sample, hyperspectral imaging.

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“…A maize leaf feature enhancement framework was designed first which enhanced the maize features under the complex environment and then designed an AlexNet architecture network named DMS-Robust AlexNet, which improved the capability of feature extraction combined with dilated convolution and multiscale convolution. Liu, B et al [7] proposed a generative adversarial network-based leaf disease identification model. This model generated images of four different leaf diseases for training, then fused DenseNet and instance normalization to identify real and fake disease images as well as feature extraction capability on grape leaf lesions.…”

Section: Introductionmentioning

confidence: 99%