Leaves image synthesis using generative adversarial networks with regularization improvement

Purbaya, Muhammad Eka; Setiawan, Noor Akhmad; Adji, Teguh Bharata

doi:10.1109/icoiact.2018.8350780

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…The DCGAN (deep convolution generative adversarial network) [25] used a CNN (convolutional neural network) to replace the multilayer perceptron in GAN to improve the quality of the generated images. Purbaya et al [26] used a regularization method to improve DCGAN and generated plant leaf data to prevent the model from overfitting. Although DCGAN improves the quality of the generated images, it cannot control the classes of the generated images, limiting the generation of multi-class images.…”

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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“…22,28 For the purpose of obtaining large number of plant seedling images, SL Madsen et al 28 designed WacGAN using a supervised conditioning scheme that enabled the model to produce visually distinct samples for multiple classes. ME Purbaya et al 29 30 to generate citrus canker images to improve the accuracy of the classification network. 31 G Hu et al 11 segmented disease spots from tea leaf's disease images using SVM; subsequently, data augmentation was implemented by means of one improved conditional deep convolutional generative adversarial network (C-DCGAN) for generating new samples.…”

Section: Related Workmentioning

confidence: 99%

Identification of cucumber leaf diseases using deep learning and small sample size for agricultural Internet of Things

Zhang

Rao

Chao

et al. 2021

International Journal of Distributed Sensor Networks

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Due to the complex environments in real fields, it is challenging to conduct identification modeling and diagnosis of plant leaf diseases by directly utilizing in-situ images from the system of agricultural Internet of things. To overcome this shortcoming, one approach, based on small sample size and deep convolutional neural network, was proposed for conducting the recognition of cucumber leaf diseases under field conditions. One two-stage segmentation method was presented to acquire the lesion images by extracting the disease spots from cucumber leaves. Subsequently, after implementing rotation and translation, the lesion images were fed into the activation reconstruction generative adversarial networks for data augmentation to generate new training samples. Finally, to improve the identification accuracy of cucumber leaf diseases, we proposed dilated and inception convolutional neural network that was trained using the generated training samples. Experimental results showed that the proposed approach achieved the average identification accuracy of 96.11% and 90.67% when implemented on the data sets of lesion and raw field diseased leaf images with three different diseases of anthracnose, downy mildew, and powdery mildew, significantly outperforming those existing counterparts, indicating that it offered good potential of serving field application of agricultural Internet of things.

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“…Zhu et al [33] used a conditional GAN setup to create artificial images of Arabidopsis plants, with the focus on the improvement of leaf counting. Purbaya et al [34] used a GAN to synthesize leaf images and improve regularization. Ward et al [35] used generated images to augment the training set and improved the accuracy of leaf-image segmentation.…”

Section: Related Workmentioning

confidence: 99%

Data Enhancement for Plant Disease Classification Using Generated Lesions

et al. 2020

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Deep learning has recently shown promising results in plant lesion recognition. However, a deep learning network requires a large amount of data for training, but because some plant lesion data is difficult to obtain and very similar in structure, we must generate complete plant lesion leaf images to augment the dataset. To solve this problem, this paper proposes a method to generate complete and scarce plant lesion leaf images to improve the recognition accuracy of the classification network. The advantages of our study include: (i) proposing a binary generator network to solve the problem of how a generative adversarial network (GAN) generates a lesion image with a specific shape and (ii) using the edge-smoothing and image pyramid algorithm to solve the problem that occurs when synthesizing a complete lesion leaf image where the synthetic edge pixels are different and the network output size is fixed but the real lesion size is random. Compared with the recognition accuracy of human experts and AlexNet, it was shown that our method can effectively expand the plant lesion dataset and improve the recognition accuracy of a classification network.

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Leaves image synthesis using generative adversarial networks with regularization improvement

Cited by 6 publications

References 10 publications

RAHC_GAN: A Data Augmentation Method for Tomato Leaf Disease Recognition

RAHC_GAN: A Data Augmentation Method for Tomato Leaf Disease Recognition

Identification of cucumber leaf diseases using deep learning and small sample size for agricultural Internet of Things

Data Enhancement for Plant Disease Classification Using Generated Lesions

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