Novel data augmentation strategies to boost supervised segmentation of plant disease

Douarre, Clément; Crispim-Junior, Carlos; Gélibert, Anthony; Tougne, Laure; Rousseau, D.

doi:10.1016/j.compag.2019.104967

Cited by 63 publications

(33 citation statements)

References 19 publications

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“…It has been proven to be able to generate images with a similar distribution to the original data, showing subversive performance in image generation [20][21][22][23]. Clément et al [24] used a GAN-based image augmentation method to enhance the training data and segmented the apple disease on tree crown with a smaller data set. Compared with the results without image generation, the F1 value was increased by 17%.…”

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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“…When using imbalanced datasets in the field of crop pests and diseases, some studies adopt simple image augmentation methods to augment images and balance datasets (Pandian et al, 2019 ; Kusrini et al, 2020 ), while other studies adopt GAN to generate related images and balance datasets (Douarre et al, 2019 ; Cap et al, 2020 ; Nazki et al, 2020 ; Zhu et al, 2020 ). Our image augmentation method focuses on spatially augmenting images of rice pests and diseases.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Rebalancing Network for the Classification of Large Imbalanced Rice Pest and Disease Datasets in the Field

et al. 2021

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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.

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“…In order to enhance data quantity and quality, data augmentation technique based on the state-of-the-art unsupervised Generative Adversarial Networks (GANs) has been developed [16][17][18]. The GANs mainly contain two networks, a generator and a discriminator network.…”

Section: Introductionmentioning

confidence: 99%

Automated Sewer Defects Detection Using Style-Based Generative Adversarial Networks and Fine-Tuned Well-Known CNN Classifier

et al. 2021

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Automated sewer defects detection has become an important trend for better management and maintenance of urban sewer systems. Deep learning technology has developed rapidly and offers an innovative solution for automated detection in engineering applications. However, insufficient data and unbalanced samples have proposed a big challenge to deep learning model training. This study adopts the state-of-the-art Style-based Generative Adversarial Networks (StyleGANs) model and compares the performances of its two variants in producing high-quality synthetic sewer defects images. Seven wellknown CNN models are further fine-tuned and trained using the synthetic images for automated sewer defects detection to examine the effects of StyleGANs on augmenting the detection performance. Results show that both StyleGANs are efficient in producing high-quality images with various styles and high-level details for multiple types of sewer defects. Specifically, the StyleGAN2-Adaptive Discriminator Augmentation (StyleGAN2-ADA) with the aid of Freeze Discriminator (Freeze-D) yields the best model performance. Among the adopted CNN classifiers, Inception_v3 achieves the highest detection accuracy. The mean detection accuracy is 94% (with a specific accuracy of 99.7%, 97%, 95.3% and 84% for tree root, residential wall, disjoint and obstacle, respectively) and confirms the reliability of the StyleGANs' performance. The study shows that StyleGANs provide a promising method to alleviate the limited and uneven dataset problem and can improve the deep learning model performance.

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Novel data augmentation strategies to boost supervised segmentation of plant disease

Cited by 63 publications

References 19 publications

RAHC_GAN: A Data Augmentation Method for Tomato Leaf Disease Recognition

RAHC_GAN: A Data Augmentation Method for Tomato Leaf Disease Recognition

Convolutional Rebalancing Network for the Classification of Large Imbalanced Rice Pest and Disease Datasets in the Field

Automated Sewer Defects Detection Using Style-Based Generative Adversarial Networks and Fine-Tuned Well-Known CNN Classifier

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