Data Augmentation on Plant Leaf Disease Image Dataset Using Image Manipulation and Deep Learning Techniques

Pandian, J. Arun; Geetharamani, G.; Annette, B

doi:10.1109/iacc48062.2019.8971580

Cited by 54 publications

(44 citation statements)

References 2 publications

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“…Besides, these basic image manipulation can be adapted from one image to more than one image (Dwibedi et al, 2017). Kuznichov et al Label-condition algorithms generate images from given labels by using generative adversarial networks (GANs) (Valerio Giuffrida et al, 2017;Pandian et al, 2019;Bi and Hu, 2020;Abbas et al, 2021). In contrast, image-condition algorithms produce images from given images.…”

Section: Preliminarymentioning

confidence: 99%

Style-Consistent Image Translation: A Novel Data Augmentation Paradigm to Improve Plant Disease Recognition

Yoon

Fuentes

et al. 2022

Front. Plant Sci.

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Deep learning shows its advantages and potentials in plant disease recognition and has witnessed a profound development in recent years. To obtain a competing performance with a deep learning algorithm, enough amount of annotated data is requested but in the natural world, scarce or imbalanced data are common, and annotated data is expensive or hard to collect. Data augmentation, aiming to create variations for training data, has shown its power for this issue. But there are still two challenges: creating more desirable variations for scarce and imbalanced data, and designing a data augmentation to ease object detection and instance segmentation. First, current algorithms made variations only inside one specific class, but more desirable variations can further promote performance. To address this issue, we propose a novel data augmentation paradigm that can adapt variations from one class to another. In the novel paradigm, an image in the source domain is translated into the target domain, while the variations unrelated to the domain are maintained. For example, an image with a healthy tomato leaf is translated into a powdery mildew image but the variations of the healthy leaf are maintained and transferred into the powdery mildew class, such as types of tomato leaf, sizes, and viewpoints. Second, current data augmentation is suitable to promote the image classification model but may not be appropriate to alleviate object detection and instance segmentation model, mainly because the necessary annotations can not be obtained. In this study, we leverage a prior mask as input to tell the area we are interested in and reuse the original annotations. In this way, our proposed algorithm can be utilized to do the three tasks simultaneously. Further, We collect 1,258 images of tomato leaves with 1,429 instance segmentation annotations as there is more than one instance in one single image, including five diseases and healthy leaves. Extensive experimental results on the collected images validate that our new data augmentation algorithm makes useful variations and contributes to improving performance for diverse deep learning-based methods.

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Section: Preliminarymentioning

confidence: 99%

Style-Consistent Image Translation: A Novel Data Augmentation Paradigm to Improve Plant Disease Recognition

Yoon

Fuentes

et al. 2022

Front. Plant Sci.

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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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“…(8) Noise injection. The 0-mean 0.01-variance Gaussian noises [27] were added to all training images to produce W new noised images:…”

Section: Improvement 3: L-way Data Augmentationmentioning

confidence: 99%

Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling

Zhang

Satapathy

et al. 2020

Complex Intell. Syst.

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Ductal carcinoma in situ (DCIS) is a pre-cancerous lesion in the ducts of the breast, and early diagnosis is crucial for optimal therapeutic intervention. Thermography imaging is a non-invasive imaging tool that can be utilized for detection of DCIS and although it has high accuracy (~ 88%), it is sensitivity can still be improved. Hence, we aimed to develop an automated artificial intelligence-based system for improved detection of DCIS in thermographs. This study proposed a novel artificial intelligence based system based on convolutional neural network (CNN) termed CNN-BDER on a multisource dataset containing 240 DCIS images and 240 healthy breast images. Based on CNN, batch normalization, dropout, exponential linear unit and rank-based weighted pooling were integrated, along with L-way data augmentation. Ten runs of tenfold cross validation were chosen to report the unbiased performances. Our proposed method achieved a sensitivity of 94.08 ± 1.22%, a specificity of 93.58 ± 1.49 and an accuracy of 93.83 ± 0.96. The proposed method gives superior performance than eight state-of-the-art approaches and manual diagnosis. The trained model could serve as a visual question answering system and improve diagnostic accuracy.

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Data Augmentation on Plant Leaf Disease Image Dataset Using Image Manipulation and Deep Learning Techniques

Cited by 54 publications

References 2 publications

Style-Consistent Image Translation: A Novel Data Augmentation Paradigm to Improve Plant Disease Recognition

Style-Consistent Image Translation: A Novel Data Augmentation Paradigm to Improve Plant Disease Recognition

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

Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling

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