Fruit quality and defect image classification with conditional GAN data augmentation

Bird, Jordan J.; Barnes, Chloe M.; Manso, Luis J.; Ekárt, Anikó; Faria, Diego R.

doi:10.1016/j.scienta.2021.110684

Cited by 91 publications

(33 citation statements)

References 45 publications

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“…Experimental results show that the enhanced data sets get higher detection accuracy. In order to alleviate the problem of data scarcity, Brid et al [121] adopted conditional GAN to synthesize images to enhance the dataset (Lemons Quality Control Dataset [122]), and finally achieved 88.75% defect classification accuracy. Even if the model is compressed to half the original size, the conditional GAN enhanced classification network can maintain the classification accuracy of 81.16%.…”

Section: Overview Of Gan-based Application In Defect Detection Of Agr...mentioning

confidence: 99%

A Survey of Defect Detection Applications Based on Generative Adversarial Networks

Chang²,

Zhang³

et al. 2022

IEEE Access

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Section: Overview Of Gan-based Application In Defect Detection Of Agr...mentioning

confidence: 99%

A Survey of Defect Detection Applications Based on Generative Adversarial Networks

Chang²,

Zhang³

et al. 2022

IEEE Access

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“…Second, they required complex optimization methods to find optimized parameters. According to mentioned challenges, deep learning methods are developed [33]. Deep learning methods used deep networks, i.e., CNNs to automatically learn features rather than manual setting parameters to obtain effective effects in image processing tasks, i.e., image classification [33], image inpainting [34] and image super-resolution [1].…”

Section: Developments Of Gansmentioning

confidence: 99%

“…According to mentioned challenges, deep learning methods are developed [33]. Deep learning methods used deep networks, i.e., CNNs to automatically learn features rather than manual setting parameters to obtain effective effects in image processing tasks, i.e., image classification [33], image inpainting [34] and image super-resolution [1]. Although these methods are effective big samples, they are limited for image tasks with small samples [29].…”

Section: Developments Of Gansmentioning

confidence: 99%

Generative Adversarial Networks for Image Super-Resolution: A Survey

Tian¹,

Zhang²,

Lin³

et al. 2022

Preprint

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Single image super-resolution (SISR) has played an important role in the field of image processing. Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images with small samples. However, there are little literatures summarizing different GANs in SISR. In this paper, we conduct a comparative study of GANs from different perspectives. We first take a look at developments of GANs. Second, we present popular architectures for GANs in big and small samples for image applications. Then, we analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised manners. Next, we compare performance of these popular GANs on public datasets via quantitative and qualitative analysis in SISR. Finally, we highlight challenges of GANs and potential research points for SISR.

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“…It is noted that the GAN images for each defect class were generated separately rather than through a multi-class data generation process, which can be potentially more efficient. Recently, Bird et al (2021) applied CGAN to generate synthetic lemon images for classifying healthy and unhealthy fruit, using a public lemon dataset (Adamiak, 2020). By using synthetic fruit images, the classification based on VGG16 achieved an accuracy of 88.75% against 83.77% without data augmentation.…”

Section: Postharvest Quality Assessmentmentioning

confidence: 99%

“…Compared to real samples, low-quality generated images may lack texture details and contain unrealistic, undesirable artifacts. In Bird et al (2021) for generating lemon images, many synthetic images were found more reminiscent of potatoes than lemons and some suffered from unrealistic checkboard patterns. Provision of sufficient samples for training GANs would facilitate generating high-quality, realistic images and eventually benefit DL models.…”

Section: Training With Limited Datamentioning

confidence: 99%

Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review

Olaniyi¹,

Chen²,

Lu³

et al. 2022

Preprint

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In agricultural image analysis, optimal model performance is keenly pursued for better fulfilling visual recognition tasks (e.g., image classification, segmentation, object detection and localization), in the presence of challenges with biological variability and unstructured environments. Large-scale, balanced and ground-truthed image datasets, however, are often difficult to obtain to fuel the development of advanced, high-performance models. As artificial intelligence through deep learning is impacting analysis and modeling of agricultural images, data augmentation plays a crucial role in boosting model performance while reducing manual efforts for data preparation, by algorithmically expanding training datasets. Beyond traditional data augmentation techniques, generative adversarial network (GAN) invented in 2014 in the computer vision community, provides a suite of novel approaches that can learn good data representations and generate highly realistic samples. Since 2017, there has been a growth of research into GANs for image augmentation or synthesis in agriculture for improved model performance. This paper presents an overview of the evolution of GAN architectures followed by a systematic review of their application to agriculture (https://github.com/Derekabc/GANs-Agriculture), involving various vision tasks for plant health, weeds, fruits, aquaculture, animal farming, plant phenotyping as well as postharvest inspection of fruits. Challenges and opportunities of GANs are discussed for future research.

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Fruit quality and defect image classification with conditional GAN data augmentation

Cited by 91 publications

References 45 publications

A Survey of Defect Detection Applications Based on Generative Adversarial Networks

A Survey of Defect Detection Applications Based on Generative Adversarial Networks

Generative Adversarial Networks for Image Super-Resolution: A Survey

Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review

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