An Improved Approach to Detection of Rice Leaf Disease with GAN-Based Data Augmentation Pipeline

Haruna, Yunusa; Qin, Shiyin; Kiki, Mesmin J. Mbyamm

doi:10.3390/app13031346

Cited by 16 publications

(9 citation statements)

References 41 publications

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“…Their analysis concludes that the XceptionNet model achieves the highest accuracy, notably 94.33%, surpassing the performance of other transfer-learned models. Haruna et al [12] developed a specialized model called StyleGAN2-ADA, which stands for Style-Generative Adversarial Network Adaptive Discriminator Augmentation. This model was designed to detect diseases in rice leaves.…”

Section: Related Studymentioning

confidence: 99%

Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets

Aggarwal,

Khullar,

Goyal

et al. 2023

Agronomy

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Paddy leaf diseases encompass a range of ailments affecting rice plants’ leaves, arising from factors like bacteria, fungi, viruses, and environmental stress. Precision agriculture leverages technologies for enhanced crop production, with disease detection being a vital element. Prompt identification of diseases in paddy leaves is critical for curtailing their propagation and reducing crop damage. However, manually diagnosing paddy diseases in regions with vast agricultural areas and limited experts proves immensely difficult. The utilization of machine learning (ML) and deep learning (DL) for diagnosing diseases in agricultural crops appears to be effective and well-suited for widespread application. These ML/DL methods cannot ensure data privacy, as they involve sharing training data with a central server, overlooking competitive and regulatory considerations. As a solution, federated learning (FL) aims to facilitate decentralized training to tackle the identified limitations of centralized training. This paper utilizes the FL approach for the classification of rice-leaf diseases. The manuscript presents an effective approach for rice-leaf disease classification with a federated architecture, ensuring data privacy. We have compiled an unbalanced dataset of rice-leaf disease images, categorized into four diseases with their respective image counts: bacterial blight (1584), brown spot (1440), blast (1600), and tungro (1308). The proposed method, called federated transfer learning (F-TL), maintains privacy for all connected devices using a decentralized client-server setup. Both IID (independent and identically distributed) and non-IID datasets were utilized for testing the F-TL framework after preprocessing. Initially, we conducted an effectiveness analysis of CNN and eight transfer learning models for rice-leaf disease classification. Among them, MobileNetV2 and EfficientNetB3 outperformed the other transfer-learned models. Subsequently, we trained these models using both IID and non-IID datasets in a federated learning environment. The framework’s performance was assessed through diverse scenarios, comparing it with traditional and federated learning models. The evaluation considered metrics like validation accuracy, loss as well as resource utilization such as CPU and RAM. EfficientNetB3 excelled in training, achieving 99% accuracy with 0.1 loss for both IID and non-IID datasets. MobilenetV2 showed slightly lower training accuracy at 98% (IID) and 90% (non-IID) with losses of 0.4 and 0.6, respectively. In evaluation, EfficientNetB3 maintained 99% accuracy with 0.1 loss for both datasets, while MobilenetV2 achieved 90% (IID) and 97% (non-IID) accuracy with losses of 0.6 and 0.2, respectively. Results indicated the F-TL framework’s superiority over traditional distributed deep-learning classifiers, demonstrating its effectiveness in both single and multiclient instances. Notably, the framework’s strengths lie in its cost-effectiveness and data-privacy assurance for resource-constrained edge devices, positioning it as a valuable alternative for rice-leaf disease classification compared to existing tools.

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Section: Related Studymentioning

confidence: 99%

Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets

Aggarwal,

Khullar,

Goyal

et al. 2023

Agronomy

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“…Lamba et al [ 23 ] enhanced the rice disease dataset by GAN and then used CNN for classification, achieving 98.23% accuracy. Haruna et al [ 24 ] proposed balancing the rice leaf disease dataset with StyleGAN and achieved 93% accuracy using the fast-RCNN model. Zhao et al [ 25 ] used DoubleGAN to form an image of unhealthy plant leaves to balance the dataset and improve plant disease recognition accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…This problem can be ameliorated by applying augmented dataset techniques to minority classes. Some works [ 21 , 22 , 23 , 24 , 25 , 26 ] use a GAN to expand the dataset or solve the problem of class-imbalanced datasets. Refs.…”

Section: Introductionmentioning

confidence: 99%

“…[ 21 , 26 ] use CGAN to synthesize images to augment and balance datasets, but experiments in this paper prove that synthetic images of CGAN have a low level of accuracy. The methods [ 22 , 23 , 24 , 25 ] use nonlabel-conditional GANs to augment or balance plant disease datasets, but the disadvantage of these methods is that GANs need to be trained separately for each class. In addition, ref.…”

Section: Introductionmentioning

confidence: 99%

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GACN: Generative Adversarial Classified Network for Balancing Plant Disease Dataset and Plant Disease Recognition

Wang,

Cao

2023

Sensors

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Plant diseases are a critical threat to the agricultural sector. Therefore, accurate plant disease classification is important. In recent years, some researchers have used synthetic images of GAN to enhance plant disease recognition accuracy. In this paper, we propose a generative adversarial classified network (GACN) to further improve plant disease recognition accuracy. The GACN comprises a generator, discriminator, and classifier. The proposed model can not only enhance convolutional neural network performance by generating synthetic images to balance plant disease datasets but the GACN classifier can also be directly applied to plant disease recognition tasks. Experimental results on the PlantVillage and AI Challenger 2018 datasets show that the contribution of the proposed method to improve the discriminability of the convolution neural network is greater than that of the label-conditional methods of CGAN, ACGAN, BAGAN, and MFC-GAN. The accuracy of the trained classifier for plant disease recognition is also better than that of the plant disease recognition models studied on public plant disease datasets. In addition, we conducted several experiments to observe the effects of different numbers and resolutions of synthetic images on the discriminability of convolutional neural network.

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“…However, deep neural networks are prone to overfitting when the number of samples is insufficient. Although some transfer learning [36], and data augmentation [37] methods can mitigate overfitting, they do not solve it.…”

Section: Few-shot Learningmentioning

confidence: 99%

An Environmental Pattern Recognition Method for Traditional Chinese Settlements Using Deep Learning

Kong

Xue

et al. 2023

Applied Sciences

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The recognition of environmental patterns for traditional Chinese settlements (TCSs) is a crucial task for rural planning. Traditionally, this task primarily relies on manual operations, which are inefficient and time consuming. In this paper, we study the use of deep learning techniques to achieve automatic recognition of environmental patterns in TCSs based on environmental features learned from remote sensing images and digital elevation models. Specifically, due to the lack of available datasets, a new TCS dataset was created featuring five representative environmental patterns. We also use several representative CNNs to benchmark the new dataset, finding that overfitting and geographical discrepancies largely contribute to low classification performance. Consequently, we employ a semantic segmentation model to extract the dominant elements of the input data, utilizing a metric-based meta-learning method to enable the few-shot recognition of TCS samples in new areas by comparing their similarities. Extensive experiments on the newly created dataset validate the effectiveness of our proposed method, indicating a significant improvement in the generalization ability and performance of the baselines. In sum, the proposed method can automatically recognize TCS samples in new areas, providing a powerful and reliable tool for environmental pattern research in TCSs.

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An Improved Approach to Detection of Rice Leaf Disease with GAN-Based Data Augmentation Pipeline

Cited by 16 publications

References 41 publications

Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets

Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets

GACN: Generative Adversarial Classified Network for Balancing Plant Disease Dataset and Plant Disease Recognition

An Environmental Pattern Recognition Method for Traditional Chinese Settlements Using Deep Learning

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