Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets
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 identificatio...
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| Format: | Article |
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MDPI AG
2023-09-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/13/10/2483 |
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| author | Meenakshi Aggarwal Vikas Khullar Nitin Goyal Rama Gautam Fahad Alblehai Magdy Elghatwary Aman Singh |
| author_facet | Meenakshi Aggarwal Vikas Khullar Nitin Goyal Rama Gautam Fahad Alblehai Magdy Elghatwary Aman Singh |
| author_sort | Meenakshi Aggarwal |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T21:31:44Z |
| format | Article |
| id | doaj.art-cef1979ec015458aa3a7495c7b359f30 |
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| language | English |
| last_indexed | 2024-03-10T21:31:44Z |
| publishDate | 2023-09-01 |
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| series | Agronomy |
| spelling | doaj.art-cef1979ec015458aa3a7495c7b359f302023-11-19T15:20:49ZengMDPI AGAgronomy2073-43952023-09-011310248310.3390/agronomy13102483Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo DatasetsMeenakshi Aggarwal0Vikas Khullar1Nitin Goyal2Rama Gautam3Fahad Alblehai4Magdy Elghatwary5Aman Singh6Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, IndiaChitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, IndiaDepartment of Computer Science and Engineering, School of Engineering and Technology, Central University of Haryana, Mahendragarh 123031, Haryana, IndiaDepartment of Electronics and Communication Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra 123031, Haryana, IndiaComputer Science Department, Community College, King Saud University, Riyadh 11437, Saudi ArabiaBiomedical Technology Department, College of Applied Medical Sciences, King Saud University, Riyadh 12372, Saudi ArabiaEngineering Research & Innovation Group, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, SpainPaddy 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.https://www.mdpi.com/2073-4395/13/10/2483federated learningtransfer learningresource utilizationIID and non-IID |
| spellingShingle | Meenakshi Aggarwal Vikas Khullar Nitin Goyal Rama Gautam Fahad Alblehai Magdy Elghatwary Aman Singh Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets Agronomy federated learning transfer learning resource utilization IID and non-IID |
| title | Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets |
| title_full | Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets |
| title_fullStr | Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets |
| title_full_unstemmed | Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets |
| title_short | Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets |
| title_sort | federated transfer learning for rice leaf disease classification across multiclient cross silo datasets |
| topic | federated learning transfer learning resource utilization IID and non-IID |
| url | https://www.mdpi.com/2073-4395/13/10/2483 |
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