Lightweight Federated Learning for Rice Leaf Disease Classification Using Non Independent and Identically Distributed Images
Rice (<i<Oryza sativa</i< L.) is a vital food source all over the world, contributing 15% of the protein and 21% of the energy intake per person in Asia, where most rice is produced and consumed. However, bacterial, fungal, and other microbial diseases that have a negative effect on the...
Ausführliche Beschreibung
Autor*in: |
Meenakshi Aggarwal [verfasserIn] Vikas Khullar [verfasserIn] Nitin Goyal [verfasserIn] Abdullah Alammari [verfasserIn] Marwan Ali Albahar [verfasserIn] Aman Singh [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
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2023 |
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Übergeordnetes Werk: |
In: Sustainability - MDPI AG, 2009, 15(2023), 16, p 12149 |
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Übergeordnetes Werk: |
volume:15 ; year:2023 ; number:16, p 12149 |
Links: |
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DOI / URN: |
10.3390/su151612149 |
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Katalog-ID: |
DOAJ093551266 |
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520 | |a Rice (<i<Oryza sativa</i< L.) is a vital food source all over the world, contributing 15% of the protein and 21% of the energy intake per person in Asia, where most rice is produced and consumed. However, bacterial, fungal, and other microbial diseases that have a negative effect on the health of plants and crop yield are a major problem for rice farmers. It is challenging to diagnose these diseases manually, especially in areas with a shortage of crop protection experts. Automating disease identification and providing readily available decision-support tools are essential for enabling effective rice leaf protection measures and minimising rice crop losses. Although there are numerous classification systems for the diagnosis of rice leaf disease, no reliable, secure method has been identified that meets these needs. This paper proposes a lightweight federated deep learning architecture while maintaining data privacy constraints for rice leaf disease classification. The distributed client–server design of this framework protects the data privacy of all clients, and by using independent and identically distributed (IID) and non-IID data, the validity of the federated deep learning models was examined. To validate the framework’s efficacy, the researchers conducted experiments in a variety of settings, including conventional learning, federated learning via a single client, as well as federated learning via multiple clients. The study began by extracting features from various pre-trained models, ultimately selecting EfficientNetB3 with an impressive 99% accuracy as the baseline model. Subsequently, experimental results were conducted using the federated learning (FL) approach with both IID and non-IID datasets. The FL approach, along with a dense neural network trained and evaluated on an IID dataset, achieved outstanding training and evaluated accuracies of 99% with minimal losses of 0.006 and 0.03, respectively. Similarly, on a non-IID dataset, the FL approach maintained a high training accuracy of 99% with a loss of 0.04 and an evaluation accuracy of 95% with a loss of 0.08. These results indicate that the FL approach performs nearly as well as the base model, EfficientNetB3, highlighting its effectiveness in handling both IID and non-IID data. It was found that federated deep learning models with multiple clients outperformed conventional pre-trained models. The unique characteristics of the proposed framework, such as its data privacy for edge devices with limited resources, set it apart from the existing classification schemes for rice leaf diseases. The framework is the best alternative solution for the early classification of rice leaf disease because of these additional features. | ||
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10.3390/su151612149 doi (DE-627)DOAJ093551266 (DE-599)DOAJa6be85622525423390cb9dad26a73240 DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Meenakshi Aggarwal verfasserin aut Lightweight Federated Learning for Rice Leaf Disease Classification Using Non Independent and Identically Distributed Images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rice (<i<Oryza sativa</i< L.) is a vital food source all over the world, contributing 15% of the protein and 21% of the energy intake per person in Asia, where most rice is produced and consumed. However, bacterial, fungal, and other microbial diseases that have a negative effect on the health of plants and crop yield are a major problem for rice farmers. It is challenging to diagnose these diseases manually, especially in areas with a shortage of crop protection experts. Automating disease identification and providing readily available decision-support tools are essential for enabling effective rice leaf protection measures and minimising rice crop losses. Although there are numerous classification systems for the diagnosis of rice leaf disease, no reliable, secure method has been identified that meets these needs. This paper proposes a lightweight federated deep learning architecture while maintaining data privacy constraints for rice leaf disease classification. The distributed client–server design of this framework protects the data privacy of all clients, and by using independent and identically distributed (IID) and non-IID data, the validity of the federated deep learning models was examined. To validate the framework’s efficacy, the researchers conducted experiments in a variety of settings, including conventional learning, federated learning via a single client, as well as federated learning via multiple clients. The study began by extracting features from various pre-trained models, ultimately selecting EfficientNetB3 with an impressive 99% accuracy as the baseline model. Subsequently, experimental results were conducted using the federated learning (FL) approach with both IID and non-IID datasets. The FL approach, along with a dense neural network trained and evaluated on an IID dataset, achieved outstanding training and evaluated accuracies of 99% with minimal losses of 0.006 and 0.03, respectively. Similarly, on a non-IID dataset, the FL approach maintained a high training accuracy of 99% with a loss of 0.04 and an evaluation accuracy of 95% with a loss of 0.08. These results indicate that the FL approach performs nearly as well as the base model, EfficientNetB3, highlighting its effectiveness in handling both IID and non-IID data. It was found that federated deep learning models with multiple clients outperformed conventional pre-trained models. The unique characteristics of the proposed framework, such as its data privacy for edge devices with limited resources, set it apart from the existing classification schemes for rice leaf diseases. The framework is the best alternative solution for the early classification of rice leaf disease because of these additional features. <i<Oryza sativa</i< L. rice leaf disease federated learning deep learning IIDs non-IIDs Environmental effects of industries and plants Renewable energy sources Environmental sciences Vikas Khullar verfasserin aut Nitin Goyal verfasserin aut Abdullah Alammari verfasserin aut Marwan Ali Albahar verfasserin aut Aman Singh verfasserin aut In Sustainability MDPI AG, 2009 15(2023), 16, p 12149 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:15 year:2023 number:16, p 12149 https://doi.org/10.3390/su151612149 kostenfrei https://doaj.org/article/a6be85622525423390cb9dad26a73240 kostenfrei https://www.mdpi.com/2071-1050/15/16/12149 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 16, p 12149 |
spelling |
10.3390/su151612149 doi (DE-627)DOAJ093551266 (DE-599)DOAJa6be85622525423390cb9dad26a73240 DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Meenakshi Aggarwal verfasserin aut Lightweight Federated Learning for Rice Leaf Disease Classification Using Non Independent and Identically Distributed Images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rice (<i<Oryza sativa</i< L.) is a vital food source all over the world, contributing 15% of the protein and 21% of the energy intake per person in Asia, where most rice is produced and consumed. However, bacterial, fungal, and other microbial diseases that have a negative effect on the health of plants and crop yield are a major problem for rice farmers. It is challenging to diagnose these diseases manually, especially in areas with a shortage of crop protection experts. Automating disease identification and providing readily available decision-support tools are essential for enabling effective rice leaf protection measures and minimising rice crop losses. Although there are numerous classification systems for the diagnosis of rice leaf disease, no reliable, secure method has been identified that meets these needs. This paper proposes a lightweight federated deep learning architecture while maintaining data privacy constraints for rice leaf disease classification. The distributed client–server design of this framework protects the data privacy of all clients, and by using independent and identically distributed (IID) and non-IID data, the validity of the federated deep learning models was examined. To validate the framework’s efficacy, the researchers conducted experiments in a variety of settings, including conventional learning, federated learning via a single client, as well as federated learning via multiple clients. The study began by extracting features from various pre-trained models, ultimately selecting EfficientNetB3 with an impressive 99% accuracy as the baseline model. Subsequently, experimental results were conducted using the federated learning (FL) approach with both IID and non-IID datasets. The FL approach, along with a dense neural network trained and evaluated on an IID dataset, achieved outstanding training and evaluated accuracies of 99% with minimal losses of 0.006 and 0.03, respectively. Similarly, on a non-IID dataset, the FL approach maintained a high training accuracy of 99% with a loss of 0.04 and an evaluation accuracy of 95% with a loss of 0.08. These results indicate that the FL approach performs nearly as well as the base model, EfficientNetB3, highlighting its effectiveness in handling both IID and non-IID data. It was found that federated deep learning models with multiple clients outperformed conventional pre-trained models. The unique characteristics of the proposed framework, such as its data privacy for edge devices with limited resources, set it apart from the existing classification schemes for rice leaf diseases. The framework is the best alternative solution for the early classification of rice leaf disease because of these additional features. <i<Oryza sativa</i< L. rice leaf disease federated learning deep learning IIDs non-IIDs Environmental effects of industries and plants Renewable energy sources Environmental sciences Vikas Khullar verfasserin aut Nitin Goyal verfasserin aut Abdullah Alammari verfasserin aut Marwan Ali Albahar verfasserin aut Aman Singh verfasserin aut In Sustainability MDPI AG, 2009 15(2023), 16, p 12149 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:15 year:2023 number:16, p 12149 https://doi.org/10.3390/su151612149 kostenfrei https://doaj.org/article/a6be85622525423390cb9dad26a73240 kostenfrei https://www.mdpi.com/2071-1050/15/16/12149 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 16, p 12149 |
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10.3390/su151612149 doi (DE-627)DOAJ093551266 (DE-599)DOAJa6be85622525423390cb9dad26a73240 DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Meenakshi Aggarwal verfasserin aut Lightweight Federated Learning for Rice Leaf Disease Classification Using Non Independent and Identically Distributed Images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rice (<i<Oryza sativa</i< L.) is a vital food source all over the world, contributing 15% of the protein and 21% of the energy intake per person in Asia, where most rice is produced and consumed. However, bacterial, fungal, and other microbial diseases that have a negative effect on the health of plants and crop yield are a major problem for rice farmers. It is challenging to diagnose these diseases manually, especially in areas with a shortage of crop protection experts. Automating disease identification and providing readily available decision-support tools are essential for enabling effective rice leaf protection measures and minimising rice crop losses. Although there are numerous classification systems for the diagnosis of rice leaf disease, no reliable, secure method has been identified that meets these needs. This paper proposes a lightweight federated deep learning architecture while maintaining data privacy constraints for rice leaf disease classification. The distributed client–server design of this framework protects the data privacy of all clients, and by using independent and identically distributed (IID) and non-IID data, the validity of the federated deep learning models was examined. To validate the framework’s efficacy, the researchers conducted experiments in a variety of settings, including conventional learning, federated learning via a single client, as well as federated learning via multiple clients. The study began by extracting features from various pre-trained models, ultimately selecting EfficientNetB3 with an impressive 99% accuracy as the baseline model. Subsequently, experimental results were conducted using the federated learning (FL) approach with both IID and non-IID datasets. The FL approach, along with a dense neural network trained and evaluated on an IID dataset, achieved outstanding training and evaluated accuracies of 99% with minimal losses of 0.006 and 0.03, respectively. Similarly, on a non-IID dataset, the FL approach maintained a high training accuracy of 99% with a loss of 0.04 and an evaluation accuracy of 95% with a loss of 0.08. These results indicate that the FL approach performs nearly as well as the base model, EfficientNetB3, highlighting its effectiveness in handling both IID and non-IID data. It was found that federated deep learning models with multiple clients outperformed conventional pre-trained models. The unique characteristics of the proposed framework, such as its data privacy for edge devices with limited resources, set it apart from the existing classification schemes for rice leaf diseases. The framework is the best alternative solution for the early classification of rice leaf disease because of these additional features. <i<Oryza sativa</i< L. rice leaf disease federated learning deep learning IIDs non-IIDs Environmental effects of industries and plants Renewable energy sources Environmental sciences Vikas Khullar verfasserin aut Nitin Goyal verfasserin aut Abdullah Alammari verfasserin aut Marwan Ali Albahar verfasserin aut Aman Singh verfasserin aut In Sustainability MDPI AG, 2009 15(2023), 16, p 12149 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:15 year:2023 number:16, p 12149 https://doi.org/10.3390/su151612149 kostenfrei https://doaj.org/article/a6be85622525423390cb9dad26a73240 kostenfrei https://www.mdpi.com/2071-1050/15/16/12149 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 16, p 12149 |
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10.3390/su151612149 doi (DE-627)DOAJ093551266 (DE-599)DOAJa6be85622525423390cb9dad26a73240 DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Meenakshi Aggarwal verfasserin aut Lightweight Federated Learning for Rice Leaf Disease Classification Using Non Independent and Identically Distributed Images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rice (<i<Oryza sativa</i< L.) is a vital food source all over the world, contributing 15% of the protein and 21% of the energy intake per person in Asia, where most rice is produced and consumed. However, bacterial, fungal, and other microbial diseases that have a negative effect on the health of plants and crop yield are a major problem for rice farmers. It is challenging to diagnose these diseases manually, especially in areas with a shortage of crop protection experts. Automating disease identification and providing readily available decision-support tools are essential for enabling effective rice leaf protection measures and minimising rice crop losses. Although there are numerous classification systems for the diagnosis of rice leaf disease, no reliable, secure method has been identified that meets these needs. This paper proposes a lightweight federated deep learning architecture while maintaining data privacy constraints for rice leaf disease classification. The distributed client–server design of this framework protects the data privacy of all clients, and by using independent and identically distributed (IID) and non-IID data, the validity of the federated deep learning models was examined. To validate the framework’s efficacy, the researchers conducted experiments in a variety of settings, including conventional learning, federated learning via a single client, as well as federated learning via multiple clients. The study began by extracting features from various pre-trained models, ultimately selecting EfficientNetB3 with an impressive 99% accuracy as the baseline model. Subsequently, experimental results were conducted using the federated learning (FL) approach with both IID and non-IID datasets. The FL approach, along with a dense neural network trained and evaluated on an IID dataset, achieved outstanding training and evaluated accuracies of 99% with minimal losses of 0.006 and 0.03, respectively. Similarly, on a non-IID dataset, the FL approach maintained a high training accuracy of 99% with a loss of 0.04 and an evaluation accuracy of 95% with a loss of 0.08. These results indicate that the FL approach performs nearly as well as the base model, EfficientNetB3, highlighting its effectiveness in handling both IID and non-IID data. It was found that federated deep learning models with multiple clients outperformed conventional pre-trained models. The unique characteristics of the proposed framework, such as its data privacy for edge devices with limited resources, set it apart from the existing classification schemes for rice leaf diseases. The framework is the best alternative solution for the early classification of rice leaf disease because of these additional features. <i<Oryza sativa</i< L. rice leaf disease federated learning deep learning IIDs non-IIDs Environmental effects of industries and plants Renewable energy sources Environmental sciences Vikas Khullar verfasserin aut Nitin Goyal verfasserin aut Abdullah Alammari verfasserin aut Marwan Ali Albahar verfasserin aut Aman Singh verfasserin aut In Sustainability MDPI AG, 2009 15(2023), 16, p 12149 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:15 year:2023 number:16, p 12149 https://doi.org/10.3390/su151612149 kostenfrei https://doaj.org/article/a6be85622525423390cb9dad26a73240 kostenfrei https://www.mdpi.com/2071-1050/15/16/12149 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 16, p 12149 |
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Lightweight Federated Learning for Rice Leaf Disease Classification Using Non Independent and Identically Distributed Images |
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Rice (<i<Oryza sativa</i< L.) is a vital food source all over the world, contributing 15% of the protein and 21% of the energy intake per person in Asia, where most rice is produced and consumed. However, bacterial, fungal, and other microbial diseases that have a negative effect on the health of plants and crop yield are a major problem for rice farmers. It is challenging to diagnose these diseases manually, especially in areas with a shortage of crop protection experts. Automating disease identification and providing readily available decision-support tools are essential for enabling effective rice leaf protection measures and minimising rice crop losses. Although there are numerous classification systems for the diagnosis of rice leaf disease, no reliable, secure method has been identified that meets these needs. This paper proposes a lightweight federated deep learning architecture while maintaining data privacy constraints for rice leaf disease classification. The distributed client–server design of this framework protects the data privacy of all clients, and by using independent and identically distributed (IID) and non-IID data, the validity of the federated deep learning models was examined. To validate the framework’s efficacy, the researchers conducted experiments in a variety of settings, including conventional learning, federated learning via a single client, as well as federated learning via multiple clients. The study began by extracting features from various pre-trained models, ultimately selecting EfficientNetB3 with an impressive 99% accuracy as the baseline model. Subsequently, experimental results were conducted using the federated learning (FL) approach with both IID and non-IID datasets. The FL approach, along with a dense neural network trained and evaluated on an IID dataset, achieved outstanding training and evaluated accuracies of 99% with minimal losses of 0.006 and 0.03, respectively. Similarly, on a non-IID dataset, the FL approach maintained a high training accuracy of 99% with a loss of 0.04 and an evaluation accuracy of 95% with a loss of 0.08. These results indicate that the FL approach performs nearly as well as the base model, EfficientNetB3, highlighting its effectiveness in handling both IID and non-IID data. It was found that federated deep learning models with multiple clients outperformed conventional pre-trained models. The unique characteristics of the proposed framework, such as its data privacy for edge devices with limited resources, set it apart from the existing classification schemes for rice leaf diseases. The framework is the best alternative solution for the early classification of rice leaf disease because of these additional features. |
abstractGer |
Rice (<i<Oryza sativa</i< L.) is a vital food source all over the world, contributing 15% of the protein and 21% of the energy intake per person in Asia, where most rice is produced and consumed. However, bacterial, fungal, and other microbial diseases that have a negative effect on the health of plants and crop yield are a major problem for rice farmers. It is challenging to diagnose these diseases manually, especially in areas with a shortage of crop protection experts. Automating disease identification and providing readily available decision-support tools are essential for enabling effective rice leaf protection measures and minimising rice crop losses. Although there are numerous classification systems for the diagnosis of rice leaf disease, no reliable, secure method has been identified that meets these needs. This paper proposes a lightweight federated deep learning architecture while maintaining data privacy constraints for rice leaf disease classification. The distributed client–server design of this framework protects the data privacy of all clients, and by using independent and identically distributed (IID) and non-IID data, the validity of the federated deep learning models was examined. To validate the framework’s efficacy, the researchers conducted experiments in a variety of settings, including conventional learning, federated learning via a single client, as well as federated learning via multiple clients. The study began by extracting features from various pre-trained models, ultimately selecting EfficientNetB3 with an impressive 99% accuracy as the baseline model. Subsequently, experimental results were conducted using the federated learning (FL) approach with both IID and non-IID datasets. The FL approach, along with a dense neural network trained and evaluated on an IID dataset, achieved outstanding training and evaluated accuracies of 99% with minimal losses of 0.006 and 0.03, respectively. Similarly, on a non-IID dataset, the FL approach maintained a high training accuracy of 99% with a loss of 0.04 and an evaluation accuracy of 95% with a loss of 0.08. These results indicate that the FL approach performs nearly as well as the base model, EfficientNetB3, highlighting its effectiveness in handling both IID and non-IID data. It was found that federated deep learning models with multiple clients outperformed conventional pre-trained models. The unique characteristics of the proposed framework, such as its data privacy for edge devices with limited resources, set it apart from the existing classification schemes for rice leaf diseases. The framework is the best alternative solution for the early classification of rice leaf disease because of these additional features. |
abstract_unstemmed |
Rice (<i<Oryza sativa</i< L.) is a vital food source all over the world, contributing 15% of the protein and 21% of the energy intake per person in Asia, where most rice is produced and consumed. However, bacterial, fungal, and other microbial diseases that have a negative effect on the health of plants and crop yield are a major problem for rice farmers. It is challenging to diagnose these diseases manually, especially in areas with a shortage of crop protection experts. Automating disease identification and providing readily available decision-support tools are essential for enabling effective rice leaf protection measures and minimising rice crop losses. Although there are numerous classification systems for the diagnosis of rice leaf disease, no reliable, secure method has been identified that meets these needs. This paper proposes a lightweight federated deep learning architecture while maintaining data privacy constraints for rice leaf disease classification. The distributed client–server design of this framework protects the data privacy of all clients, and by using independent and identically distributed (IID) and non-IID data, the validity of the federated deep learning models was examined. To validate the framework’s efficacy, the researchers conducted experiments in a variety of settings, including conventional learning, federated learning via a single client, as well as federated learning via multiple clients. The study began by extracting features from various pre-trained models, ultimately selecting EfficientNetB3 with an impressive 99% accuracy as the baseline model. Subsequently, experimental results were conducted using the federated learning (FL) approach with both IID and non-IID datasets. The FL approach, along with a dense neural network trained and evaluated on an IID dataset, achieved outstanding training and evaluated accuracies of 99% with minimal losses of 0.006 and 0.03, respectively. Similarly, on a non-IID dataset, the FL approach maintained a high training accuracy of 99% with a loss of 0.04 and an evaluation accuracy of 95% with a loss of 0.08. These results indicate that the FL approach performs nearly as well as the base model, EfficientNetB3, highlighting its effectiveness in handling both IID and non-IID data. It was found that federated deep learning models with multiple clients outperformed conventional pre-trained models. The unique characteristics of the proposed framework, such as its data privacy for edge devices with limited resources, set it apart from the existing classification schemes for rice leaf diseases. The framework is the best alternative solution for the early classification of rice leaf disease because of these additional features. |
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container_issue |
16, p 12149 |
title_short |
Lightweight Federated Learning for Rice Leaf Disease Classification Using Non Independent and Identically Distributed Images |
url |
https://doi.org/10.3390/su151612149 https://doaj.org/article/a6be85622525423390cb9dad26a73240 https://www.mdpi.com/2071-1050/15/16/12149 https://doaj.org/toc/2071-1050 |
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Vikas Khullar Nitin Goyal Abdullah Alammari Marwan Ali Albahar Aman Singh |
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Vikas Khullar Nitin Goyal Abdullah Alammari Marwan Ali Albahar Aman Singh |
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up_date |
2024-07-03T17:59:59.854Z |
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