Lightweight Federated Learning for Rice Leaf Disease Classification Using Non Independent and Identically Distributed Images
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Published:2023-08-09
Issue:16
Volume:15
Page:12149
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ISSN:2071-1050
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Container-title:Sustainability
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language:en
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Short-container-title:Sustainability
Author:
Aggarwal Meenakshi1, Khullar Vikas1ORCID, Goyal Nitin2ORCID, Alammari Abdullah3ORCID, Albahar Marwan Ali4ORCID, Singh Aman567ORCID
Affiliation:
1. Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India 2. Department of Computer Science and Engineering, School of Engineering and Technology, Central University of Haryana, Mahendragarh, Haryana 123031, India 3. Faculty of Education, Curriculums and Teaching Department, Umm Al-Qura University, Mecca P.O. Box 715, Saudi Arabia 4. Department of Computer Science, Umm Al-Qura University, Mecca P.O. Box 715, Saudi Arabia 5. Engineering Research & Innovation Group, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain 6. Islamic University Centre for Scientific Research, The Islamic University, Najaf 54001, Iraq 7. Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, Uttarakhand 248007, India
Abstract
Rice (Oryza sativa 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.
Funder
Umm Al-Qura University
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
Reference41 articles.
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