Lightweight Federated Learning for Rice Leaf Disease Classification Using Non Independent and Identically Distributed Images
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Date
2023-08
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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 identifi cation 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.