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Optimization Based Rice Leaf Disease Classification in Federated Learning

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Abstract

Numerous farmers worldwide are impacted by diseases connected to rice leaves that frequently endanger the sustainability of the rice industry. Diseases that affect the leaves of rice plants severely limit their ability to produce rice, and they are typically brought on by bacteria, viruses, or fungi. This paper proposes an innovative classification scheme for rice leaf diseases based on Federated Learning (FL). Here, FL framework comprises two entities, namely nodes and servers. Every node does initial local training using local data. Moreover, produced local model is then updated on server. Model aggregation is done at the server since several nodes update their local models and send them to it. The nodes download the global model that server has generated as a result. The nodes update their training using transferred global model and local model. The following series of actions are taken in the training model. The input image is mainly obtained from a database and pre-processed with a Kalman filter to eliminate noise. Then, numerous operations for data augmentation are applied. In addition, feature extraction is done and generated features are used in LeNet for rice leaf diseases classification. LeNet is trained using the Spotted Hyena Archimedes Optimizer (SHAO). The developed method shows better precision of 91.3%, recall of 92.2%, f-measure of 91.7%, loss function of 3.3%, Mean Square Error (MSE) of 7.3%, and Root MSE of 27.1%.

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Data Availability

In case of benchmark data:  The data taken for this work is available in Rice leaf disease dataset taken from “https://archive.ics.uci.edu/ml/datasets/Rice+Leaf+Diseases”.

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Acknowledgements

I would like to express my very great appreciation to the co-authors of this manuscript for their valuable and constructive suggestions during the planning and development of this research work.

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This research did not receive any specific funding.

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Authors and Affiliations

  1. Chitkara University School of Engineering, Chitkara University, Himachal Pradesh, India

    Ramamani Tripathy

  2. Department of Computer science and engineering, Christ (Deemed to be) University, Bangalore, India

    Jyothi Mandala

  3. Department of Computer Science and Engineering, GITAM (Deemed to be University),Visakhapatnam Campus, Andhra Pradesh, Visakhapatnam, India

    Sasibhushana Rao Pappu

  4. Department of Information Technology, KKR & KSR Institute of Technology and Sciences, Affiliated to JNTUK Kakinada, Guntur, India

    Guru Kesava Dasu Gopisetty

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All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to Sasibhushana Rao Pappu.

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Tripathy, R., Mandala, J., Pappu, S.R. et al. Optimization Based Rice Leaf Disease Classification in Federated Learning. Multimed Tools Appl 83, 72491–72517 (2024). https://doi.org/10.1007/s11042-023-18085-9

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