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An enhanced deep learning model for high-speed classification of plant diseases with bioinspired algorithm

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Abstract

Agriculture is one of the most crucial aspects of a nation’s growth. However, the quality and quantity of crop yield are severely affected by various plant diseases. Plant diseases must be identified and prevented at an early stage to improve food quality and production rate. The emerging deep learning network of convolutional neural networks (CNNs) achieved excellent results in plant disease classification. However, the classification potential of the network depends largely on the configuration of hyperparameters. Finding the optimal set of hyperparameters is a tedious, time-consuming, and challenging task. To tackle such an issue, this paper proposes an optimized CNN integrated with a novel modified whale optimization algorithm (MWOA) to achieve plant disease classification. Here, the novel method of optimizing CNN hyperparameters facilitates quicker implementation. To boost the proposed model's efficiency, several data augmentation methods are used such as rotation, scaling, and flipping. The proposed MWOA-CNN model is implemented using a plant village database that includes fourteen plant species, 38 disease classes, and healthy leaves as well. The experiential findings showed that the proposed model outperforms the existing models by attaining the highest classification accuracy of 99.92%, resulting in an effective model for plant disease classification.

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Sethu Institute of Technology, KariapattiVirudhunagar, Tamil Nadu, 626115, India

    A. Ahila

  2. Department of Computer Applications, JP College of Arts and Science, Agarakattu, Tenkasi, Tamil Nadu, 627852, India

    V. Prema

  3. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Vellore, Tamil Nadu, 632014, India

    S. Ayyasamy

  4. Department of Computer Science, JP College of Arts and Science, Agarakattu, Tenkasi, Tamil Nadu, 627852, India

    M. Sivasubramanian

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All authors made an equal contribution in the conception and design of the work, data collection, simulation analysis, drafting the article, and critical revision of the article. All authors have read and approved the final copy of the manuscript.

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Correspondence to A. Ahila.

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Ahila, A., Prema, V., Ayyasamy, S. et al. An enhanced deep learning model for high-speed classification of plant diseases with bioinspired algorithm. J Supercomput 80, 3713–3737 (2024). https://doi.org/10.1007/s11227-023-05622-4

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