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A detection of tomato plant diseases using deep learning MNDLNN classifier

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Abstract

In the world, tomato is a significant economic crop. However, it is easily affected by various diseases. Misprediction of disease is caused since many prevailing methodologies focused on the tomato plant’s specific portion. Thus, by employing deep learning (DL) multivariate normal DL neural network (MNDLNN) classifier, the study has proposed a framework for tomato plant disease (PD) detection. Firstly, the input images’ colours are transmitted into HSI format. Next, from the images, the green pixels are masked, and healthy and unhealthy regions are isolated. Next by deploying the region of interest (ROI), the fruit and root are detected. Then, by utilizing the rectilinear K-means (KM) clustering (RKMC) algorithm, the unhealthy regions are segmented. Afterwards, by utilizing random motion squirrel search optimization (RMSSO), the essential features are extracted. Finally, MNDLNN effectively detects and classifies the disease types. The results revealed that the proposed framework performed the disease detection process more precisely than other top-notch methodologies.

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Authors and Affiliations

  1. Department of CSE, Amity School of Engineering and Technology, Amity University, Mumbai, Maharashtra, India

    Rina Bora

  2. Department of CSE, Amity School of Engineering and Technology, Amity University, Mumbai, Maharashtra, India

    Deepa Parasar

  3. Amity School of Engineering and Technology, Amity University, Mumbai, Maharashtra, India

    Shrikant Charhate

Authors
  1. Rina Bora
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  2. Deepa Parasar
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  3. Shrikant Charhate
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Rina Bora, Deepa Parasar. The first draft of the manuscript was written by Rina Bora and all authors commented on previous versions of the manuscript.

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Correspondence to Rina Bora.

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Bora, R., Parasar, D. & Charhate, S. A detection of tomato plant diseases using deep learning MNDLNN classifier. SIViP 17, 3255–3263 (2023). https://doi.org/10.1007/s11760-023-02498-y

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  • DOI: https://doi.org/10.1007/s11760-023-02498-y

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