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Tomato Plant Disease Classification Using Deep Learning Architectures: A Review

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Proceedings of Second International Conference on Advances in Computer Engineering and Communication Systems

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Tomato is a major commercial crop that gets infected with more types of diseases. Plant diseases are easily identified by their leaves. To improve the crop yield, automatic identification, and classification of plant leaf diseases and their severity estimation is one of the research area. We performed a literature review to extract and synthesize the algorithms and features that have been used in tomato disease classification. We have selected around 50 publications for review, analyzed the methods used, and provided suggestions for further research. The traditional machine learning approach fails to extract and classify multi-class tomato diseases as there is more variance in the diseased image data. To overcome this issue, more recent research work is focused on using convolutional neural network architectures. In this paper, we have summarized major machine learning and deep learning methods that have been proposed in identifying diseases.

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Acknowledgements

This review process was fully conducted at Acharya Institute of Technology, Bengaluru. Author 1 is currently working in Presidency University, Author 3 is working in SBIC, A*Star, and Author 4 is working in Sai Vidya Institute of Technology, and during the submission of this menu script, no research activity was conducted at Presidency University, SBIC, A*Star, and Sai Vidya Institute of Technology.

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Authors and Affiliations

  1. Presidency University Bangalore, Bengaluru, India

    U. Shruthi

  2. Acharya Institute of Technology, Bengaluru, India

    V. Nagaveni

  3. Singapore Bioimaging Consortium A*Star, Biopolis Way, Singapore

    C. S. Arvind

  4. Sai Vidya Institute of Technology, Bengaluru, India

    G. L. Sunil

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  1. U. Shruthi
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  2. V. Nagaveni
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  3. C. S. Arvind
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  4. G. L. Sunil
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India

    A. Brahmananda Reddy

  2. Department of Computer Science and Engineering, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India

    B.V. Kiranmayee

  3. Centre for Business Data Analytics, Copenhagen Business School, Frederiksberg, Denmark

    Raghava Rao Mukkamala

  4. Department of Computer Science and Engineering, CMR Technical Campus, Hyderabad, Telangana, India

    K. Srujan Raju

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Shruthi, U., Nagaveni, V., Arvind, C.S., Sunil, G.L. (2022). Tomato Plant Disease Classification Using Deep Learning Architectures: A Review. In: Reddy, A.B., Kiranmayee, B., Mukkamala, R.R., Srujan Raju, K. (eds) Proceedings of Second International Conference on Advances in Computer Engineering and Communication Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-7389-4_15

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