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Plant Disease Classification Using Transfer Learning

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Applied Computational Technologies (ICCET 2022)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 303))

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Abstract

Plant diseases are a considerable danger in the agriculture industry. And it is somewhat impractical to monitor the different pattern variations in plant diseases manually. It involves complex stages of proficiency in various plant diseases under high processing time. Traditional approaches such as digital image processing have already been utilized for plant diseases detection. However, their performance is firm under standard conditions and may not work well for heterogeneous data. The remarkable progress in artificial intelligence techniques, including machine learning and deep learning, has shown a good impact in many sectors and can also be applied to plant disease detection. Inspired by deep learning models, this work has implemented and assessed a transfer learning-based approach to classifying more than 35 different plant disease classes. The idea of the proposed work in multiclass classification is applied on the public dataset and validated through standard evaluation matrices that obtain the decent quantitative score and outperform standard and related methods in the presented domain.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Vishwavidyalaya Engineering College, Ambikapur, India

    Ashutosh Mishra & Ankit Arora

Authors
  1. Ashutosh Mishra
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  2. Ankit Arora
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Correspondence to Ashutosh Mishra .

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Editors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, Dr. B. A. Technological University, Lonere, Maharashtra, India

    Brijesh Iyer

  2. Department of Digital Education and Policy, Swansea University, Swansea, UK

    Tom Crick

  3. Creative Technologies and Product Design, National Taipei University of Business, Taipei City, Taiwan

    Sheng-Lung Peng

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Mishra, A., Arora, A. (2022). Plant Disease Classification Using Transfer Learning. In: Iyer, B., Crick, T., Peng, SL. (eds) Applied Computational Technologies. ICCET 2022. Smart Innovation, Systems and Technologies, vol 303. Springer, Singapore. https://doi.org/10.1007/978-981-19-2719-5_22

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