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Plant Leaf Disease Detection and Classification: A Survey

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Advances in IoT and Security with Computational Intelligence (ICAISA 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 756))

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Abstract

Yields are impacted by climate and temperature, making them susceptible to pathogen infection during growth. Progressive disease detection and prevention in crops are compulsory to avoid disease-induced damage during growth, harvesting and post-harvesting, enhance productivity, and ensure yield sustainability. In the earlier decade, the authors contributed several research articles to detect disease locations and identify complex disease patterns using leaf images. The leaf is the most prominent organ that shows the most distinct features that plant pathologists can identify through visual inspection. This article analyzes the principal aspects that affect the design and effectiveness of disease detection and classification framework using current technologies. An in-depth analysis of the various findings, highlighting advantages and shortcomings, has been discussed, leading to more realistic conclusions about the subject. The assessment is centralized on providing a thorough study and factors for evolving AI-based techniques to support plant disease detection and provide disease oversight support to agriculturalists.

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Author information

Authors and Affiliations

  1. National Institute of Technology, Kurukshetra, India

    Rajiv Bansal & Rajesh Kumar Aggarwal

  2. JMIT Radaur, Radaur, India

    Rajiv Bansal

  3. M.M. Institute of Computer Technology & Business Management, Maharishi Markandeshwar Deemed to be University, Mullana, Ambala, Haryana, India

    Neha Goyal

Authors
  1. Rajiv Bansal
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  2. Rajesh Kumar Aggarwal
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  3. Neha Goyal
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Corresponding author

Correspondence to Rajiv Bansal .

Editor information

Editors and Affiliations

  1. Department of Electronics, Deen Dayal Upadhyaya College, University of Delhi, New Delhi, India

    Anurag Mishra

  2. Department of Computer Science and Engineering, MNNIT Allahabad, Prayagraj, India

    Deepak Gupta

  3. Faculty of Science and Technology, University of Canberra, Bruce, ACT, Australia

    Girija Chetty

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Bansal, R., Aggarwal, R.K., Goyal, N. (2023). Plant Leaf Disease Detection and Classification: A Survey. In: Mishra, A., Gupta, D., Chetty, G. (eds) Advances in IoT and Security with Computational Intelligence. ICAISA 2023. Lecture Notes in Networks and Systems, vol 756. Springer, Singapore. https://doi.org/10.1007/978-981-99-5088-1_22

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