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A Comprehensive Review of Machine Learning-Based Approaches to Detect Crop Diseases

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Proceedings of International Conference on Paradigms of Communication, Computing and Data Analytics (PCCDA 2023)

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

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Abstract

Incessant agrarian distress and scarcity of food grains in times of Covid-19 have underlined the increased and emergent need for the deployment of technology to safeguard the continuous production and supply of agro-based foods. In India, the rampant availability of spurious insect and pest control products and abysmally low state budgeting in agro-informatics research has led to multiple crop failures and farmers’ distress due to large-scale and repeated destruction of major cash crops like cotton, wheat, rice, etc. Artificial intelligence (AI) techniques, in general, and machine learning, in particular, may prove a boon in the field of crop disease detection. This article has presented an exhaustive review of various machine learning-based strategies proposed for the identification of plant leaf diseases. Various image segmentation approaches have been discussed which can be used to automatically detect and classify plant diseases using the imagery of plant leaves. Literature covering a plethora of plants namely grape, beans, green gram, pine, pepper, citrus fruits, peach, tomato leaves, mango, banana, apple, paddy, and areca nut has been reviewed. Further, AI-based classification techniques that have been covered in the review include artificial neural networks, Bayes classifiers, fuzzy logic, and hybrid algorithms. Present communication concludes by agreeing that these methods could be used for the early-stage detection or preliminary diagnosis of plant diseases.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Chaudhary Devi Lal University, Sirsa, Haryana, India

    Rajesh Kumar & Vikram Singh

Authors
  1. Rajesh Kumar
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  2. Vikram Singh
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Correspondence to Rajesh Kumar .

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Editors and Affiliations

  1. Department of Mathematics, Dr. B. R. Ambedkar National Institute Technology, Jalandhar, India

    Anupam Yadav

  2. Department of Electronics and Communication Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, India

    Satyasai Jagannath Nanda

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Meng-Hiot Lim

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Kumar, R., Singh, V. (2023). A Comprehensive Review of Machine Learning-Based Approaches to Detect Crop Diseases. In: Yadav, A., Nanda, S.J., Lim, MH. (eds) Proceedings of International Conference on Paradigms of Communication, Computing and Data Analytics. PCCDA 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-4626-6_17

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