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An Automated Citrus Disease Detection System Using Hybrid Feature Descriptor

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Congress on Intelligent Systems (CIS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1335))

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Abstract

An automated system for citrus disease detection relies upon using computer-aided tools. By minimizing manual error in disease detection and diagnosis, the computer-aided diagnostic (CAD) tools have acquired a major role in the resources needed to boost and increase the efficiency in production yield. The CAD tools have the properties of automatically speeding up the system’s decision-making capability on the complex disease data. Since these plant diseases can be devastating in terms of economic loss and loss of nutrition, the need for disease detection is growing at an early stage in order to meet human nutritional needs. The use of these computer-aided methods has enabled reducing the loss of production and early disease detection. This article proposes a machine learning-based methodology to automate the process of disease detection thereby reducing the manual efforts. While designing the complete system, the optimization and improvement at every substage are considered in this article so that the complete system possesses high levels of accuracy and thereby can equip farmers to deploy disease detection in a better way.

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Author information

Authors and Affiliations

  1. Chandigarh University, Mohali, India

    Bobbinpreet Kaur, Tripti Sharma & Bhawna Goyal

  2. Ronin Institute, Montclair, NJ, 07043, USA

    Ayush Dogra

Authors
  1. Bobbinpreet Kaur
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  2. Tripti Sharma
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  3. Bhawna Goyal
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  4. Ayush Dogra
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

  2. Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

    Mukesh Saraswat

  3. National Institute of Technology, Jalandhar, Punjab, India

    Anupam Yadav

  4. Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  5. South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

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Kaur, B., Sharma, T., Goyal, B., Dogra, A. (2021). An Automated Citrus Disease Detection System Using Hybrid Feature Descriptor. In: Sharma, H., Saraswat, M., Yadav, A., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. CIS 2020. Advances in Intelligent Systems and Computing, vol 1335. Springer, Singapore. https://doi.org/10.1007/978-981-33-6984-9_51

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