Machine Learning-Based Approaches for Plant Leaf Disease Identification : A Survey

Authors

  • Nandha Kumar G  Research Scholar, Department of Computer Science, Sri Ramakrishna College of Arts and Science, Coimbatore, India
  • Dr. V. Vijayakumar  Professor, Computer Science and Controller of Examinations, Sri Ramakrishna College of Arts and Science- Coimbatore, India

Keywords:

Plant Leaf Diseases, Machine Learning, Agriculture, Classification, Crop Disease.

Abstract

Plant leaf diseases can significantly impact agricultural yields and food security. Machine learning-based approaches have emerged as a promising solution for the rapid and accurate identification of plant leaf diseases, aiding farmers in timely disease management and crop protection. This survey provides a comprehensive overview of the state-of-the-art in machine learning techniques for plant leaf disease identification. We explore various aspects of this domain, including the types of machine learning algorithms commonly employed, the diverse datasets used for training and evaluation, and the challenges associated with real-world deployment. Additionally, we discuss the potential impact of machine learning in agriculture and propose future research directions. By synthesizing existing knowledge and highlighting key trends, this survey serves as a valuable resource for researchers, practitioners, and stakeholders interested in leveraging machine learning to combat plant leaf diseases, thereby contributing to sustainable and resilient agricultural practices.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-10-30

Issue

Volume 9, Issue 5, September-October-2023

Section

Research Articles

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How to Cite

[1]
Nandha Kumar G, Dr. V. Vijayakumar, " Machine Learning-Based Approaches for Plant Leaf Disease Identification : A Survey" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 5, pp.118-124, September-October-2023.