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Machine Learning Approaches for Automatic Disease Detection from Paddy Crops - A Review

Machine Learning Approaches for Automatic Disease Detection from Paddy Crops - A Review
	© 2022 by IJETT Journal
	Volume-70 Issue-12
	Year of Publication : 2022
	Author : M. Karthick, D. Vijayalakshmi, Malaya Kumar Nath and M. Mathumathi
	DOI : 10.14445/22315381/IJETT-V70I12P237

How to Cite?

M. Karthick, D. Vijayalakshmi, Malaya Kumar Nath and M. Mathumathi, "Machine Learning Approaches for Automatic Disease Detection from Paddy Crops - A Review," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 392-405, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I12P237

Abstract
Crop disease diagnosis is a significant area of concern which needs to be addressed for agricultural development and the flourishing nation's economy. Diagnosing plant disease with conventional methods is a long time-consuming task that necessitates a great deal of effort and knowledge. The prerequisite demand for developing nations is the automatic detection and categorization of crop diseases. Automatic detection of crop disease conditions aids in preventing damage at an early stage, resulting in increased yield efficiency. Advanced computer vision, digital image processing, deep learning and machine learning techniques deliver accurate results and information. The exact, accurate, and real-time data on crop health, disease categorization, and infection location pave the way for determining the best disease management strategy. This review has conducted an in-depth analysis to assess the feasibility of employing cutting-edge technologies of deep learning and machine learning models to identify rice plant diseases. Primarily, various potential infections and diseases, along with their causes and symptoms on the paddy crop, are investigated. The review paper elaborately focuses on a detailed examination of the various actions required for paddy crop disease diagnosis and classification using artificial intelligence-based machine learning and deep learning techniques. A number of online databases for paddy disease prediction have also been offered. Various obstacles and potential research directions in the use of machine learning and deep learning for crop disease detection are further discussed.

Keywords
Automatic disease detection, Deep learning and machine learning techniques, Rice leaf diseases, Paddy crop diseases.

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