This paper is published in Volume-6, Issue-5, 2020
Area
Machine Learning
Author
Tejaswi Pallapothu, Harshita Nangia, Manmeet Singh, Riya Sinha, Prashant Udawant
Org/Univ
Mukesh Patel School of Technology Management and Engineering, Shirpur, Maharashtra, India
Pub. Date
28 October, 2020
Paper ID
V6I5-1401
Publisher
Keywords
Cotton diseases, Image Pre-Processing, Feature Extraction, Image Segmentation, Classification

Citationsacebook

IEEE
Tejaswi Pallapothu, Harshita Nangia, Manmeet Singh, Riya Sinha, Prashant Udawant. Cotton plant disease detection, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Tejaswi Pallapothu, Harshita Nangia, Manmeet Singh, Riya Sinha, Prashant Udawant (2020). Cotton plant disease detection. International Journal of Advance Research, Ideas and Innovations in Technology, 6(5) www.IJARIIT.com.

MLA
Tejaswi Pallapothu, Harshita Nangia, Manmeet Singh, Riya Sinha, Prashant Udawant. "Cotton plant disease detection." International Journal of Advance Research, Ideas and Innovations in Technology 6.5 (2020). www.IJARIIT.com.

Abstract

Agriculture is one of the major sectors of the Indian economy and food security. Agriculture needs to find new ways to improve efficiency, production and yields. Approximately 30-35% of the yield gets affected by pests and diseases. The reason it happens is because these diseases are detected at a late stage which gets difficult to control. Hence, advancement in image processing technology and automated learning plays an important role in plant disease detection especially image processing and machine learning. On the other hand, manually detecting diseases in plants needs a tremendous amount of work, expertise and is very expensive because of the involvement of an expert or a plant pathologist. This paper majorly focuses on the need for a solution for early detection of cotton plant diseases, the diseases of cotton and their characteristics, different challenges farmers face while cultivating cotton and while identifying diseases in them, and the step by step technical approaches being used for the detection of cotton plant diseases. There is a lot scope in the advancement of technologies to improve the production by detecting and providing solutions to the farmers on an urgent basis. Agriculture is one of the major sectors of the Indian economy and food security. Agriculture needs to find new ways to improve efficiency, production and yields. Approximately 30-35% of the yield gets affected by pests and diseases. The reason it happens is because these diseases are detected at a late stage which gets difficult to control. Hence, advancement in image processing technology and automated learning plays an important role in plant disease detection especially image processing and machine learning. On the other hand, manually detecting diseases in plants needs a tremendous amount of work, expertise and is very expensive because of the involvement of an expert or a plant pathologist. This paper majorly focuses on the need for a solution for early detection of cotton plant diseases, the diseases of cotton and their characteristics, different challenges farmers face while cultivating cotton and while identifying diseases in them, and the step by step technical approaches being used for the detection of cotton plant diseases. There is a lot scope in the advancement of technologies to improve the production by detecting and providing solutions to the farmers on an urgent basis.
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