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Agricultural sector is the main contributor in the progress of India. This economic growth dependent to quality of the crops which is proportional to the infections. Banana is a crop which is commonly produced and reason for economic growth of the farmers. The hurdle in this is the diseases on these plants, the productivity and quality degradation happen due to many diseases. This problem can be solved by accurate detection of disease. Though on banana crops it is one of the challenging tasks, by understanding image properties and using machine learning a solution can be given to the problem. In this paper color and texture features using Gray Level Cooccurrence Matrix (GLCM) the visual features are extracted, and neighborhood-based approach is used to detect the disease. The system developed shows high accuracy in identification of Cucumber Mosaic Virus infection and Yellow Sigatoka disease. The proposed system is assessed using metrics precision, recall, and accuracy.
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Cite Article:
"Nearest neighborhood approach for identification of cucumber mosaic virus infection and yellow sigatoka disease on banana plants ", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 5, page no.155 - 161, May-2022, Available :http://www.ijrti.org/papers/IJRTI2205026.pdf
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000205078
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator