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Rice Crop Disease Prediction Using Machine Learning Technique

Rice Crop Disease Prediction Using Machine Learning Technique

Bharati Patel, Aakanksha Sharaff
Copyright: © 2021 |Volume: 12 |Issue: 4 |Pages: 15
ISSN: 1947-3192|EISSN: 1947-3206|EISBN13: 9781799861614|DOI: 10.4018/IJAEIS.20211001.oa5
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MLA

Patel, Bharati, and Aakanksha Sharaff. "Rice Crop Disease Prediction Using Machine Learning Technique." IJAEIS vol.12, no.4 2021: pp.1-15. http://doi.org/10.4018/IJAEIS.20211001.oa5

APA

Patel, B. & Sharaff, A. (2021). Rice Crop Disease Prediction Using Machine Learning Technique. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 12(4), 1-15. http://doi.org/10.4018/IJAEIS.20211001.oa5

Chicago

Patel, Bharati, and Aakanksha Sharaff. "Rice Crop Disease Prediction Using Machine Learning Technique," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 12, no.4: 1-15. http://doi.org/10.4018/IJAEIS.20211001.oa5

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Abstract

Crop yields are affected at large scale due to spread of unchecked diseases. The spread of these diseases is similar to the spreading of cancer in human body. But, unlike cancer these diseases can be identified at early stages through plant phenotyping traits analysis. In order to effectively identify these diseases, effective segmentation, feature extraction, feature selection and classification processes must be followed. Selection of the best combination for the given methods is very complex due to the presence of a large number of the aforementioned methods. Thereby disease prediction models are generally found to be ineffective. This paper proposes a highly effective machine learning-based formulation approach to select a proper classification process which improves the overall accuracy of crop disease detection with different dimensionality of plant dataset and included maximum features also. Hence, the proposed adaptive learning algorithm gives 99.2% accuracy compared to other techniques like Back-propagation Neural Network (BPNN), Convolutional Neural Network (CNN), and SVM.