Abstract
This study was conducted to develop a prototype which computes the severity of diseases appears in the rice crop using machine learning and computational intelligence. The symptoms of rice crop diseases imply the seriousness of the disease and suggest choosing the best approach to dealing with the disease. Most of the diseases in rice crop appear as a spot on the leaves. It is also needful to diagnose the disease properly and on-time to avoid the great harm of the rice crop. The treatment of rice crop diseases by applying disproportionate pesticides increases the cost and environmental pollution. So the use of pesticides must be minimized. This can be actualizing by targeting the diseased area, with the appropriate quantity and concentration of pesticide by estimating disease severity. This paper introduces Fuzzy Logic with K-Means segmentation technique to compute the degree of disease severity of leaves in rice crop. The proposed method estimated to give up to about 86.35% of accuracy.
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Sethy, P.K., Negi, B., Barpanda, N.K., Behera, S.K., Rath, A.K. (2018). Measurement of Disease Severity of Rice Crop Using Machine Learning and Computational Intelligence. In: Cognitive Science and Artificial Intelligence. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-10-6698-6_1
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DOI: https://doi.org/10.1007/978-981-10-6698-6_1
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