Accurate Error Prediction of Sugarcane Yield Using a Regression Model
|M. Mohanadevi1 , V. Vinodhini2|
Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-7 , Page no. 66-71, Jul-2018
Online published on Jul 31, 2018
Copyright © M. Mohanadevi, V. Vinodhini . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: M. Mohanadevi, V. Vinodhini, “Accurate Error Prediction of Sugarcane Yield Using a Regression Model”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.66-71, 2018.
MLA Style Citation: M. Mohanadevi, V. Vinodhini "Accurate Error Prediction of Sugarcane Yield Using a Regression Model." International Journal of Computer Sciences and Engineering 6.7 (2018): 66-71.
APA Style Citation: M. Mohanadevi, V. Vinodhini, (2018). Accurate Error Prediction of Sugarcane Yield Using a Regression Model. International Journal of Computer Sciences and Engineering, 6(7), 66-71.
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|In this paper an attempt has been made to review on application of data mining techniques in the field of agriculture. India is the largest producer and consumer of sugar in the world and its most efficient crops in converting solar energy into chemical energy. Sugar-cane is an important commercial crop of the world. About 45 million sugarcane farmers, their dependents and a large agricultural force, constituting 7.5 percent of the rural population, are involved in sugar-cane cultivation, harvesting and ancillary activities. Sugar industries development is backbone to economic development of the nation. In India, Sugar industry is the second largest agro-based industry and it contributes significantly to the socio economic development of the nation. The major Sugar-cane crop growing states in India are Uttar Pradesh, Bihar, Assam, Haryana, Gujarat, Maharashtra, Karnataka and Tamil Nadu. This paper presents state of Karnataka datasets to predict less error rate on better productivity and yield using regression metrics.|
|Key-Words / Index Term :|
|Agriculture data, Data mining Techniques, Weka tool,Regression Model|
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