Author(s): KS Tailor

Abstract: Agriculture is the primary source of livelihood which forms the backbone of our country. Current challenges of water shortages, uncontrolled cost due to demand-supply, and weather uncertainty necessitate farmers to be equipped with smart farming. In particular, low yield of crops due to uncertain climatic changes, poor irrigation facilities, reduction in soil fertility and traditional farming techniques need to be addressed. Machine learning is one such technique employed to predict crop yield in agriculture. Various machine learning techniques such as prediction, classification, regression and clustering are utilized to forecast crop yield. In this paper, a non-parametric model called multivariate adaptive regression spline (MARS) is used to predict the sugarcane production in India. MARS (Friedman, 1991) is a non-parametric model that divides data into various partitions and formulates the relationship between independent and dependent spatial drivers.

Pages: 14-21 | Views: 585 | Downloads: 16

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