This paper is published in Volume-5, Issue-2, 2019
Area
Machine Learning
Author
Keerti Mulgund
Co-authors
Rupa S. G.
Org/Univ
Smt Kamala and Sri Venkappa M. Agadi College of Engineering and Technology, Lakshmeshwar, Karnataka, India
Pub. Date
11 March, 2019
Paper ID
V5I2-1228
Publisher
Keywords
Crop yield estimation, Support Vectors, Least square Support Vector Machine, Data analytics, Agriculture analytics

Citationsacebook

IEEE
Keerti Mulgund, Rupa S. G.. Application of machine learning techniques in estimation of crop yield, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Keerti Mulgund, Rupa S. G. (2019). Application of machine learning techniques in estimation of crop yield. International Journal of Advance Research, Ideas and Innovations in Technology, 5(2) www.IJARIIT.com.

MLA
Keerti Mulgund, Rupa S. G.. "Application of machine learning techniques in estimation of crop yield." International Journal of Advance Research, Ideas and Innovations in Technology 5.2 (2019). www.IJARIIT.com.

Abstract

It is a process in which we can know what happened in the past. And we know that past is the best forecaster of the future. In this research paper we apply evocative analytics in the agriculture production domain for sugarcane crop to find efficient crop yield estimation. In this paper we have three datasets like as Soil dataset, Rainfall dataset, and Yield dataset. And we make a combined dataset and on this combined dataset we apply several supervised techniques to find the actual estimated cost and the accuracy of several techniques. In this paper three supervised techniques are used like as K-Nearest Neighbor, Support Vector Machine, and Least Squared Support Vector Machine. It is a comparative study which tells the accuracy of training proposed model and error rate. The accuracy of training model should be higher and error rate should be minimum. And the proposed model is able to give the actual cost of estimated crop yield and it is label like as LOW, MID, and HIGH.