IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

An Effective Multiple Linear Regression-Based Forecasting Model for Demand-Based Constructive Farming

An Effective Multiple Linear Regression-Based Forecasting Model for Demand-Based Constructive Farming
View Sample PDF
Author(s): Balaji Prabhu B.V. (B.M.S College of Engineering, Bengaluru, VTU, Belgaum, India)and M. Dakshayini (B.M.S College of Engineering, Bengaluru, VTU, Belgaum, India)
Copyright: 2020
Volume: 15
Issue: 2
Pages: 18
Source title: International Journal of Web-Based Learning and Teaching Technologies (IJWLTT)
Editor(s)-in-Chief: Mahesh S. Raisinghani (Texas Woman's University, USA)
DOI: 10.4018/IJWLTT.2020040101

Purchase

View An Effective Multiple Linear Regression-Based Forecasting Model for Demand-Based Constructive Farming on the publisher's website for pricing and purchasing information.

Abstract

Demand planning plays a very strategic role in improving the performance of every business, as the planning for a whole lot of other activities depends on the accuracy and validity of this exercise. The field of agriculture is not an exception; demand forecasting plays an important role in this area also, where a farmer can plan for the crop production according to the demand in future. Hence, a system which could forecasts the demand for day-to-day food harvests and assists the farmers in planning the crop production accordingly may lead to beneficial farming business. This paper would experiment by forecasting the demand using multiple linear regression (EMLR-DF) for different food commodities and implements the model to assists the farmers in demand based constructive farming. Implementation results have proved the effectiveness of the proposed system in educating the farmers in producing the yields mapping to the demand. Implementation and comparison results have proved the proposed EMLR-DF is more effective and accurate.

Related Content

Bingbing Yan, Chixiang Ma, Mingfei Wang, Ana Isabel Molina. © 2024. 20 pages.
Zhao Wang. © 2024. 15 pages.
Jingyuan Chen, Zongjian Fu, Hongfeng Liu, Jinku Wang. © 2024. 14 pages.
Hongyu Xie, He Xiao, Yu Hao. © 2024. 14 pages.
Dan Shen, Wenjia Zhao. © 2024. 18 pages.
Ying Liu. © 2024. 16 pages.
Juanjuan Niu. © 2024. 17 pages.
Body Bottom