Extra-tree learning based Socio-economic factor analysis and multi-class adaptive boosting meta-estimator for prediction of agricultural productivity

Bighnaraj Naik

doi:10.17485/ijst/v13i29.839

Abstract

Background/Objectives: In socio-economic factor analysis, the observed data are essential in the random distribution for the adequate representation of the random components associated with various factors and lead to poor prediction in the case of the Logit and Probit model. The objective of this work is to have machine learning based model for socio-economic factors analysis and ensemble learning based model for efficient prediction of agricultural productivity. Methods: In this work, extra-tree classifier machine learning model based socio-economic factors selection has been used and found capable to evaluate the socio-economic factors that contain relevant information to the target variable agricultural productivity. In addition to this, the multi-class adaptive boosting ensemble learning approach is used for the prediction of agricultural productivity of respondents (farmers) from their socio-economic profiles. This proposed research has been evaluated by using the test case of analyzing the socio-economic factors of the farmers affecting agricultural productivity in Sambalpur District, of Odisha State, India.The farmers’ socio-economic data are collected by using structured interviews through questionnaires that are in line with standard Participatory Rural Appraisal. Findings: It is found that the proposed approach of socio-economic factor identification is efficient for computing the relationships between socioeconomic factors and agricultural productivity. Novelty: In this application domain of socio-economic factor analysis, the proposed method employs extra-tree classifier and boosting ensemble learning for socio-economic factor analysis towards agricultural productivity which is found efficient than other existing approaches such as Logit, Probit, Linear Regression, Linear Discriminant Analysis, Naïve Baise, and other counterparts. Keywords: Socio-economic factor analysis; multiclass adaptive boosting; ensemble learning; extra-tree classifier; Probit; Logit

Highlights

Un-doubtfully, agriculture is the most important gift of environmental services including water, forest, pastures, and soil nutrients
The summary of the result of the Logit and Probit model for socio-economic factor analysis are displayed in Tables 2 and 3 respectively
The list of selected socio-economic factors by using Probit, Logit, and proposed Extra-tree learning model are listed in Table 4 in the Appendix

Summary

Introduction

Un-doubtfully, agriculture is the most important gift of environmental services including water, forest, pastures, and soil nutrients. Socio-economic factors of farmers such as Marital Status, Household Size, Total Annual Income, Educational Level, Farm Size, Membership of farmers cooperative society, Years of residence, Available amenities (such as Electricity, Pipe borne water, Tarred roads, etc.), Farming experience, Quantity and Type of fertilizer used, Access to Government Schemes, etc., plays important roles in sustainable agricultural productivity. Some national and international reports [1] indicated that the country needs to produce more major agricultural products such as rice and wheat for the increasing number of population. Some other major factors related to environmental, social, technological, and policy-oriented need to be taken care of at the utmost level. The distribution of research and modernization has the future to unlock enormous benefits in the Indian agricultural sector in which a major part of the country’s population is directly and indirectly associated

Objectives

Results

Conclusion