Socio-economic factor analysis for sustainable and smart precision agriculture: An ensemble learning approach

Abstract

The socio-economic factor analyses by using the Logit and Probit model have the limitation of representing random taste variation for the unobserved factors and the issues of temporally correlated errors respectively. 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. In this work, Extra-trees classifier machine learning model based socio-economic factors selection has been used and found capable to find out the socio-economic factors that contain relevant information to the target variable agricultural productivity. In addition to this, the voting classifiers ensemble learning approach is used for the prediction of agricultural productivity of respondents (farmers) from their socio-economic profiles. This proposed work has been evaluated by using the test case of analyzing the socio-economic factors of the farmers affecting agricultural productivity in Sambalpur District, in Odisha State, India. In this study, the farmers’ socio-economic data are collected by using structured interviews through questionnaires that are in line with standard Participatory Rural Appraisal. It is found that, the proposed approach of socio-economic factor identification is found efficient for finding the relationships between socio-economic factors and agricultural productivity, and the proposed ensemble learning-based approach is efficient in terms of agricultural productivity prediction.