Impact of Adopting Machine Learning Methods on Indian Agriculture Industry- A Case Study

Abstract

Background/Purpose: Machine learning in today’s world is the driving mechanism for achieving sustainable agriculture. A study of existing literature on applying Machine learning in the agriculture sector and the impact of these methods on the Indian agriculture sector is presented in this paper. Based on the agriculture market and analysis of agriculture trends using Machine Learning and also government initiatives to support Artificial Intelligence-powered agriculture in India, the strengths, weaknesses, opportunities, and challenges are identified and a broader analysis is given in this paper.

Design/Methodology/Approach: The data required for this study on the adoption of Machine learning solutions in the agriculture sector of India are collected from secondary resources including scholarly publications, research articles, web reports, and government websites. The qualitative research method is adopted in conducting the study.

Findings/Result: The study has given insights into various machine learning methods and their applications in the agriculture domain. The knowledge-based agriculture practices could improve overall agriculture productivity. The facts and figures explored during the study of Indian agriculture are analyzed and it is seen that predictive analytics using Machine Learning has great potential in making significant advances in agricultural production.

Research limitations/implications: Machine Learning approaches can be adopted in all the allied sectors of agriculture. The study is limited to improvising farming practices using machine learning methods for better productivity and contributing to the growth of the Indian economy.

Originality/Value: This paper presents a study of the Indian agriculture sector and the scope of incorporating data-driven approaches using machine learning algorithms that help in supporting the growth of the industry.

Paper Type: A case study