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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

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Background/Purpose: The objective of this literature review is to explore different land use and land cover methods using machine learning techniques and also their applications in change detection. Reviewing various methods adopted in this domain opens up a new path for taking up further research by extending the current approaches. Design/Methodology/Approach: The research findings presented in various scholarly articles are collected from secondary resources including scholarly journal publications. These articles are analyzed, and the interpretations are highlighted in this review paper. Findings/Result: This research provides insight into various techniques used to classify remote sensing imagery. The gaps identified during the analysis with different approaches have helped to get a clear picture when formulating research questions in the remote sensing geographic information systems domain. Research limitations/implications: This study has surveyed various applications of remote sensing in GIS. This study is limited to a review of the various machine-learning approaches used for implementing change detection. The various deep learning architectures for image classification could be further explored. Originality/Value: The articles selected for review in this study are from scholarly research journals and are cited by other authors in their publications. The papers selected for review are relevant to the research work and research proposal presented in this paper. Paper Type: Literature review paper.

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