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Today in the world, the crop recommendations and predictions are highly required by the farmers to enhance the production of the crop. The paper will set out to review the different studies conducted on crop recommendations to enhance the crop production in India. The thorough research of multiple literature works can provide an insight into the fact that machine learning (ML) techniques play a crucial role in crop forecasting and advisory. The different ML algorithms that have been very effective in predicting the results are addressed with both pros and cons. This paper discusses the parameters employed in datasets, as well as, standard procedure employed to forecast the crop recommendations. Lastly different ML and crop prediction performance indicators are given. This whole paper provides the discussion of crop recommendation methodology.
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Copyright © 2025, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: Ammanni Bidinamcherla, bammanni@gitam.in
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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