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 Journal of Statistics and Management Systems cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510

Monthly Journal: Publishes peer-reviewed aticles on theoretical and applied statistics and management systems, expoloring industrial statistics, actuarial and decision sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Open Access Research Article

Enhancing agriculture productivity with machine learning in crop yield optimization

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pp. 213–223Vol. 27Issue 2March 2024DOI: 10.47974/JSMS-1248XML
Published Online:
30 Mar 2024
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1248
Pages:
213–223

Abstract

The majority of the GDP of an agriculture-based economy comes from farming. This initiative was inspired by the rising suicide rates among farmers, which may be related to poor crop yields. The field of agriculture is now seriously threatened by changes in the climate and other environmental factors. For this problem to be solved effectively and practically, machine learning is a crucial strategy. Estimating agricultural output based on historical information such as Ph, humidity temperature, rainfall, N, P, K. We used Machine Learning method to achieve this. We constructed and compared a number of various machine learning algorithms and ultimately settled on the Random Forest Algorithm, which provided an accuracy of 97.87%. 

Keywords

Subject Classifications

68M11

References

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