Title: Enhancing crop yield prediction through machine learning regression analysis

Authors: Seema Sharma; Anupriya Jain; Sachin Sharma; Pawan Whig

Addresses: Faculty of Computer Application, MRIIRS, Faridabad, India ' Faculty of Computer Application, MRIIRS, Faridabad, India ' Faculty of Computer Application, MRIIRS, Faridabad, India ' Vivekananda Institute of Professional Studies-TC, New Delhi, India

Abstract: The economic prosperity of any nation hinges significantly on its agricultural output, a cornerstone of sustained growth. The integration of advanced technology plays a pivotal role in enhancing agricultural productivity. Farmers today are leveraging breakthroughs in data mining, the internet of things (IoT), artificial intelligence (AI), and machine learning to optimise their practices. This paper is dedicated to the exploration of this transformative field, offering insights into its multifaceted applications. It delves into the assessment of diverse parameters to facilitate the cultivation of specific crops in a given region. Moreover, it collaborates closely with farmers, tailoring these parameters to maximise crop yields and diversify agricultural produce. The study also incorporates the deployment of AI algorithms, such as logistic regression and multiple regressions, to bolster decision-making processes in agriculture.

Keywords: agriculture; artificial intelligence; logistic regression; machine learning; data mining.

DOI: 10.1504/IJSAMI.2025.143099

International Journal of Sustainable Agricultural Management and Informatics, 2025 Vol.11 No.1, pp.29 - 47

Received: 27 Sep 2023
Accepted: 13 Nov 2023
Published online: 03 Dec 2024 *

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