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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM50964

Agri- wiz An AI Driven Platform for Crop Recommendation and Yield Maximization

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM50964

Volume:

9 |

6 |

Jun 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM50964

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

JRG

Jathar Raj Girish

SPB

Sapkal Pavan Baban

BAK

Bhojani Alimehdi Kausarali

MSCMP(C

Mohite Sourav Chitrasen.HOD: Mr.A.N. Patil (Dept: CSE)

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Abstract

This presentation delves into the evolution and real-world use of a crop recommendation system powered by machine learning. By harnessing historical agricultural data and key environmental variables, the system offers farmers personalized crop choices that promote higher productivity. Drawing on diverse data sources and cutting-edge algorithms, these AI-based solutions have shown impressive gains in performance—boosting efficiency, enhancing precision, and supporting more sustainable practices. Among the notable advancements are a 15% increase in the accuracy of yield predictions, a reduction in water usage by up to 30%, and a 20% decrease in fertilizer application, all without sacrificing output. While the journey toward implementation isn’t without its hurdles—such as concerns around data privacy and the need for upfront investments—the long-term advantages of adopting AI in agriculture are compelling. These include better profitability for farmers, improved ecological balance, and stronger food security. Ultimately, this in-depth analysis sheds light on the transformative power of AI-driven precision agriculture. It reveals how these technologies are redefining traditional farming methods and paving the way to solving global food production challenges, all while lessening the environmental impact of agriculture.

KEYWORDS

Various sources include historical crop data weather records soil analysis reports and satellite imagery.

Cite This Article

Jathar Raj Girish, Sapkal Pavan Baban, Bhojani Alimehdi Kausarali, Mohite Sourav Chitrasen.HOD: Mr.A.N. Patil (Dept: CSE), "Agri- wiz An AI Driven Platform for Crop Recommendation and Yield Maximization," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 6, Jun 2025. https://doi.org/10.55041/IJSREM50964

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Publication Date

6/25/2025

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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