IJRTI
International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14

Issue per Year : 12

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Paper Title: Comparative Evaluation of Machine Learning Models for Accurate Crop Yield Forecasting
Authors Name: Uppu nirosha
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IJRTI_204479
Published Paper Id: IJRTI2506008
Published In: Volume 10 Issue 6, June-2025
DOI:
Abstract: Accurate crop yield forecasting is vital for ensuring food security and supporting precision agriculture. This study investigates and compares various machine learning models for crop yield prediction using environmental, climatic, and soil data. We assess multiple algorithms, including linear regression, random forests, and deep learning, on real-world datasets from diverse regions. The results highlight the potential of advanced data-driven techniques to enhance yield predictions and identify the most influential factors. Our findings provide practical insights for agricultural planning and decision-making, laying the groundwork for future precision agriculture initiatives.
Keywords: Crop yield prediction, machine learning, data-driven agriculture, precision agriculture, deep learning
Cite Article: "Comparative Evaluation of Machine Learning Models for Accurate Crop Yield Forecasting", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 6, page no.a46-a53, June-2025, Available :http://www.ijrti.org/papers/IJRTI2506008.pdf
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ISSN: 2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID: IJRTI2506008
Registration ID:204479
Published In: Volume 10 Issue 6, June-2025
DOI (Digital Object Identifier):
Page No: a46-a53
Country: palava city, kalyan, maharastra, India
Research Area: Computer Engineering 
Publisher : IJ Publication
Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2506008
Published Paper PDF: https://www.ijrti.org/papers/IJRTI2506008
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED, Journal Starting Year (ESTD) : 2016

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