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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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Issue: September 2025
Volume 10 | Issue 9
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 112
Article Submitted : 17566
Article Published : 7616
Total Authors : 20196
Total Reviewer : 740
Total Countries : 135
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| Paper Title: | Predicting Annual Crop Yields in India's States: Leveraging XGBoost Techniques for a Web-Based Machine Learning Model |
| Authors Name: | Banda akshaya bhavika , Gundampadu samaira , K.deepika , Reyyi kusum |
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IJRTI_201429
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| Published Paper Id: | IJRTI2503121 |
| Published In: | Volume 10 Issue 3, March-2025 |
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| Abstract: | This research focuses on improving crop yield predictions through advanced machine learning techniques, particularly XGBoost. By analyzing environmental factors such as rainfall, seasonal variations, and soil conditions from datasets spanning 1997-2018, the study demonstrates the algorithm’s robustness in handling missing data and scalability. The research provides insights into the impact of climatic and biotic factors on crop productivity and emphasizes the utility of ensemble models in large-scale agricultural predictions. XGBoost's ability to process both categorical and numerical variables makes it highly adaptable for varying crops, seasons, and geoclimatic conditions, offering tailored predictions for farmers. This work not only supports India's Zero Hunger Agenda by 2030 but also contributes to sustainable farming practices, helping farmers and policymakers make informed decisions on crop selection and resource management. By integrating modern machine learning techniques like XGBoost, the study addresses challenges from climate change and fluctuating weather patterns, optimizing crop management and boosting agricultural productivity, which is essential for India's food security and economic growth. |
| Keywords: | Machine Learning (ML), Deep Learning, XGBoost Algorithm, Decision Support Systems, Regression Analysis, Ensemble Learning |
| Cite Article: | "Predicting Annual Crop Yields in India's States: Leveraging XGBoost Techniques for a Web-Based Machine Learning Model", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 3, page no.b140-b146, March-2025, Available :http://www.ijrti.org/papers/IJRTI2503121.pdf |
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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 |
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Published Paper ID: IJRTI2503121
Registration ID:201429
Published In: Volume 10 Issue 3, March-2025
DOI (Digital Object Identifier):
Page No: b140-b146 Country: Mancherial, Telangana , India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2503121 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2503121 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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