International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Meteorological Data and Pesticide Usage for Crop Yield Prediction Using Machine Learning

Author(s) Prof. Karthigadevi R, Ms. Afrin Mekhanas A, Ms. Iswarya A
Country India
Abstract This project presents a machine learning–based system for predicting crop yield using meteorological and pesticide usage data. Models such as XGBoost, LSTM, CatBoost, and GNN were implemented, with a hybrid LSTM–XGBoost model achieving the highest accuracy and efficiency. A Streamlit dashboard enables real-time yield visualization and analysis, supporting data-driven, sustainable, and climate-resilient agriculture.
Keywords Crop Yield Prediction, Machine Learning, LSTM, XGBoost, CatBoost, GNN.
Field Engineering
Published In Volume 7, Issue 5, September-October 2025
Published On 2025-10-24
DOI https://doi.org/10.36948/ijfmr.2025.v07i05.58763
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E-ISSN 2582-2160
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