Crop Yield Prediction in India using Machine Learning
Kousalya A
, Mayilvelan P , Arjun raj A S , Archana N , Sanjana Lakshmi P
Crop Yield Prediction - Food Security - Agricultural Planning - XGBoost - Regression Models - Machine Learning - Ensemble Learning - Label Encoding -Feature Scaling-Exploratory Data Analysis (EDA) -Indian Agriculture - Predictive Model - Outlier Robustness –Forecasting
Crop yield prediction is essential in ensuring food security, guiding agricultural planning, and supporting stakeholders. This study leverages various regression models, with a focus on the eXtreme Gradient Boosting (XGBoost) model, to predict crop yields based on features such as crop type, season, state, area, pesticides, fertilizers and production. Using an extensive dataset from Indian agriculture records, data pre-processing included label encoding, feature scaling, and exploratory data analysis (EDA). Among the evaluated models, XGBoost outperformed others with high accuracy and robustness against outliers. The results demonstrate the effectiveness of ensemble-based machine learning models in agricultural forecasting.
"Crop Yield Prediction in India using Machine Learning", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.12, Issue 4, page no.a980-a987, April-2025, Available :https://tijer.org/TIJER/papers/TIJER2504139.pdf
Volume 12
Issue 4,
April-2025
Pages : a980-a987
Paper Reg. ID: TIJER_157463
Published Paper Id: TIJER2504139
Downloads: 000328
Research Area: Science and Technology
Country: Coimbatore, Tamilnadu, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
