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Development of a Crop-Based Predictive System for Optimizing Crop Yield for Farmers
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
Abstract
Crop yield prediction is essential for helping farmers make informed decisions, optimize resource allocation, and enhance productivity. This study presents a machine learning-based predictive system that leverages an ensemble approach, combining Bagging Regressor, XGBoost, and Random Forest models to forecast crop yields. Utilizing a comprehensive dataset that includes vital crop parameters such as crop type, area, temperature, rainfall, and soil type, the system provides valuable insights into the factors influencing predicted yields. By identifying key determinants, the system enables farmers to optimize crop management strategies, leading to improved productivity, reduced uncertainty, and enhanced sustainability. The proposed system empowers farmers with data-driven decision-making tools, contributing to food security and promoting sustainable agricultural practices.
Keywords
Crop yield Prediction, Ensemble Algorithm, Farmers, Machine Learning, Productivity
How to cite this paper
@article{1706433,
author = {Kinga Mary Temidayo, Olutayo Kehinde Boyinbode, Olawale Solomon Akintola},
title = {Development of a Crop-Based Predictive System for Optimizing Crop Yield for Farmers},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {4},
pages = {706-711},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1706433.pdf},
abstract = {Crop yield prediction is essential for helping farmers make informed decisions, optimize resource allocation, and enhance productivity. This study presents a machine learning-based predictive system that leverages an ensemble approach, combining Bagging Regressor, XGBoost, and Random Forest models to forecast crop yields. Utilizing a comprehensive dataset that includes vital crop parameters such as crop type, area, temperature, rainfall, and soil type, the system provides valuable insights into the factors influencing predicted yields. By identifying key determinants, the system enables farmers to optimize crop management strategies, leading to improved productivity, reduced uncertainty, and enhanced sustainability. The proposed system empowers farmers with data-driven decision-making tools, contributing to food security and promoting sustainable agricultural practices.},
keywords = {Crop yield Prediction, Ensemble Algorithm, Farmers, Machine Learning, Productivity},
month = {October},
}