Development of a Crop-Based Predictive System for Optimizing Crop Yield for Farmers

• Author(s): Kinga Mary Temidayo ; Olutayo Kehinde Boyinbode ; Olawale Solomon Akintola
• Paper ID: 1706433
• Page: 706-711
• Published Date: 26-11-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 4 October-2024

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

Citations

IRE Journals:
Kinga Mary Temidayo , Olutayo Kehinde Boyinbode , Olawale Solomon Akintola "Development of a Crop-Based Predictive System for Optimizing Crop Yield for Farmers" Iconic Research And Engineering Journals Volume 8 Issue 4 2024 Page 706-711

IEEE:
Kinga Mary Temidayo , Olutayo Kehinde Boyinbode , Olawale Solomon Akintola "Development of a Crop-Based Predictive System for Optimizing Crop Yield for Farmers" Iconic Research And Engineering Journals, 8(4)