AgroYield: Country Wise Crop Yield Prediction Using Machine Learning
- Version
- Download 22
- File Size 573.37 KB
- File Count 1
- Create Date 27 May 2025
- Last Updated 27 May 2025
AgroYield: Country Wise Crop Yield Prediction Using Machine Learning
Authors:
Ranjana Ray1, Saptarshi Mondal1, Anindita Sarkar1, Nisha Pandey1
1Electronics and Communication Engineering, JIS College of Engineering
Abstract - Some of the current challenges in modern agriculture include unsustainable pesticide use along with the untimely rainfall, increasing temperatures, and climate change which directly impact the productivity of crops and food security. In light of these challenges, we have developed a solution that predicts a country's crop yields with unprecedented accuracy and reliability called AgroYield, this solution employs machine learning technology and leverages features such as crop type, country, year, average rainfall, temperature, and pesticide usage to make yield predictions in kg/hectare. The dataset was preprocessed with OneHotEncoding for categorical variables and StandardScaler for numerical inputs. After trialing multiple regression models, the Decision Tree Regressor was found to perform best. Alongside yield forecasting, AgroYield enables decision-making towards sustainable farming, fostering long-term ecological balance. By helping farmers, agronomists, and policymakers adapt to resource and environmental constraints, this solution aids in building a resilient agricultural ecosystem.
Key Words: Crop Yield Prediction, Machine Learning, OneHotEncoding, StandardScaler, Decision Tree Regressor
Download