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A machine learning project based on 45 years of historical data (1981-2024) that predicts corn yields in 35 regions of Punjab, Pakistan using meteorological, soil, and agronomic data, achieving a maximum R² accuracy of 92%.
Section 01
This article introduces an open-source project for corn yield prediction in Punjab, Pakistan. Based on 45 years of historical data (1981-2024) including meteorological, soil, and agronomic data, it predicts corn yields in 35 regions. The gradient boosting model achieves an R² of 92.21%, aiming to provide scientific basis for agricultural decision-making and optimize planting efficiency.
Section 02
Pakistan is an important agricultural country, and corn yields in Punjab affect food security. Traditional agricultural decisions rely on experience, making it difficult to cope with the uncertainty of climate change. This project, developed by Muhammad Zeeshan, integrates multi-source data to help farmers and management departments accurately predict yields and optimize planting decisions.
Section 03
The data covers 35 regions from 1981 to 2024, with sources including Pakistan's Bureau of Statistics, agricultural departments, NASA POWER, and ISRIC SoilGrids. Features are divided into three categories: meteorological (temperature, precipitation, etc.), soil (nitrogen, SOC, pH), and agronomic (region, year, etc.). Preprocessing includes handling outliers using the 99th percentile and removing highly correlated humidity features. Models compared include decision tree (R² 0.849), random forest (0.9119), and gradient boosting (optimal at 0.9221). The gradient boosting model uses 800 trees, depth 10, learning rate 0.01, and prevents overfitting via staged_predict.
Section 04
SHAP analysis reveals three key factors affecting yield: soil characteristics (organic carbon, nitrogen), germination period temperature, and total precipitation. A 2023 test in Okara region: actual yield 3673 kg/acre, predicted yield 3615 kg/acre, with an error of about 1.6%. The accuracy supports applications such as agricultural insurance and reserve planning.
Section 05
The technology stack is based on Python, using Pandas (data cleaning), NumPy (numerical computation), Scikit-learn (models), Matplotlib (visualization), and SHAP (interpretability). Future directions: expansion to deep learning, satellite image fusion, integration of real-time weather forecasts, Web/mobile deployment. The project is open-source for global researchers to reference.
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