Machine Learning-Based Crop Yield Forecasting Using Environmental and Soil Parameters

Authors

  • Emman Qadir Department of Electrical Engineering, University of Engineering and Technology, Lahore, Pakistan
  • Abdul Wasay Department of Electrical Engineering, University of Engineering and Technology, Lahore, Pakistan
  • Farheen Fayyaz Department of Electrical Engineering, University of Engineering and Technology, Lahore, Pakistan
  • Muhammad Junaid Department of Electrical Engineering, University of Engineering and Technology, Lahore, Pakistan
  • Zaryab Basharat MOE Key Laboratory of Thermo-Fluid Science and Engineering, Xi'an Jiaotong University, China

DOI:

https://doi.org/10.70112/ajsat-2026.15.1.4341

Keywords:

Yield Prediction, Precision Agriculture, Random Forest Regression , Environmental Monitoring, Soil Analysis, Machine Learning

Abstract

Precise crop yield prediction transforms agricultural planning from intuition-based practice to data-driven strategy. This work demonstrates a machine learning approach for forecasting crop yield from seven fundamental agronomic inputs: seasonal temperature, soil pH, total rainfall, pesticide application rate, year, crop type, and cultivation region. Using a cleaned dataset of 28,242 samples spanning 10 crops across more than 100 regions (1990-2013), we deploy a carefully tuned Random Forest regressor. The final model achieves a test R² of 0.9146, RMSE of 7,918.04 units, and MAE of 4,313.27 units. Feature analysis identifies rainfall and soil pH as the most significant factors influencing yield. The system is lightweight, explainable, and suitable for integration with IoT and remote sensing data for next-stage field deployment.

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Published

10-04-2026

How to Cite

Emman Qadir, Abdul Wasay, Farheen Fayyaz, Muhammad Junaid, & Zaryab Basharat. (2026). Machine Learning-Based Crop Yield Forecasting Using Environmental and Soil Parameters. Asian Journal of Science and Applied Technology, 15(1), 13–19. https://doi.org/10.70112/ajsat-2026.15.1.4341

Issue

Vol. 15 No. 1 (2026): January-June 2026

Section

Research Article

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Copyright (c) 2026 Centre for Research and Innovation

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