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IRFC – Smart Farming: Crop Recommendation System Using Improved Random Forest Classification Algorithm

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  • G. Buvaanyaa
  • M. Gobi

Abstract

A rapidly expanding field that is vital to the development of agricultural methods is crop prediction. Within the framework of machine learning theory, Random Forest (RF) was created. This article examines crop recommendation performance using the Random Forest (RF) machine-learning technique. To accurately forecast which crop would thrive in a particular area, the suggested approach uses a variety of features, including soil and climate data. Performance indicators including MAE, R2, Recall, Precision, and F1-Score were used to demonstrate the effectiveness of this RF model. It revealed that the anticipated result of the RF representations matches the expected data, proving the efficacy of the model. This proposed strategy has significant ramifications for farmers, agricultural stakeholders, and policymakers since it makes data-driven decisions and resource allocation easier, which raises crop yields.

Suggested Citation

  • G. Buvaanyaa & M. Gobi, 2025. "IRFC – Smart Farming: Crop Recommendation System Using Improved Random Forest Classification Algorithm," International Journal of Scientific Research in Artificial Intelligence and Machine Learning, International Journal of Scientific Research in Artificial Intelligence and Machine Learning, vol. 1(3), pages 01-09, June.
    • Handle: RePEc:jbo:ijsrml:v1:y2025:i3:id:27
    Note: Article URL: https://ijsraiml.com/home/article/view/IJSRAIML25131
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