IRO Journals
Journal of Soft Computing Paradigm

Random Forest–Based Integrated Crop Recommendation and NPK-Driven Fertilizer Decision Support System for Precision Agriculture

Open Access
Published: 27 October, 2026
Pages: 404-418
  • Sampiruthi R. , Sampiruthi R.
    Student, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Anna University, India
    Student, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Anna University, India
  • Sarumathi M. , Sarumathi M.
    Student, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Anna University, India
    Student, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Anna University, India
  • Snekha C. , Snekha C.
    Student, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Anna University, India
    Student, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Anna University, India
  • Zeshan Ali. , Zeshan Ali.
    Student, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Anna University, India
    Student, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Anna University, India
  • GnanaRubini R. GnanaRubini R.
    Assistant Professor, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Anna University, India
    Assistant Professor, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Anna University, India
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How to Cite

R., Sampiruthi, Sarumathi M., Snekha C., Zeshan Ali., and GnanaRubini R. 2026. “Random Forest–Based Integrated Crop Recommendation and NPK-Driven Fertilizer Decision Support System for Precision Agriculture”. Journal of Soft Computing Paradigm 8 (4): 404-18. https://doi.org/10.36548/jscp.2026.4.006.
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Keywords

Precision Agriculture
Multiclass Crop Classification
Random Forest Ensemble
Soil Nutrient Profiling
Agroclimatic Feature Integration
NPK-Based Fertilizer Recommendation
Agricultural Decision Support

Abstract

Precision agriculture calls for data-driven methods to determine the optimal choice of crops and fertilizers depending on soil conditions and climate. In this paper, an integrated decision support system is presented, which is based on Random Forest crop classification and NPK-based fertilizer recommendation system. The proposed system works with seven agronomic features, including nitrogen, phosphorus, potassium, temperature, humidity, soil pH, and rainfall, from crop recommendation dataset with 2,200 entries and 22 classes of crops. After processing data and separating them into training set (80%) and testing set (20%), a Random Forest classifier with 100 decision trees was built to classify the crops. The classified crop is matched with the nutrient knowledge base, and the current soil NPK content is compared to the nutrient needs of the crop. Finally, the rules-based decision engine finds out nutrient deficiencies and makes fertilizer recommendations. The performance of the classifier on the test dataset was 97.6% accuracy, 97.1% precision, 96.8% recall, and 96.9% F1-score.

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