Data-Driven Crop Suitability Recommendation Using Random Forest And Soil-Climate Feature Analysis
DOI:
https://doi.org/10.63665/IJAICE.0203.07Keywords:
Crop Recommendation, Random Forest, Precision Agriculture, Soil Analysis, Climate Analysis, Machine Learning, Crop Suitability, Data-Driven Agriculture, Sustainable FarmingAbstract
Efficient crop selection is a crucial component of sustainable agriculture, improved productivity, and long-term food security. Selecting an appropriate crop depends on multiple soil and environmental conditions, while conventional farming practices often rely on farmers' experience and traditional cultivation patterns. This study presents a data-driven crop suitability recommendation system using the Random Forest algorithm and soil-climate feature analysis to support accurate and reliable crop selection.
The proposed system uses key agricultural parameters, including Nitrogen, Phosphorus, Potassium, soil pH, temperature, humidity, and rainfall, to evaluate soil fertility and prevailing climatic conditions. Data preprocessing and exploratory analysis are performed to improve data quality and identify significant relationships between agricultural features and crop suitability. A Random Forest Classifier is then employed to learn complex patterns from historical agricultural data. By combining the predictions of multiple decision trees, the model improves classification performance while reducing the risk of overfitting.
The developed system can recommend 22 different crop varieties based on varying soil and climatic conditions. Its performance is assessed using accuracy, precision, recall, F1-score, and a confusion matrix. Experimental results show that the Random Forest model achieves approximately 99% accuracy on the test dataset, demonstrating strong predictive performance and reliability. The proposed approach can support farmers and agricultural stakeholders in making informed, data-driven decisions, thereby contributing to efficient crop planning, sustainable farming practices, and enhanced agricultural productivity.
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