Empirical Analysis of Classification Algorithms for Crop Prediction Based on Soil Nutrients
DOI:
https://doi.org/10.53573/rhimrj.2025.v12n5.020Keywords:
Crop prediction, Classification, Decision Tree, Naïve Bayes, KNNAbstract
An essential aspect of agricultural planning is determining the appropriate crops for cultivation. This study employs data mining classification techniques to predict suitable crops based on soil nutrient availability. The dataset used in this research originates from soil test centers in the Dindigul district of Tamil Nadu, with parameters reflecting the nutrient composition of collected soil samples. Various classification algorithms were applied to soil datasets of different sizes to evaluate their performance. The study examines the efficiency of seven classification models using multiple evaluation metrics, including confusion matrix and classification reports. The results indicate that the Decision Tree classifier provides the most effective predictions for the given soil dataset.
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