Smart Agriculture: Optimizing Soil pH and Crop Recommendations Using Machine Learning

This study explores how hyperparameter tuning enhances the performance of machine learning models, achieving a 99.5% accuracy in crop classification and highlighting the potential of AI-driven optimization for sustainable precision agriculture.

Precision agriculture relies heavily on machine learning to provide actionable insights into crop management. This study investigates the application of hyperparameter tuning to improve crop classification models using environmental and soil data. By leveraging a dataset of 22 crop types with features such as nitrogen content, potassium levels, pH, and rainfall, Decision Tree and Random Forest models were developed and optimized. Hyperparameters like max_depth and criterion were systematically adjusted to balance bias and variance. Through extensive tuning, the Decision Tree Classifier achieved a classification accuracy of 99.5%, while the Random Forest Regressor demonstrated a Mean Absolute Error (MAE) of less than 0.4. This paper explores the impact of hyperparameter optimization on model performance and highlights its practical implications for improving crop recommendation systems in agriculture. The findings demonstrate the value of AI-driven decision-making in sustainable farming and pave the way for future innovations in agricultural technology.

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Research Paper