Soil Nutrient Deficiency Analysis in Tamil Nadu Using Optuna-Optimized CatBoost

Proper detection of soil deficiencies creates essential conditions for both better agricultural output and sustainable farming management methods. The process of traditional soil testing requires extensive time while operating outside real-time requirements and remains inadequate for adapting to dynamic environmental situations. The application of classical machine learning models yields inadequate accuracy results in soil health analysis due to the limitations of non-adaptive features and poor selection process. This research develops a strong approach to analyzing soil deficiency by implementing the CatBoost model and enhancing performance through Optuna. The model operates with macro- and micronutrient village data from Tamil Nadu, which performs soil health classification and provides fertilizer recommendations. The CatBoost algorithm reached a 96% success rate when detecting soil types together with deficiencies. The predictive performance reached 97.18% after adding Optuna integration, which proved the value of optimization techniques for performance improvement. The real-time algorithm supported by artificial intelligence helps farmers make better decisions that enhance nutrient efficiency while improving their crop yields. The investigation shows how innovative machine learning technologies, alongside optimization methods, enable improvements in sustainable soil management techniques coupled with agricultural sustainability success.