Interpretable AI Driven Deep Learning for Soil Fertility Classification

Predicting the appropriate crops for a particular soil category in a specific region is one of the most important issues in precision farming. To solve this problem, different algorithms in machine learning, such as Support Vector Machines (SVM), Decision Tree models, and Random Forest ensembles - have been developed in recent years, which learn the historical data about various soil properties of multiple crops in a particular region to forecast yield productivity. Traditional machine learning methods often struggle with high-dimensional tabular soil data and lack interpretability. This study introduces an interpretable AI driven deep learning framework tailored for tabular datasets to classify the chemical attributes of soil. These attributes include essential macronutrients—nitrogen (N), phosphorus (P), and potassium (K) as well as soil pH, which indicates its acidity or alkalinity. Unlike conventional black-box models, the proposed framework emphasizes interpretability, enabling researchers and farmers to understand the contribution of individual features to the classification process. By harnessing artificial intelligence, the approach delivers real-time, data-informed insights that not only improve the accuracy of soil classification but also contribute to long-term agricultural sustainability through better nutrient management and decision-making. The objective is to highlights the potential of integrating advanced AI techniques like TabNet in modern agricultural decision-making systems to emphasize the limitations of existing methods and propose innovative recommendations for better soil classification. This work involves preprocessing real soil datasets, training the TabNet model using PyTorch, and evaluating its accuracy and feature importance.