FT-TRANSFORMER-DRIVEN CROP SUITABILITY PREDICTION USING MULTI-NUTRIENT INDICES FOR PRECISION AGRICULTURE
Description
This study proposes an intelligent crop decision support framework that integrates soil nutrient profiling with machine learning and deep learning models to enable precision-oriented agricultural planning. Multiple models, including Random Forest, K-Nearest Neighbors, Dense Neural Networks, 1D Convolutional Neural Networks, and the FT-Transformer, are evaluated for their effectiveness in learning non-linear relationships from tabular soil nutrient data. Comparative evaluation indicates that the FT-Transformer achieves the highest predictive accuracy of 98.64%, demonstrating superior capability in capturing multi-nutrient interdependencies. Random Forest attains an accuracy of 92.00%, while KNN records 90.00%, reflecting limitations of distance-based learning in high-dimensional nutrient spaces.