Publication

Abstract : This study explores the potential of hyperspectral imaging combined with machine learning techniques to provide accurate and non-invasive methods for analyzing soil nutrient content in precision agriculture. Data were collected from agricultural regions in Tamil Nadu, India, using conventional soil sampling methods that are labor-intensive and time-consuming. In contrast, hyperspectral imaging preserves soil integrity and enables rapid, remote assessment of soil health. The red fox optimization (FOX) algorithm was employed for spectral band selection, effectively reducing data redundancy while retaining the informative features. The partial least squares regression (PLSR) model achieved high prediction accuracy for organic carbon, with R2=0.93, a mean absolute error (MAE) of 16.4, and a root mean square error (RMSE) of 20.1, whereas for nitrogen, phosphorus, and potassium, the corresponding R2 values all exceeded 0.89. These results confirm the robustness and computational efficiency of the FOX-optimized models and demonstrate that integrating hyperspectral imaging with optimized machine learning can enable accurate, real-time soil nutrient estimation without destructive sampling, thereby supporting sustainable soil monitoring and protection in large-scale precision agriculture.