Fusing Environmental Sensing and Computer Vision: A Machine Learning Pipeline for Soil Nutrient and Moisture Mapping From Hyperspectral Imagery

Abstract

Traditional soil nutrient and moisture analysis is labor-intensive, costly, and lacks spatial granularity, hindering precision agriculture. This paper proposes an integrated machine learning pipeline that fuses hyperspectral imagery (HSI) a powerful environmental sensing technology with advanced computer vision techniques to address this gap. We hypothesize that deep learning models can decode the complex, non-linear spectral signatures in HSI data to predict key soil properties accurately. Our methodology encompasses HSI data preprocessing, feature extraction using a Convolutional Neural Network (CNN), and regression modeling. Using a public dataset, we demonstrate that our proposed CNN-based model outperforms traditional spectral indices and machine learning models like Support Vector Regression (SVR) in predicting soil organic carbon (SOC) and moisture content. The results indicate the high potential of this pipeline for generating high-resolution, actionable soil maps to optimize resource use in agriculture.