MAWC-Net: A Multi-Scale Attention Wavelet Convolutional Neural Network for Soil pH Prediction

Soil is a critical natural resource that requires continuous monitoring to support sustainable agriculture. Among soil properties, pH is an essential indicator because it strongly affects nutrient availability and biological activity. Visible–Near-Infrared (Vis–NIR) spectroscopy offers a rapid and cost-effective solution for soil pH prediction, but traditional machine learning models often struggle to effectively extract features from high-dimensional spectral data. To address this challenge, we propose a Multi-Scale Attention Wavelet Convolutional Neural Network (MAWC-Net), which integrates multi-scale convolutions, attention mechanisms, and a Haar Wavelet Decomposition Module (HWDM) to enhance spectral feature representation. Experiments on the LUCAS2009 topsoil dataset demonstrate that MAWC-Net achieves superior prediction accuracy compared with conventional machine learning and deep learning baselines. These findings highlight the potential of wavelet-enhanced deep neural networks to advance soil property modeling and support precision agriculture.