Deep metric learning for soil organic matter prediction: A novel similarity-based approach using smartphone-captured images

The accurate assessment of soil organic matter (SOM) is crucial for sustainable agriculture, yet traditional methods remain time-consuming and costly. While smartphone-based digital imaging offers a promising alternative, current approaches face limitations in prediction reliability and generalization capability. This study introduces a novel similarity-based deep learning framework for SOM prediction using smartphone-captured soil images, fundamentally shifting from traditional regression-based methods to a metric learning paradigm. We developed an enhanced image acquisition system and implemented a Triplet Loss network architecture that learns to embed soil images in a semantic space where similarity relationships correlate with SOM content. The system incorporates adaptive image quality assessment and enhancement using the Blind/Referenceless Image Spatial Quality Evaluator and super-resolution techniques. Experimental validation using 500 soil samples from Southern Ontario demonstrated superior performance of our similarity-based approach (validation RMSE = 0.17) compared to traditional regression methods (validation RMSE = 0.51 for Random Forest). The model maintained consistent performance across different soil textures (RMSE variation < 0.05 between texture classes) and environmental conditions (temperature 20–30 °C, humidity 45–75 % RH). The complete analysis pipeline makes the system practical for field applications. Our approach addresses critical challenges in digital soil analysis by providing rapid, reliable, and accessible SOM assessment, contributing to improved soil monitoring and management practices in precision agriculture. These findings demonstrate the potential of similarity-based learning for advancing digital soil sensing technologies and supporting sustainable agricultural practices.