Mobile-enabled deep metric learning framework for community-based Soil Organic Matter monitoring

Soil organic matter (SOM) is fundamental to soil health and carbon storage, yet its spatial-temporal variability challenges scalable assessment. Traditional laboratory methods are resource-intensive and unsuitable for large-scale monitoring, particularly in resource-limited regions where SOM remains critically under-monitored. This research addresses this gap by developing a mobile-enabled framework integrating smartphone imaging, deep learning, and citizen science for community-based SOM characterization. Through systematic experiments on 500 samples, we identified optimal imaging conditions: 300–600 lux illumination across warm and cool lighting, and 24 h post-field capacity moisture state. A deep metric learning model using Triplet Loss architecture with ResNet-50 backbone demonstrated superior validation performance with Root Mean Square Error (RMSE) of 0.17 compared to conventional regression methods, Random Forest validation with RMSE of 0.51. The framework was operationalized through a React Native mobile application with Flask-PyTorch backend, featuring sensor-guided image acquisition, cloud-based prediction application programming interfaces, where images undergo preprocessing and are mapped into a learned embedding space using a Triplet Loss architecture, with SOM predicted via similarity-based nearest-neighbour estimation and Inverse Distance Weighting spatial interpolation. External validation with an independent New Brunswick agricultural dataset (n = 30) yielded RMSE = 0.26, successfully capturing spatial variability patterns aligned with topographic controls. By enabling non-specialists to generate georeferenced, high-resolution soil data through intuitive interfaces with real-time feedback, this framework democratizes soil monitoring, supporting precision agriculture and sustainable land management.