Digital soil mapping of available phosphorus using a smartphone-integrated RGB imaging device and ascorbic acid extraction method

Soil fertility, specifically phosphorus (P) availability, is critical for agricultural productivity and environmental health. Traditional methods for measuring soil available P are typically lab-based and expensive. This study explored the potential of a smartphone-integrated imaging device combined with a digital soil mapping (DSM) approach to estimate and spatially map soil available P in six districts of West Bengal, India. A total of 482 surface soil samples were collected and analyzed using both conventional spectrophotometry (UV) and the developed smartphone-based method (NP). The results showed a strong correlation (R² = 0.94) between the two methods, with no significant difference in P estimation, validating the device's field applicability. A DSM model was developed using environmental covariates and random forest model to predict soil P distribution. Additionally, synthetic data were generated using three generative models (Triplet-based Variational Autoencoder, Conditional Tabular Generative Adversarial Network, and Gaussian Copula) to enhance prediction accuracy, particularly for high P values. The Gaussian Copula model, combined with real data, provided the highest test accuracy (R² = 0.69, RMSE = 21.15 kg ha⁻¹ for UV and R² = 0.73, RMSE = 12.91 kg ha⁻¹ for NP). Spatial maps revealed high P availability in alluvial soils of Nadia and East Medinipur, and low P in red and lateritic soils of Birbhum and Jhargram, reflecting the influence of soil type and climatic conditions. The smartphone-based device, coupled with DSM, offers a cost-effective, accurate, and practical tool for soil P assessment and mapping. This technology can significantly aid farmers in resource-constrained regions by providing precise nutrient management recommendations, enhancing sustainable agricultural practices, and mitigating environmental impacts. Future work will focus on further validation with diverse soil types and continuous improvement of the DSM models to address dynamic soil nutrient variability.