Exploring soil multi-parameter stacking measurement through Raman and NIR dual-spectroscopy

The excessive use of fertilizers can lead to increased production costs, degraded soil quality, diminished product excellence, and environmental contamination. To address this issue, a solution involving soil testing and customizing fertilizer application has been proposed. The current standard methodology for soil parameter assessment relies on chemical analysis performed by trained laboratory technicians, which only allows for the measurement of one indicator at a time. Hence, a novel approach utilizing the fusion of near-infrared (NIR) and Raman dual-spectral features has been suggested to simultaneously determine five crucial indicators (hydrolyzed N, available P, quick-release K, OM, and pH) in soil with a single scan. In this research, seven preprocessing techniques and four feature extraction methods were initially explored to optimize the composite NIR and Raman feature variables. Subsequently, a regressor with a two-layer network structure (RF, LR, SVR; ELM, and PLS) was developed using the stacking algorithm. This methodology synergizes the strengths of the five base learners, minimizes the risk of overfitting, and demonstrates high computational efficiency for linear data correlations and robust fitting capabilities for nonlinear data correlations. Additionally, it showcases strong generalization capabilities, noise resilience, and robustness. The model produced relevant results for hydrolyzed N, available P, quick release K, OM, and pH measurements, with R_p² values of 0.9966, 0.9722, 0.9855, 0.9557, and 0.9951, RMSEP values of 2.9547, 2.9972, 7.6550, 0.0765, and 0.0313, and RPD values of 6.0855, 2.4655, 3.0511, 8.3084, and 10.6977. This work delivers a twofold contribution by presenting a swift method for simultaneous measurement of multiple soil parameters, enabling concurrent ploughing, soil surveying, and fertilizer application. Furthermore, it introduces a stacking measurement model based on dual fusion features, showcasing detailed model parameters. The stacking model outperformed mono-spectral models (NIR and Raman) and the dual PLS model in terms of R_p², RPD, and RMSEP values, and fluctuation ranges, demonstrating enhanced stability, predictive prowess, and reliable observations. Overall, the stacking model offers a cost-effective, rapid, and precise solution for online evaluation of soil physicochemical conditions, catering to the requirements of modern agricultural production well. This innovative approach signifies a significant leap forward and provides a solid theoretical foundation for the enhancement of associated online monitoring systems and tools.