Can fusion of vis-NIR and MIR spectra at three levels improve the prediction accuracy of soil nutrients?

Abstract

Soil nutrients are an important component of soil fertility, and having accurate and timely soil nutrients information is conducive to scientifically guided agricultural fertilization and improved crop yields. Traditional soil nutrient measurement methods are accurate but time-consuming and costly. Visible near-infrared (vis-NIR) and mid-infrared (MIR) as techniques of spectroscopy measurements are cheap, fast, and non-destructive, and, after training, have been used separately to predict soil nutrients. They can give complementary information, so using them separately may not be optimal. This study investigated whether the fusion of vis-NIR and MIR spectra would improve the prediction of six soil nutrients: total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkali-hydrolyzable nitrogen (AN), available phosphorus (AP) and available potassium (AK). The sample set was 501 tillage (0 ∼ 20 cm) soil samples from Guizhou Province (China). Three different sensor fusion techniques were compared: (1) direct fusion of spectra (low-level fusion; SF1); (2) fusion of spectral features selected by the least absolute shrinkage and selection operator (LASSO) algorithm (middle-level fusion; SF2); and (3) fusion using the Granger-Ramanathan averaging (GRA) method of the separate vis-NIR and MIR results (high-level fusion; SF3). Prediction models were built using partial least squares regression (PLSR) and support vector machine (SVM), and evaluated using leave-one-out cross-validation (LOOCV), coefficient of determination (R2), and ratio of performance to interquartile (RPIQ), defined as Rcv2 and RPIQCV. All three sensor fusion methods were more accurate than single-sensor methods in predicting TN, TK and AN (0.60 ≤ Rcv2 ≤ 0.92, 1.86 ≤ RPIQCV ≤ 5.08), but were less accurate for TP, AP and AK (Rcv2 ≤ 0.34, RPIQCV ≤ 1.55). Compared to the best prediction using each single sensor model, low-level fusion improved the prediction of TN and AN; middle-level fusion improved the prediction of TN, TP, AN, AP and AK; and high-level fusion improved the prediction of all soil nutrients. Moreover, the SF2-PLSR model provided the best prediction for TN (Rcv2 = 0.83, RPIQCV = 3.18), the SF2-SVM model for AN (Rcv2 = 0.67, RPIQCV = 2.06), and the SF3-PLSR model for TK (Rcv2 = 0.92, RPIQCV = 5.08). The SF2 and SF3 methods are recommended to predict TN and AN, and MIR spectra are recommended to predict TK (Rcv2 = 0.92, RPIQCV = 5.02) since using only a single sensor is cost-effective. The SF2 and SF3 methods improved the prediction accuracy of AP and AK, but the prediction accuracy was still low. This study only covered one area with its set of soil landscapes, and only used well-established modelling methods. The results can motivate research on new spectra techniques and advanced modelling methods, as well as transferability to other soil landscapes.