Quick Determination of Soil Quality Using Portable Spectroscopy and Efficient Multivariate Techniques

Rapid and onsite determination of the soil status and quality parameters holds a brighter potential for improving food security, and minimizing waste of the excessive application of soil amendments hence reducing environmental pollution. In this study, a pocket-sized shortwave NIR spectroscopy (740–1070 nm) and multivariate statistics were used to classify soil from different land-use types and simultaneously predict nitrogen (N), phosphorus (P), potassium (K), calcium (Ca2+), magnesium (Mg2+), and pH in Ghana. Different Algorithms. Linear discriminant analysis (LDA), support vector machine (SVM), and partial least squares algorithms (full-range partial least square, FrPLS; interval partial least squares, IPLS; synergy interval partial least squares, Si-PLS) were attempted for building a suitable classification and quantification model. The models were assessed by the classification rate, coefficient of determination (Rp2), and root mean square error of prediction (RMSEP) in the prediction set. A total of 110 soil samples from 0 to 15 cm, 15 to 30 cm, and 30 to 45 cm layers were collected from the field of different land-use cropping systems. The results obtained showed that SVM had a 98.61% classification rate of the soil from the cropping system. While Si-PLS was superior in predicting N, P, K, Mg2+, Ca2+, and pH. The performance of the Si-PLS model for N, P, K, Mg2+, Ca2+, and pH were 0.571, 0.779, 0.910, 0.778, 0.826, and 0.904 for Rp2 and 0.033%, 0.738 mg·kg−1, 0.117 cmol·kg−1, 0.654 cmol·kg−1, 3.0219 cmol·kg−1, and 0.4760 pH unit for RMSEP, respectively. The results revealed that the portable NIR spectroscopic technique could be used to measure the soil status and some quality parameters. However, further studies are needed to proof its application. This could lead to improving the yield and saving the cost of fertilizer application.