Soil fertility is conventionally evaluated by soil properties such as C, N, and P contents. Evaluation of soil fertility is
now becoming a routine work for soil management and crop production. However, laboratory-analysis based determination of soil
properties is time and cost consuming, which is not suitable for precision agriculture. Here, infrared spectroscopy (IR) appears
as an alternative and fast technique to measure soil fertility. The IR transmission method is generally used in soil qualitative
analysis, while the IR reflectance can be used in soil quantitative analysis, and most of soil-related research is focused
on reflectance spectroscopy. Infrared reflectance spectra, including diffuse reflectance spectra and total attenuated reflectance
spectra, are involved in soil quantitative analysis. We observe an excellent performance of predicting soil C and N contents
using IR spectra. Moreover, in most of cases the predictions of the contents of soil P, K, Ca, Mg, S, and some other microelements
are satisfactory. Soil water, soil clays, and soil microbes can also be characterized and evaluated using IR spectroscopy.
In recent years, a new method named infrared photoacoustic spectra was applied in soil analysis. Infrared-photoacoustic spectra
is indeed more convenient for sample pretreatment and spectra recording, and the recorded soil spectra contain more useful
information versus conventional reflectance spectroscopy. Though currently the application of infrared photoacoustic spectroscopy
in soil analysis is limited, it appears promising to measure soil fertility. The application of infrared spectroscopy in soil
fertility is largely dependent on spectra pretreatment and multivariate calibration due to strong interferences in the spectra.
Partial least square (PLS) and artificial neural network (ANN) are two widely used mathematical tools in the prediction of
soil properties, and more mathematical tools combined models will benefit the prediction performance. To make full use of
soil infrared spectra, soil spectra library construction is needed in future, and a standard procedure should be first decided
in the construction. Based on soil infrared spectra library soil fertility can be fast evaluated combining suitable mathematical
model, which will play an important role in the sustainable agriculture.
Keywords Soil fertility - Sustainable agriculture - Infrared spectroscopy - Reflectance spectra - Photoacoustic spectra - Multivariate calibration - Partial least square - Artificial neural network