NIR hyperspectral imaging spectroscopy and chemometrics for the discrimination of roots and crop residues extracted from soil samples

Roots play a major role in plant development. Their study in field conditions is important to identify suitable soil management practices for sustainable crop productions. Soil coring, which is a common method in root production measurement, is limited in sampling frequency due to the hand-sorting step. This step, needed to sort roots from other elements extracted from soil cores like crop residues, is time consuming, tedious, and vulnerable to operator ability and subjectivity. To get rid of the cumbersome hand-sorting step, avoid confusion between these elements, and reduce the time needed to quantify roots, a new procedure, based on near-infrared hyperspectral imaging spectroscopy and chemometrics, has been proposed. It was tested to discriminate roots of winter wheat (Triticum aestivum L.) from crop residues and soil particles. Two algorithms (support vector machine and partial least squares discriminant analysis) have been compared for discrimination analysis. Models constructed with both algorithms allowed the discrimination of roots from other elements, but the best results were reached with models based on support vector machine. The ways to validate models, with selected spectra or with hyperspectral images, provided different kinds of information but were complementary. This new procedure of root discrimination is a first step before root quantification in soil samples with near-infrared hyperspectral imaging. The results indicate that the methodology could be an interesting tool to improve the understanding of the effect of tillage or fertilization, for example, on root system development. Root quantification based on the soil coring method is limited in sampling frequency due to the hand-sorting step needed to sort roots and crop residues extracted from soil samples. To get rid of hand-sorting step and avoid confusion between these elements, a new procedure, based on near-infrared hyperspectral imaging spectroscopy and chemometrics, has been proposed. Support vector machine and partial least squares discriminant analysis have been compared and allowed both the discrimination of elements, but the best results were reached with support vector machine.