Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops

Abstract

Reflectance spectroscopy, in combination with machine learning and artificial intelligence algorithms, is an effective method for classifying and predicting pigments and phenotyping in agronomic crops. This study aims to use hyperspectral data to develop a robust and precise method for the simultaneous evaluation of pigments, such as chlorophylls, carotenoids, anthocyanins, and flavonoids, in six agronomic crops: corn, sugarcane, coffee, canola, wheat, and tobacco. Our results demonstrate high classification accuracy and precision, with principal component analyses (PCAs)-linked clustering and a kappa coefficient analysis yielding results ranging from 92 to 100% in the ultraviolet–visible (UV–VIS) to near-infrared (NIR) to shortwave infrared (SWIR) bands. Predictive models based on partial least squares regression (PLSR) achieved R2 values ranging from 0.77 to 0.89 and ratio of performance to deviation (RPD) values over 2.1 for each pigment in C3 and C4 plants. The integration of pigment phenotyping methods with fifteen vegetation indices further improved accuracy, achieving values ranging from 60 to 100% across different full or range wavelength bands. The most responsive wavelengths were selected based on a cluster heatmap, β-loadings, weighted coefficients, and hyperspectral vegetation index (HVI) algorithms, thereby reinforcing the effectiveness of the generated models. Consequently, hyperspectral reflectance can serve as a rapid, precise, and accurate tool for evaluating agronomic crops, offering a promising alternative for monitoring and classification in integrated farming systems and traditional field production. It provides a non-destructive technique for the simultaneous evaluation of pigments in the most important agronomic plants.

Chemometric Analysis for the Prediction of Biochemical Compounds in Leaves Using UV-VIS-NIR-SWIR Hyperspectroscopy

“…Flavonoids are secondary metabolites recognised to protect plants against UV radiation and pathogens [20,25]. Their variable concentrations, as denoted by the high CV, possibly reflect the adaptive nature of plants to varying environmental factors.…”

“…The Unscrambler X software, version 10.4 (CAMO Software, Oslo, Norway), was used to conduct PCA on the growth parameter data, with a statistical significance level set at p < 0.01. To avoid underfitting and overfitting, the optimal number of principal components was determined based on the first maximum value of overall accuracy [25].…”

Estimation of Biochemical Compounds in Tradescantia Leaves Using VIS-NIR-SWIR Hyperspectral and Chlorophyll a Fluorescence Sensors

“…Principal component analysis (PCA) is an advanced statistical method that is widely applied to address the complexity and high dimensionality of hyperspectral and fluorescence data [22,[26][27][28]. This method effectively separates and recognises patterns by reducing data dimensionality while maintaining most of the variance, thus revealing the underlying variability among the samples.…”

“…Partial least squares regression (PLSR) is a general method for analysing hyperspectral data for predictive modelling because it can effectively handle highly collinear and multivariate data [22,26,27,39,40]. This method is particularly useful when there are more predictor variables than observations, which is often the case in spectral analysis.…”

“…PLSR stands out for its flexibility and efficiency in dealing with complex and large datasets that are common in hyperspectral imaging, making it a preferred method for both academic research and practical applications in fields such as remote sensing, chemometrics, and precision agriculture [27,40,41]. For those who want to know more about this method, detailed applications and comparisons with other techniques are often found in the scientific literature related to spectroscopy and data analysis [22,26,27,39,40].…”