Near-infrared leaf reflectance modeling of Annona emarginata seedlings for early detection of variations in nitrogen concentration

Abstract

Nitrogen (N) monitoring is essential in nurseries to ensure the production of high-quality seedlings. Near-infrared spectroscopy (NIRS) is an instantaneous, nondestructive method to monitor N. Spectral data such as NIRS can also provide the basis for developing a new vegetation spectral index (VSI). Here, we evaluated whether NIRS combined with statistical modeling can accurately detect early variations in N concentration in leaves of young plants of Annona emarginata and developed a new VSI for this task. Plants were grown in a hydroponics system with 0, 2.75, 5.5 or 11 mM N for 45 days. Then we measured gas exchange, chlorophylla fluorescence, and pigments in leaves; analyzed complete leaf nutrients, and recorded spectral data for leaves at 966 to 1685 nm using NIRS. With a statistical learning approach, the dimensionality of the spectral data was reduced, then models were generated using two classes (N deficiency, N) or four classes (0, 2.75, 5.5, 11 mM N). The best combination of techniques for dimensionality reduction and classification, respectively, was stepwise regression (PROC STEPDISC) and linear discriminant function. It was possible to detect N deficiency in seedlings leaves with 100% precision, and the four N concentrations with 93.55% accuracy before photosynthetic damage to the plant occurred. Thereby, NIRS combined with statistical modeling of multidimensional data is effective for detecting N variations in seedlings leaves of A. emarginata.