Estimation of critical nitrogen contents in peach orchards using visible-near infrared spectral mixture analysis

Abstract

The aim of this study was to predict the critical nitrogen (N) content in peach trees using spectrometric measurements. A nutrient-controlled hydroponics experiment was designed for this purpose. Peach saplings were grown under three N conditions: deficient, sufficient, and excessive. The reflectance values of a plant leaves were measured using a handheld field spectroradiometer fitted with a plant probe. The N contents of leaves were determined in the laboratory and Gaussian mixture discriminant analysis (GMDA) was used to estimate N levels in the leaves from reflectance values. The N levels were categorized for each of the three different N conditions. The wavelengths at 425 nm, 574 nm, 696 nm, and 700 nm were found to be diagnostic of the different N levels. The model developed here classified the experimental plants with high accuracy for NDeficient, 89.28%; NSufficient, 96.30%; and NExcess, 71.42% with 85.71% coefficients. The reliability of the model was also tested under field conditions using 96 peach trees representing the three different N status. Leaves were analyzed by reflectance at 425 nm, 574 nm, 696 nm, and 700 nm, which functioned in real N, percentage classes determined based on the laboratory analyses of the orchard samples, and the data were categorized as NDeficient, NSufficient, and NExcess with a similarity ratio of 77.78%, 80%, and 67.74%, respectively with the general correct classification rate of 75%. The study findings showed that the model developed using hyperspectral reflectance data can discriminate different N nutritional status in plants with an accuracy of ≥70% and can be applied under field conditions. The results of this research provide a new perspective for future studies by showing that GMDA with hyperspectral remote sensing may be useful for the classification of different plant nutrient contents.