A study on an accurate modeling for distinguishing nitrogen, phosphorous and potassium status in summer maize using in situ canopy hyperspectral data

Nitrogen (N), phosphorus (P) and potassium (K) are important macronutrients to crops, and hence, in situ, timely and non-destructive estimation of their contents and distinguishing N, P, and K status is of critical prominence in precision farming for rational use of fertilizers. The main goal of this study was to proposes an accurate model to monitor leaf N, P, and K contents (i.e., LNC, LPC and LKC) utilizing canopy hyperspectral data of summer maize. Twelve field experiments were conducted over three consecutive growing seasons (2020-2022) at different sites (Yuanyang, Wen and Fangcheng county) in Henan, China, using different N, P, and K application rates, growing stages, cultivars and ecological sites. The in situ canopy raw hyperspectral (R) were acquired over a wavelength range from 325 to 1075 nm (the visible and near-infrared region). Continuous wavelet transform (CWT) was used to process the collected spectral reflectance; partial least square (PLS) and lambda-lambda r(2) (LL r(2)) models were applied to analyze the relationships between LNC, LPC, and LKC and the spectral reflectance. Results showed that CWT transformation technique can significantly improve the prediction accuracy of summer maize LNC, LPC, and LKC, and the best decomposition scales are CWT-1, CWT-3, and CWT-1. The CWT-PLS model for LNC, LPC, and LKC prediction in the three decomposition scales yielded a relatively higher accuracy compared to the canopy R based on the full range hyperspectra, however, the prediction accuracy varied greatly among the three nutritional status, the effect of the LNC was the best, LKC was the second. The coefficient of determination of the validation datasets (R-val(2)) were 0.821, 0.732 and 0.773 for LNC (CWT-1-PLS), LPC (CWT-3-PLS), and LKC (CWT-1-PLS) prediction, and the relative percentage deviations (RPDval) were 2.176, 1.900, and 2.041, respectively. Eventually, ten bands centred at 405, 517, 560, 660, 685, 735, 750, 770, 838 and 875 nm; ten at 442, 479, 575, 630, 700, 730, 795, 838, 858 and 870 nm; and ten at 479, 540, 597, 653, 695, 755, 808, 858, 870 and 890 nm were selected as effective wavelengths for predicting the LNC, LPC and LKC values. The newly-developed CWT-PLS models for LNC (R-val(2) = 0.780, RPDval = 1.730), LPC (R-val(2) = 0.704, RPDval = 1.434), and LKC (R-val(2) = 0.722, RPDval = 1.725) also provided relatively accurate estimations (RPD > 1.40) based on field experiment validations using the effective wavelengths. The findings will provide theoretical basis and effective methodologies for using the in situ canopy hyperspectral technique to accurate and nondestructive estimation of LNC, LPC, LKC and analyzing N, P, and K nutrient stresses of summer maize.