Hyperspectral Imaging of Adaxial and Abaxial Leaf Surfaces for Rapid Assessment of Foliar Nutrient Concentrations in Hass Avocado

Abstract

Rapid assessment tools are required for monitoring crop nutrient status and managing fertiliser applications in real time. Hyperspectral imaging has emerged as a promising assessment tool to manage crop nutrition. This study aimed to determine the potential of hyperspectral imaging for predicting foliar nutrient concentrations in avocado trees and establish whether imaging different sides of the leaves affects prediction accuracy. Hyperspectral images (400–1000 nm) were taken of both surfaces of leaves collected from Hass avocado trees 0, 6, 10 and 28 weeks after peak anthesis. Partial least squares regression (PLSR) models were developed to predict mineral nutrient concentrations using images from (a) abaxial surfaces, (b) adaxial surfaces and (c) combined images of both leaf surfaces. Modelling successfully predicted foliar nitrogen (RP2 = 0.60, RPD = 1.61), phosphorus (RP2 = 0.71, RPD = 1.90), aluminium (RP2 = 0.88, RPD = 2.91), boron (RP2 = 0.63, RPD = 1.67), calcium (RP2 = 0.88, RPD = 2.86), copper (RP2 = 0.86, RPD = 2.76), iron (RP2 = 0.81, RPD = 2.34), magnesium (RP2 = 0.87, RPD = 2.81), manganese (RP2 = 0.87, RPD = 2.76) and zinc (RP2 = 0.79, RPD = 2.21) concentrations from either the abaxial or adaxial surface. Foliar potassium concentrations were predicted successfully only from the adaxial surface (RP2 = 0.56, RPD = 1.54). Foliar sodium concentrations were predicted successfully (RP2 = 0.59, RPD = 1.58) only from the combined images of both surfaces. In conclusion, hyperspectral imaging showed great potential as a rapid assessment tool for monitoring the crop nutrition status of avocado trees, with adaxial surfaces being the most useful for predicting foliar nutrient concentrations.