Application of airborne hyperspectral imagery to estimating fruit yield in citrus

This study investigated the applicability of airborne hyperspectral imagery to the estimation of fruit yield in citrus. Hyperspectral images in 72 visible and near-infrared (NIR) wavelengths (from 407 to 898 nm) were acquired over a citrus orchard in Japan by an Airborne Imaging Spectrometer for Applications (AISA) Eagle system. The canopy features of individual trees were identified using pixel-based average spectral reflectance values at various wavelengths from the acquired images. Fruit yields on 48 individual trees were recorded and the yield prediction models were developed using different prediction variables - (i) several commonly used vegetation indices (VIs), (ii) the newly derived two band vegetation index (TBVI) and (iii) principal components (PCs) and partial least square regression (PLS) factors obtained by chemometrics analysis. In spite of the variations of prediction accuracies among different models, this study confirmed the potential of airborne hyperspectral imagery to predict the fruit yield in citrus. Yield estimates can provide valuable information for forecasting yields, planning harvest schedules and generating prescription maps for tree-specific application of alternate bearing control measures and other management practices.