Potential of airborne hyperspectral imagery to estimate fruit yield in citrus
Alternate bearing is a well-marked yield variability phenomenon that occurs in almost all tree-fruit crops. The potential benefits of applying various alternate bearing control measures on alternate bearing crops can only be realized when yield information on individual trees of particular crops is obtained. The objective of this study was to examine the potential of airborne hyperspectral imagery to estimate the 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, which were then used to develop yield prediction models. Yield prediction models were developed using five different techniques - (i) several vegetation indices (VIs), (ii) key wavelengths determined by simple correlation analysis (SCA), (iii) principal components (PCs) based on principal component regression (PCR), and (iv) PLS factors as well as (v) important wavelengths determined by B-matrix based on partial least squares (PLS) regression. The results indicated that the VIs used in this study were poorly correlated with fruit yield on individual trees. The key or important wavelengths determined by the two methods proposed in this study could provide reasonable prediction of fruit yield. Comparatively, the B-matrix method based on the PLS regression was superior to the simple correlation analysis in determining the key or importance wavelengths that are correlated to the fruit yield. However, the PCs extracted from the hyperspectral data were weak predictors of citrus yield. Greater prediction accuracy was obtained with the model based on PLS factors than with the models based on the key or important wavelengths. These results confirmed the hypothesized correlation between canopy features and citrus yield. The methods proposed in this study have considerable promise in estimating fruit yield on individual citrus trees. The yield information is valuable for planning harvest schedules and developing programs for application of tree-specific alternate bearing control measures and other management practices. (C) 2007 Elsevier B.V. All rights reserved.
hyperspectral imagery citrus prediction model principal component regression (pcr) partial least squares (pls) regression remote sensing vegetation index (vi) satsuma mandarin