In situ estimation of foliar nitrogen in wild blueberry using reflectance spectra

Abstract

In situ estimation of foliar nitrogen in wild blueberry using reflectance spectra. Can. J. Plant Sci. 92: 1155Á1161. Remote sensing techniques have the potential to serve as an important nutrient management tool in wild blueberry. The potential of visible (VIS), near infrared (NIR) and shortwave infrared (SWIR) spectroscopy was evaluated during 2006 (sprout/vegetative phase of production) to estimate foliar nitrogen (N). Canopy reflectance measurements were taken from two nutrient management experimental sites located in Nova Scotia (NS) and New Brunswick (NB). Partial least squares regression (PLSR) estimated foliar N, giving the coefficients of determination (R 2 ) values ranging from 0.69 to 0.85, and root mean square errors of cross validation (RMSECV) from 0.16% (98.29% of mean) to 0.24% (912.43% of mean) for different spectral ranges used in this study. The green peak region located in the VIS region best estimated foliar N. The tested spectral ranges differed in their predictive ability, but generally followed the biochemical basis. Variable importance in projection scores (VIP), regression vector coefficients and PLSR loading weights (LWs) plots highlight the importance of wavebands (Â550 nm, Â610 nm, 1510 nm, Â1690 nm, Â1730 nm, Â1980 nm and Â2030 nm) for in situ foliar N estimations. Thus, it was concluded that reflectance spectra may be used to estimate and ultimately map foliar N in wild blueberry production. The results illustrated the ability of multivariate techniques, such as PLSR to explore hyperspectral data and estimate leaf tissue nutrient content. , Q. U. et Buszard, D. 2012. Estimation in situ de la concentration d'azote dans les feuilles du bleuet sauvage graˆce au spectre de re´flectance. Can. J. Plant Sci. 92: 1155Á1161. Les techniques de te´le´de´tection pourraient faciliter la gestion des e´le´ments nutritifs dans les peuplements de bleuet sauvage. En 2006 (germination/phase ve´ge´tative), les auteurs ont e´value´dans quelle mesure la spectroscopie dans le spectre visible (VIS), dans le proche infrarouge et dans l'infrarouge ondes courtes permet d'estimer la teneur en azote (N) des feuilles. Ils ont mesure´la re´flectance du feuillage a`deux endroits ou`l'on proce´dait a`des expe´riences sur la gestion de la nutrition, soit un en Nouvelle-É cosse (N.-É .) et l'autre au Nouveau-Brunswick (N.-B.). La me´thode de re´gression des moindres carre´s partiels (RMCP) permet d'estimer la concentration foliaire de N, avec des coefficients de de´termination (R 2 ) de 0,69 a`0,85 et une contrevalidation de l'e´cart-type de 0,16 % (9 8,29 % de la moyenne) a`0,24 % (9 12,43 % de la moyenne) pour les fourchettes du spectre employe´es dans l'e´tude. Le pic vert dans la partie VIS du spectre donne l'estimation la plus pre´cise de la concentration foliaire de N. L'utilite´des diverses fourchettes teste´es varie sur le plan des pre´visions, mais ses fondements reposent sur la biochimie. Les courbes trac¸ant l'importance variable des re´sultats obtenus par projection, les coefficients des vecteurs de re´gre...

Breeding blueberries for a changing global environment: a review

“…Guidetti et al ( 2009 ) used a portable spectroradiometer Vis-NIR to accurately estimate soluble solids, firmness and functional compounds (anthocyanins, flavonoids, polyphenols, and ascorbic acid) in fresh and homogenized fruit samples of “Brigitta” and “Duke.” Other work on blueberries focused on monitoring osmo and air dehydration processes (Sinelli et al, 2011 ), SHB cultivar identification (Yang and Lee, 2011 ; Yang et al, 2012 ), and the recognition of foreign materials (leaves and stems) among frozen blueberries (Tsuta et al, 2006 ; Sugiyama et al, 2010 ). In the wild lowbush blueberry ( V. angustifolium ) reflectance data has also been used for detection of internal larvae fruit infestation (Peshlov et al, 2009 ), in situ levels of foliar nitrogen (Bourguignon, 2006 ; Maqbool et al, 2012 ) and to evaluate vegetative (leaf area index) and reproductive (flower number, fruit set, and berry yield) parameters (Percival et al, 2012 ). Most recently, hyperspectral imaging has been used to predict soluble solids content and firmness in NHB fruit (Leiva-Valenzuela et al, 2013 , 2014 ), to identify damaged fruit (Leiva-Valenzuela et al, 2012 ; Leiva-Valenzuela and Aguilera, 2013 ), to classify blueberry fruit growth stages (Yang et al, 2012 , 2014 ), and as a tool for early detection of leaf rust in blueberries (Ahlawat et al, 2011 ).…”