Classification of Peronospora infected grapevine leaves with the use of hyperspectral imaging analysis

The present work explores the possible utilization of hyperspectral devices, following a proximity based approach, for the diagnosis of Peronospora infection in the vineyards. It compares the performance of two hyperspectral cameras, characterized by different spectral acquisition ranges, in the identification of different levels of infection as detectable from the analysis of the leaf surface. For this purpose, healthy grapevine leaves and leaves affected by a different grade of Peronospora infection have been acquired in laboratory conditions using two different sensing devices: a Specim Imspector V10™ and a Specim Spectral Camera N17™ working in the region between 400-1000 nm and 1000-1700 nm, respectively. A Partial Least Squares Discriminant Analysis (PLS-DA) model has been built to perform the classification of healthy, infected and necrotic leaves.

[1]  Pedro Melo-Pinto,et al.  Identification of grapevine varieties using leaf spectroscopy and partial least squares , 2013 .

[2]  Renfu Lu,et al.  Optimization of the hyperspectral imaging-based spatially-resolved system for measuring the optical properties of biological materials , 2010 .

[3]  F. J. Pierce,et al.  The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars , 2009 .

[4]  P. Zarco-Tejadaa,et al.  Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle ( UAV ) , 2013 .

[5]  Sarah L. MacDonald,et al.  Remote hyperspectral imaging of grapevine leafroll-associated virus 3 in cabernet sauvignon vineyards , 2016, Comput. Electron. Agric..

[6]  Julio Nogales-Bueno,et al.  Determination of technological maturity of grapes and total phenolic compounds of grape skins in red and white cultivars during ripening by near infrared hyperspectral image: a preliminary approach. , 2014, Food chemistry.

[7]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[8]  A. Mena,et al.  Valutazione di sistemi ottici per la diagnosi di peronospora su piante di Vitis Vinifera , 2011 .

[9]  G Bonifazi,et al.  Early detection of toxigenic fungi on maize by hyperspectral imaging analysis. , 2010, International journal of food microbiology.

[10]  M. Barker,et al.  Partial least squares for discrimination , 2003 .

[11]  P. Curran Remote sensing of foliar chemistry , 1989 .

[12]  Antonietta Baiano,et al.  Application of hyperspectral imaging for prediction of physico-chemical and sensory characteristics of table grapes , 2012 .

[13]  M. D. Steven,et al.  Plant spectral responses to gas leaks and other stresses , 2005 .

[14]  Paul Geladi,et al.  Hyperspectral imaging: calibration problems and solutions , 2004 .

[15]  John R. Miller,et al.  Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy , 2005 .

[16]  Z. Niu,et al.  Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging , 2007, Precision Agriculture.

[17]  L. Plümer,et al.  Original paper: Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance , 2010 .

[18]  Zou Xiaobo,et al.  Nondestructive diagnostics of nitrogen deficiency by cucumber leaf chlorophyll distribution map based on near infrared hyperspectral imaging , 2012 .

[19]  Colm P. O'Donnell,et al.  Visible-Near Infrared Hyperspectral Imaging for the Identification and Discrimination of Brown Blotch Disease on Mushroom (Agaricus Bisporus) Caps , 2010 .

[20]  Douglas Fernandes Barbin,et al.  Grape seed characterization by NIR hyperspectral imaging , 2013 .