Hyperspectral and fluorescence devices can provide relevant information on physiological plant status related to canopy cover, plant nutrition, water status, pigments concentration and functionality. The aim of this study was to combine data from hyperspectral and fluorescence sensors with plant variables, to delineate homogeneous sub-field areas, using multivariate geostatistics. Proximal sensor and biometric data were collected in a 5-ha durum wheat field at anthesis stage, at 104 georeferenced positions. Fluorescence and hyperspectral data were analysed by principal component analysis to reduce the dimensions of the datasets; the retained components together with plant variables were analysed by means of a multivariate geostatistics approach, factorial co-kriging analysis. A linear model of coregionalization, fitted to the direct and cross experimental variograms of the Gaussian transformed variables, included a nugget effect and a spherical model with a range of 125 m. The first regionalised factor at 125m-scale, explaining 66.9% of the corresponding variance, was able to discriminate areas characterised by better overall plant status and photosynthetic performance from more stressed areas. The approach was sensitive to split the field into two main areas. However, repeated measurements over the crop season are needed to confirm the previous results.

Field partitioning by proximal hyperspectral and fluorescence sensor data and multivariate geostatistics

STELLACCI, ANNA MARIA;Losciale, P.;
2015

Abstract

Hyperspectral and fluorescence devices can provide relevant information on physiological plant status related to canopy cover, plant nutrition, water status, pigments concentration and functionality. The aim of this study was to combine data from hyperspectral and fluorescence sensors with plant variables, to delineate homogeneous sub-field areas, using multivariate geostatistics. Proximal sensor and biometric data were collected in a 5-ha durum wheat field at anthesis stage, at 104 georeferenced positions. Fluorescence and hyperspectral data were analysed by principal component analysis to reduce the dimensions of the datasets; the retained components together with plant variables were analysed by means of a multivariate geostatistics approach, factorial co-kriging analysis. A linear model of coregionalization, fitted to the direct and cross experimental variograms of the Gaussian transformed variables, included a nugget effect and a spherical model with a range of 125 m. The first regionalised factor at 125m-scale, explaining 66.9% of the corresponding variance, was able to discriminate areas characterised by better overall plant status and photosynthetic performance from more stressed areas. The approach was sensitive to split the field into two main areas. However, repeated measurements over the crop season are needed to confirm the previous results.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/184843
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