This work had the goal to assess the capability of hyperspectral line scan imaging (400â1000Â nm) to estimate crop variables in the greenhouse under combined water and nitrogen stress using multivariate data analysis and two data compression methods: canopy average spectra and hyperspectrogram extraction. Hyperspectral images contain far more information than do multispectral ones, which permits discrimination among minute pattern differences in canopy spectral reflectance. A pot greenhouse experiment of eight treatments, from the combination of four nitrogen supply levels and two water supply levels, was designed to test widely varied spinach canopies. Using partial least square regression models, the fresh and dry matter of aboveground biomasses and water and nitrogen contents were estimated from a 76-sample dataset. Both the canopy reflectance-based and hyperspectrogram-based models performed well in estimating variables strictly related to canopy leaf area index (LAI) and geometry, i.e., water content and fresh and dry matters, such that R2in independent validation reached values of 0.87, 0.65, 0.65, and 0.86, 0.74, 0.72, respectively. Estimation of nitrogen concentration from single leaf spectra hyperspectral images produced a high cross-validation R2(0.83), as opposed to the poor predictive results produced from canopy scans. This latter result arose from orientation effects due to canopy architecture. Finally, for estimation purposes, image hyperspectrogram compression without spatial information loss produced more encouraging results while considering canopy structure in crop variables than did average canopy spectra.
Hyperspectral imaging of spinach canopy under combined water and nitrogen stress to estimate biomass, water, and nitrogen content / M. Corti, P. Marino Gallina, D. Cavalli, G. Cabassi. - In: BIOSYSTEMS ENGINEERING. - ISSN 1537-5110. - 158(2017 Jun), pp. 38-50.
|Titolo:||Hyperspectral imaging of spinach canopy under combined water and nitrogen stress to estimate biomass, water, and nitrogen content|
|Parole Chiave:||crop monitoring; hyperspectrograms; multivariate data analysis; remote sensing; vis-nir canopy reflectance; control and systems engineering; food science; animal science and zoology; agronomy and crop science; soil science|
|Settore Scientifico Disciplinare:||Settore AGR/02 - Agronomia e Coltivazioni Erbacee|
|Data di pubblicazione:||giu-2017|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.biosystemseng.2017.03.006|
|Appare nelle tipologie:||01 - Articolo su periodico|
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