Hyperspectral imaging data combined with climate data to predict stomatal conductance and transpiration of grapevine plants - Archive ouverte HAL Access content directly
Conference Poster Year : 2022

Hyperspectral imaging data combined with climate data to predict stomatal conductance and transpiration of grapevine plants

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Abstract

Digital agriculture driven by new intelligent sensors is one of the main ways to improve farm management. Accessing physiological variables such as transpiration (E) and stomatal conductance (gs) in real time with optical instruments is challenging. These are the privileged variables to detect water stress. In this study, the objective is to evaluate visible- near-infrared spectral imaging data combined with climate data to predict transpiration (E) and stomatal conductance (gs) of grapevine (Vitis vinifiera L.) plants by using Sequentially- Orthogonalized Partial-Least-Square Regression (SO-PLS). A water stress gradient was obtained using pots of three grape varieties (Syrah, Merlot, Riesling) tested under two water conditions where precise monitoring of physiological variables was performed. Hyperspectral images were acquired and a weather station provided radiation (Rg), relative humidity (RH), temperature (Ta) and wind speed (Ws). For gs, best model is obtained by using only spectral data (R²= 0.656, bias=8.76, RMSE=64.7 mmol.m².s-1). For E, the best model is obtained by using both blocks (R²= 0.699, bias=0.055, RMSE=0.614 mmol.m².s-1). While E prediction model has a lower performance using only spectral data (R²= 0.625, bias=-0.02, RMSE=0.67 mmol.m².s-1). These encouraging results offer prospects for the use of spectral imaging to detect water stress of grapevine plants
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Dates and versions

hal-03738009 , version 1 (25-07-2022)

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Maxime Ryckewaert, Daphné Heran, Thierry Simonneau, Florent Abdelghafour, Romain Boulord, et al.. Hyperspectral imaging data combined with climate data to predict stomatal conductance and transpiration of grapevine plants. 8th International Conference in Spectral Imaging IASIM-2022, Jul 2022, Esbjerg, Denmark. , P03, pp.50-51, 2022, Book of Abstracts. ⟨10.1016/j.compag.2022.106973⟩. ⟨hal-03738009⟩
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