Image-based sensing of salt stress in grapevine

Abstract

Grapevine is among the most economically important crops suffering environmental constraints, including drought and salt stress. Although imaging is increasingly used to detect abiotic stress in agriculture, image-based phenotyping in grapevine still needs optimisation. This study presents the RGB-(red, green, blue)-based phenotyping of the early stage of salt stress response in potted grapevine (Aleatico/SO4) irrigated with saline water (100 mM NaCl) for 9 days in contrast with vines irrigated with fresh water. The response was measured using stomatal conductance (gs), net photosynthetic rate (A), transpiration (E), maximum potential photosynthetic efficiency (Fv/Fm), stem water potential (SWP) concurrently with RGB imaging via a robotised platform.The image-based phenotyping of salt-stressed vines employed two sets of measurements: (i) the pixel fraction of specific colour bands (Yellow, Green, Brown and Dark Green) and (ii) the mean pixel value of R, G and B and other RGB-based colorimetric indexes. Results show that the responses of gs, A, E, Fv/Fm were closely related to increasing soil electrical conductivity (EC) and that imaging could detect the EC threshold of approx. 4 dS m-1 causing a ~60 % decrease in these physiological traits compared to the pre-stress level. The SWP declined to about –0.7 MPa at the end of the experiment. The change of the relative pixel fraction of Dark Green to increasing EC has been analysed within a dose-response context, showing that a decrease of 1 % of the Dark Green colour band corresponded to the 4 dS m-1 EC threshold. This study also examined the use of the mean pixel value of the R, G and B channels as proxies of EC along with new RGB-based indexes resulting from the rearrangement of original R, G and B mean pixel values. Results show the suitability of the mean pixel value of R and Coloration Index [(R-B)/R] to serve as predictors of EC (R2 >= 0.80).

Plant phenotyping relevance

RGB画像とロボット化プラットフォームを用いてブドウの塩ストレス表現型を抽出し、画素特徴量を生理指標・土壌ECと関連付けて評価することが中心であるため。

Code and data availability

The article describes RGB imaging of salt-stressed grapevines on a LemnaTec Scanalyzer and LemnaGrid analysis, but no public phenotype dataset, images, or author analysis code is deposited or linked. The only URL (EMPHASIS infrastructure site) describes the facility, not this paper's data.

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