Non-destructive measurement of grapevine water potential using near infrared spectroscopy

Abstract

Background and Aims:Near infrared (NIR) spectroscopy techniques are not only used for a variety of physical and chemical analyses in the food industry, but also in remote sensing studies as tools to predict plant water status. In this study, NIR spectroscopy was evaluated as a method to estimate water potential of grapevines. Methods and Results: Cabernet Sauvignon, Chardonnay and Shiraz leaves were scanned using an Integrated Spectronic (300-1100 nm) or an ASD FieldSpec ® 3 (Analytical Spectral Devices, Boulder, Colorado, USA) (350-1850 nm) spectrophotometer and then measured to obtain midday leaf water potential using a pressure chamber. On the same shoot, the leaf adjacent the one used for midday leaf water potential measurement was used to measure midday stem water potential. Calibrations were built and NIR showed good prediction ability (standard error in cross validation (SECV) <0.24 MPa) for stem water potential for each of the three grapevine varieties. The best calibration was obtained for the prediction of stem water potential in Shiraz (R = 0.92 and a SECV = 0.09 MPa). Conclusion: Differences in the NIR spectra were related to the leaf surface from which the spectra were collected, and this had an effect on the accuracy of the calibration results for water potential. We demonstrated that NIR can be used as a simple and rapid method to detect grapevine water status. Significance of the Study: Grapevine water potential can be measured using NIR spectroscopy. The advantages of this new approach are speed and low cost of analysis. It may be possible for NIR to be used as a non-destructive, in-field tool for irrigation scheduling. AbbreviationsYleaf midday leaf water potential; Ystem midday stem water potential; g leaf conductance; NIR near infrared;PC principal component; PCA principal component analysis; PLS partial least squares regression; PRESS prediction residual error sum of squares; R coefficient of correlation; RWC relative water content;SECV standard error in cross validation; SD standard deviation; WUE water use efficiency. Data were obtained during the season 2006-07. n, number of samples used in calibration; R, coefficient of correlation; SD, standard deviation; SECV, standard error of cross validation. De Bei et al. Measure of grapevine water potential using NIR Manuscript

Plant-Based Methods for Irrigation Scheduling of Woody Crops

“…For whole canopies of plants under field conditions, near-infrared (NIR, 700-1100 nm) spectral indices are useful water stress indicators [80,81]. Thus, field-measured hyperspectral remote sensing data is being successfully used to estimate leaf water content and leaf water potential in vineyards [82][83][84][85], cotton fields [86] and maize [87], among other crops.…”

“…Recent advances both in hardware (NIR spectrometers and related systems for data storage) and software (mathematical models for identifying different components in the spectrum of the sampled material, chemometrics) have allowed the development of on-the-go or in-field NIR spectroscopy methods for assessing plant water status, among other crop characteristics [85]. Although robust correlations between observed and predicted water potential have been reported [84,85,90], the price of the equipment, the cost of data acquisition (portable NIR spectrometers and related systems are usually mounted on vehicles than run through the orchard) and the need for substantial data processing are limiting factors for the use of this approach in a context of precision irrigation.…”

Pheno-Copter: A Low-Altitude, Autonomous Remote-Sensing Robotic Helicopter for High-Throughput Field-Based Phenotyping

“…In recent years, the application of such indices has moved from scientific investigation to commercial application with indices, such as NDVI (Normalised Difference Vegetation Index), being used to identify weeds in fallowed fields and to manage in-season nitrogen requirement of crops [4]. New instruments and methods are providing an increasingly diverse set of indices to measure characteristics in field experiments, including canopy temperature [5], pigments, like anthocyanins and carotenoids [6,7], water content indices [8], water potential of leaves [9] and other adaptation traits, such as the stem content of water-soluble carbohydrates [10]. Many of these indices have been found to have associations with the adaptation of crops to drought or well-watered conditions to the degree that they begin to separate the predicted yield performance of different genotypes [11].…”

Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

“…Nevertheless, in both approaches, a wide range of values of the variable of interest must be sampled in the training set of spectra [18]. Recently, a few studies related with the assessment of crop water status have focused on this wavelength optimization process; however, only a limited number of VIs [11,22], or specific regions of the electromagnetic spectrum [13,26,27] have been considered. These studies include applications to several crops, including to vineyards.…”

“…The reflectance at 950-970 nm has proved to be useful for estimation of plant water status [23,24]. Water strongly absorbs radiation at 970 nm, 1200 nm, 1450 nm, 1930 nm, and 2500 nm bands, and these wavelengths can thus be used for estimating plant water content and water potential [4,13,18,[25][26][27]. Several studies have focused on the use of shortwave infrared (SWIR) data to detect crop water stress (e.g., [28,29]).…”