Leveraging very-high spatial resolution hyperspectral and thermal UAV imageries for characterizing diurnal indicators of grapevine physiology

Efficient and accurate methods to monitor crop physiological responses help growers better understand crop physiology and improve crop productivity. In recent years, developments in unmanned aerial vehicles (UAV) and sensor technology have enabled image acquisition at very-high spectral, spatial, and temporal resolutions. However, potential applications and limitations of very-high-resolution (VHR) hyperspectral and thermal UAV imaging for characterization of plant diurnal physiology remain largely unknown, due to issues related to shadow and canopy heterogeneity. In this study, we propose a canopy zone-weighting (CZW) method to leverage the potential of VHR (≤9 cm) hyperspectral and thermal UAV imageries in estimating physiological indicators, such as stomatal conductance (Gs) and steady-state fluorescence (Fs). Diurnal flights and concurrent in-situ measurements were conducted during grapevine growing seasons in 2017 and 2018 in a vineyard in Missouri, USA. We used neural net classifier and the Canny edge detection method to extract pure vine canopy from the hyperspectral and thermal images, respectively. Then, the vine canopy was segmented into three canopy zones (sunlit, nadir, and shaded) using K-means clustering based on the canopy shadow fraction and canopy temperature. Common reflectance-based spectral indices, sun-induced chlorophyll fluorescence (SIF), and simplified canopy water stress index (siCWSI) were computed as image retrievals. Using the coefficient of determination (R2) established between the image retrievals from three canopy zones and the in-situ measurements as a weight factor, weighted image retrievals were calculated and their correlation with in-situ measurements was explored. The results showed that the most frequent and the highest correlations were found for Gs and Fs, with CZW-based Photochemical reflectance index (PRI), SIF, and siCWSI (PRICZW, SIFCZW, and siCWSICZW), respectively. When all flights combined for the given field campaign date, PRICZW, SIFCZW, and siCWSICZW significantly improved the relationship with Gs and Fs. The proposed approach takes full advantage of VHR hyperspectral and thermal UAV imageries, and suggests that the CZW method is simple yet effective in estimating Gs and Fs

Increases in Vein Length Compensate for Leaf Area Lost to Lobing in Grapevine

Premise:Leaf lobing and leaf size vary considerably across and within species,including among grapevines (Vitisspp.), some of the best‐studied leaves. Weexamined the relationship between leaf lobing and leaf area across grapevinepopulations that varied in extent of leaf lobing.Methods:We used homologous landmarking techniques to measure 2632 leavesacross 2 years in 476 unique, genetically distinct grapevines fromfive biparentalcrosses that vary primarily in the extent of lobing. We determined to what extent leafarea explained variation in lobing, vein length, and vein to blade ratio.Results:Although lobing was the primary source of variation in shape across theleaves we measured, leaf area varied only slightly as a function of lobing. Rather, leafarea increases as a function of total major vein length, total branching vein length, andvein to blade ratio. These relationships are stronger for more highly lobed leaves, withthe residuals for each model differing as a function of distal lobing.Conclusions:For leaves with different extents of lobing but the same area, the morehighly lobed leaves have longer veins and higher vein to blade ratios, allowing themto maintain similar leaf areas despite increased lobing. Thesefindings show howmore highly lobed leaves may compensate for what would otherwise result in areduced leaf area, allowing for increasedphotosynthetic capacity through similarleaf siz

Rootstock Effects on Scion Phenotypes in a ‘Chambourcin’ Experimental Vineyard

Understanding how root systems modulate shoot system phenotypes is a fundamental question in plant biology and will be useful in developing resilient agricultural crops. Grafting is a common horticultural practice that joins the roots (rootstock) of one plant to the shoot (scion) of another, providing an excellent method for investigating how these two organ systems affect each other. In this study, we used the French-American hybrid grapevine ‘Chambourcin’ (Vitis L.) as a model to explore the rootstock–scion relationship. We examined leaf shape, ion concentrations, and gene expression in ‘Chambourcin’ grown ungrafted as well as grafted to three different rootstocks (‘SO4’, ‘1103P’ and ‘3309C’) across 2 years and three different irrigation treatments. We found that a significant amount of the variation in leaf shape could be explained by the interaction between rootstock and irrigation. For ion concentrations, the primary source of variation identified was the position of a leaf in a shoot, although rootstock and rootstock by irrigation interaction also explained a significant amount of variation for most ions. Lastly, we found rootstock-specific patterns of gene expression in grafted plants when compared to ungrafted vines. Thus, our work reveals the subtle and complex effect of grafting on ‘Chambourcin’ leaf morphology, ionomics, and gene expression

Multi-dimensional Leaf Phenotypes Reflect Root System Genotype in Grafted Grapevine Over the Growing Season

Modern biological approaches generate volumes of multi-dimensional data, offering unprecedented opportunities to address biological questions previously beyond reach owing to small or subtle effects. A fundamental question in plant biology is the extent to which below-ground activity in the root system influences above-ground phenotypes expressed in the shoot system. Grafting, an ancient horticultural practice that fuses the root system of one individual (the rootstock) with the shoot system of a second, genetically distinct individual (the scion), is a powerful experimental system to understand below-ground effects on above-ground phenotypes. Previous studies on grafted grapevines have detected rootstock influence on scion phenotypes including physiology and berry chemistry. However, the extent of the rootstock\u27s influence on leaves, the photosynthetic engines of the vine, and how those effects change over the course of a growing season, are still largely unknown

Grapevine leaf size influences canopy temperature

Grapevine leaves have diverse shapes and sizes which are influenced by many factors including genetics, vine phytosanitary status, environment, leaf and vine age, and node position on the shoot. To determine the relationship between grapevine leaf shape or size and leaf canopy temperature, we examined five seedling populations grown in a vineyard in California, USA. The populations had one parent with compound leaves of the Vitis piasezkii type and a different second parent with non-compound leaves. In previous work, we had measured the shape and size of the leaves collected from these populations using 21 homologous landmarks. Here, we paired these morphological data with canopy temperature measurements made using a handheld infrared thermometer. After recording time of sampling and canopy temperature, we used a linear model between time of sampling and canopy temperature to estimate temperature residuals. Based on these residuals, we determined if the canopy temperature of each vine was cooler or warmer than expected, based on the time of sampling. We established a relationship between leaf size and canopy temperature: vines with larger leaves were cooler than expected. By contrast, leaf shape was not strongly correlated with variation in canopy temperature. Ultimately, these findings indicate that vines with larger leaves may contribute to the reduction of overall canopy temperature; however, further work is needed to determine whether this is due to variation in leaf size, differences in the openness of the canopy or other related traits

The Significance Of Viticultural Management And Vinification Decisions On Wine Quality Parameters - Elemental Sulfur Residues And C13 Norisoprenoid Precursors

A series of studies were undertaken to better characterize influences of viticultural and vinification decisions on wine quality parameters, including those that affect aging potential. Elemental sulfur (S0) is commonly used to control powdery mildew, a ubiquitous disease of grapevines. While beneficial over alternative control products in many respects, late season S0 applications increase the potential for undesirable "reduced" aroma development in wine produced from treated fruit, as residual S0 concentrations >1-10 [MICRO SIGN]/g increase H2S evolution during fermentation. However, the persistence of S0 in the vineyard and through vinification is poorly understood, partially owing to the limitations of previous methods for S0 analysis in media that contain other forms of sulfur. A simple, economical technique was developed to quantify S0 residues on grapes in the vineyard and throughout vinification. The technique is based upon complete conversion of S0 to H2S and the subsequent capture and quantification of that gas using commercially available detection tubes. This method was then utilized to analyze grape and must samples from 3 years of field trials, in which the variable factors were S0 formulation, dose, and application timing relative to harvest. Additionally, a series of investigations was undertaken to better understand the impact that increased H2S production during fermentation has on final wine chemistry. While formulation and application rate affected S0 iii residue concentration and persistence for some treatments, timing between final treatment and harvest consistently had an effect on final residue concentrations. Cessation of S0 application 35 days or more prior to harvest resulted in S0 levels below 10[MICRO SIGN]g/g for all treatments. For some treatments, harvest S0 residues below 10[MICRO SIGN]g/g were observed even when spraying was ceased as late as 22 days before harvest. In wine produced from the S0 field trials in which elevated H2S production was observed, there was also increased incidence of H2S formation in finished wines post bottling, even though no H2S was present at bottling. The H2S that re-emerged 3-weeks and 6-months post bottling correlated well with H2S produced during fermentation. H2S produced during fermentation also correlated well with a "latent" pool of H2S that was releasable by treating the wine with a reducing agent. In a separate experiment, the effects of the timing of light exposure on grape derived volatiles was explored. Fruiting zone leaf removal is a common practice to increase light exposure, which has been shown to reduce disease pressure and positively influence ripening. The compound TDN (1,1,6-trimethyl 1,2-dihydronaphthalene) has been linked to the classic "petrol-like" aroma found in some Riesling wines. Higher concentrations of TDN precursors in grapes has been associated with increased cluster sun exposure. A field trial was undertaken in which fruit zone leaf removal was applied at three different timings. Concentrations of carotenoids and bound and free TDN and other volatiles were quantified in harvested berries and in wines. Leaf removal at 33-days post-berry set significantly increased zeaxanthin in Riesling grapes mid-season, total TDN and vitispirane in the juice of mature Riesling grapes, and free and total TDN in finished wine, while earlier or later leaf removal had no effect. i

Workflow to Investigate Subtle Differences in Wine Volatile Metabolome Induced by Different Root Systems and Irrigation Regimes

To allow for a broad survey of subtle metabolic shifts in wine caused by rootstock and irrigation, an integrated metabolomics-based workflow followed by quantitation was developed. This workflow was particularly useful when applied to a poorly studied red grape variety cv. Chambourcin. Allowing volatile metabolites that otherwise may have been missed with a targeted analysis to be included, this approach allowed deeper modeling of treatment differences which then could be used to identify important compounds. Wines produced on a per vine basis, over two years, were analyzed using SPME-GC-MS/MS. From the 382 and 221 features that differed significantly among rootstocks in 2017 and 2018, respectively, we tentatively identified 94 compounds by library search and retention index, with 22 confirmed and quantified using authentic standards. Own-rooted Chambourcin differed from other root systems for multiple volatile compounds with fewer differences among grafted vines. For example, the average concentration of β-Damascenone present in own-rooted vines (9.49 µg/L) was significantly lower in other rootstocks (8.59 µg/L), whereas mean Linalool was significantly higher in 1103P rootstock compared to own-rooted. β-Damascenone was higher in regulated deficit irrigation (RDI) than other treatments. The approach outlined not only was shown to be useful for scientific investigation, but also in creating a protocol for analysis that would ensure differences of interest to the industry are not missed

Dual Activation Function-Based Extreme Learning Machine (ELM) for Estimating Grapevine Berry Yield and Quality

Reliable assessment of grapevine productivity is a destructive and time-consuming process. In addition, the mixed effects of grapevine water status and scion-rootstock interactions on grapevine productivity are not always linear. Despite the potential opportunity of applying remote sensing and machine learning techniques to predict plant traits, there are still limitations to previously studied techniques for vine productivity due to the complexity of the system not being adequately modeled. During the 2014 and 2015 growing seasons, hyperspectral reflectance spectra were collected using a handheld spectroradiometer in a vineyard designed to investigate the effects of irrigation level (0%, 50%, and 100%) and rootstocks (1103 Paulsen, 3309 Couderc, SO4 and Chambourcin) on vine productivity. To assess vine productivity, it is necessary to measure factors related to fruit ripeness and not just yield, as an over cropped vine may produce high-yield but poor-quality fruit. Therefore, yield, Total Soluble Solids (TSS), Titratable Acidity (TA) and the ratio TSS/TA (maturation index, IMAD) were measured. A total of 20 vegetation indices were calculated from hyperspectral data and used as input for predictive model calibration. Prediction performance of linear/nonlinear multiple regression methods and Weighted Regularized Extreme Learning Machine (WRELM) were compared with our newly developed WRELM-TanhRe. The developed method is based on two activation functions: hyperbolic tangent (Tanh) and rectified linear unit (ReLU). The results revealed that WRELM and WRELM-TanhRe outperformed the widely used multiple regression methods when model performance was tested with an independent validation dataset. WRELM-TanhRe produced the highest prediction accuracy for all the berry yield and quality parameters (R2 of 0.522–0.682 and RMSE of 2–15%), except for TA, which was predicted best with WRELM (R2 of 0.545 and RMSE of 6%). The results demonstrate the value of combining hyperspectral remote sensing and machine learning methods for improving of berry yield and quality prediction

Early Detection of Plant Physiological Responses to Different Levels of Water Stress Using Reflectance Spectroscopy

Early detection of water stress is critical for precision farming for improving crop productivity and fruit quality. To investigate varying rootstock and irrigation interactions in an open agricultural ecosystem, different irrigation treatments were implemented in a vineyard experimental site either: (i) nonirrigated (NIR); (ii) with full replacement of evapotranspiration (FIR); or (iii) intermediate irrigation (INT, 50% replacement of evapotranspiration). In the summers 2014 and 2015, we collected leaf reflectance factor spectra of the vineyard using field spectroscopy along with grapevine physiological parameters. To comprehensively analyze the field-collected hyperspectral data, various band combinations were used to calculate the normalized difference spectral index (NDSI) along with 26 various indices from the literature. Then, the relationship between the indices and plant physiological parameters were examined and the strongest relationships were determined. We found that newly-identified NDSIs always performed better than the indices from the literature, and stomatal conductance (Gs) was the plant physiological parameter that showed the highest correlation with NDSI(R603,R558) calculated using leaf reflectance factor spectra (R2 = 0.720). Additionally, the best NDSI(R685,R415) for non-photochemical quenching (NPQ) was determined (R2 = 0.681). Gs resulted in being a proxy of water stress. Therefore, the partial least squares regression (PLSR) method was utilized to develop a predictive model for Gs. Our results showed that the PLSR model was inferior to the NDSI in Gs estimation (R2 = 0.680). The variable importance in the projection (VIP) was then employed to investigate the most important wavelengths that were most effective in determining Gs. The VIP analysis confirmed that the yellow band improves the prediction ability of hyperspectral reflectance factor data in Gs estimation. The findings of this study demonstrate the potential of hyperspectral spectroscopy data in motoring plant stress response