Vineyard Canopy Research Articles

The Effect of Irrigation on the Vineyard Canopy and Individual Leaf Morphology Evaluated with Proximal Sensing, Colorimetry, and Traditional Morphometry

The high number of grapevine (Vitis vinifera L.) cultivars grown world-wide are described and identified according to detailed morphological and morphometric descriptor lists. The grapevine leaf is of utmost importance in characterization, despite its traits being very sensitive to environmental factors. In this study, the effect of irrigation/drought stress on the individual leaf morphology and morphometry of the ‘Hárslevelű’ grapevine (Vitis vinifera L.) cultivar was examined. To verify the effect of the applied irrigation methods (drip and subsoil irrigation) on the plant’s water status, water potential measurements were carried out during the 2022 season. The effect of the applied treatments on the vegetative growth was evaluated according to point quadrat and a multichannel LiDAR analysis in order to describe the width of the canopy area, row volume, and area coverage index. The individual leaf morphology was assessed via traditional morphometry and colorimetry. Our results showed that rainfed plants had a significantly lower stem ψ compared to the drip- and subsoil-irrigated plants at all examined dates. The point quadrat results indicate that the leaf layer number was significantly (p < 0.05) influenced by the position, while the treatment showed no effect on the leaf layer number. The leaf colorimetry showed a difference among the samples, as significant alterations were found in 28 out of the 32 examined color properties. Within the traditional morphometric analysis, 54 traits were evaluated, and 14 of the traits were significantly altered due to the different water management systems.

Automated System for Continuous Green Shoot Thinning in Vineyards

Highlights An automated green shoot thinning system was developed by integrating machine vision and mechanical systems. Mask R-CNN used for segmenting cordons at different shoot growth stages. Point cloud registration used to accurately stitch cordon trajectories for continuous thinning operation. Field and lab tests demonstrated the integrated system can efficiently automate green shoot thinning operation. Abstract. Mechanized green shoot thinning in vineyards has helped reduce labor and associated costs. However, high variability in the thinning efficiency has been observed with the current machines in non-selective thinning due to challenges faced by the operators to precisely position thinning end-effectors along the cordon trajectories. In this study, an automated green shoot thinning system is proposed for cordon-trained grapevines to enhance the precision and efficiency of mechanical thinning operation. The automated system consisted of (1) a machine vision system for estimating cordon trajectories and (2) a mechanical system to position thinning rollers along the estimated trajectories during the real-time thinning operation. The vision pipeline used a Mask R-CNN model to determine cordon profiles at different shoot growth stages and a depth image to estimate the 3D cordon trajectory. In addition, point-cloud registration estimated transformation matrices for accurately stitching adjacent cordon trajectories and generating a long map of trajectory for the thinning rollers to follow. An image processing-based calibration method was used to obtain transformation parameters that transformed the estimated trajectories from the camera coordinate space to the thinning-roller coordinate space required for trajectory following. Lab tests, conducted using simulated cordons, showed the integrated system can precisely position the thinning rollers with root mean square errors (RMSE) of 2.0 cm and 1.7 cm for vertical and lateral positions, respectively. To quantify the thinning performance of the integrated system in the field, shoot thinning efficiency (the percentage of target shoots impacted) was determined. From the field evaluation, shoot thinning efficiency of 76% and 85% were achieved for two rows of grapevines, with an overall shoot thinning efficiency of 80%. The favorable test results demonstrated that the proposed integrated system is capable of effectively automating green shoot thinning operations, which has the potential to improve shoot thinning performance in vineyards. Keywords: Automated green shoot thinning, Cordon trajectory estimation, Mask R-CNN, Vineyard canopy management.

Vineyard canopy management techniques to cope with climate change

Vineyard canopy management techniques to cope with climate change

Survey of Pacific Northwest Grapegrowers on Their Use, Choice, and Understanding of Vineyard Canopy Sprayers

Survey of Pacific Northwest Grapegrowers on Their Use, Choice, and Understanding of Vineyard Canopy Sprayers

Transient Heat Therapy Has Few Effects on Grapevine Performance in the Okanagan Valley, British Columbia, Canada

<h3>Background and goals</h3> The goal of this study was to evaluate a commercially available machine (Agrotherm XT, Agrothermal Systems) for transient heat application to enhance vineyard canopy temperature during the growing season and to study the effects of transient heat application on grapevine performance, phenology, and berry quality. <h3>Methods and key findings</h3> Heat was applied up to six times during the growing seasons in 2019 and 2020 in three vineyards located in East and West Kelowna in the Okanagan Valley in British Columbia, Canada. Shoot and cluster counts, bud fruitfulness, grapevine phenology, leaf greenness, harvest, pruning weight, and crop load data were collected each year. Results showed that transient heat application is not an effective management practice. Results were not consistent among vineyard locations or growing seasons. In 2019, treated Merlot vines had fewer berries per cluster but higher juice pH, and treated Chardonnay 2 vines had reduced leaf greenness but higher pruning weight. In 2020, treated Merlot vines had reduced leaf greenness but higher pruning weights, and treated Chardonnay 2 vines had reduced leaf greenness but higher berry weights and soluble solids. In 2021 only, treated Chardonnay 2 vines had greater fruitfulness (clusters/shoot). <h3>Conclusions and significance</h3> Heat treatment had inconsistent effects on grapevine performance and berry quality and no changes in grapevine phenology were observed. Given the high purchase costs, transient heat therapy is not an effective management strategy to improve grapevine performance and berry quality in the north central part of the Okanagan Valley in British Columbia.

Transient Heat Therapy Has Few Effects on Grapevine Performance in the Okanagan Valley, British Columbia, Canada

<h3>Summary</h3> <h3>Goals</h3> The goal of this study was to evaluate a commercially available machine (Agrotherm XT™, Agrothermal Systems®, Napa, CA) for transient heat application as a means of enhancing vineyard canopy temperatures during the growing season and to study the effects of transient heat application on grapevine performance, phenology and berry quality. The study was conducted over two growing seasons in three vineyards located in East and West Kelowna in the Okanagan Valley in British Columbia, Canada. <h3>Key Findings</h3> Transient heat application is not an effective management practice Results were not consistent among vineyard locations nor growing seasons. In 2019 only, treated Merlot vines had fewer berries per cluster but higher juice pH. In 2019, treated Chardonnay 2 vines had reduced leaf greenness but higher pruning weight. In 2020 only, treated Merlot vines had reduced leaf greenness but higher pruning weights. In 2020, treated Chardonnay 2 vines had reduced leaf greenness but higher berry weights and soluble solids. In 2021 only, treated Chardonnay 2 vines had higher fruitfulness (clu/sh). <h3>Impact and Significance</h3> Heat treatment did not have consistent effects on grapevine performance and berry quality, and no changes in grapevine phenology were observed. Given the high purchase costs, transient heat therapy is not an effective management strategy to improve grapevine performance and berry quality in the north central part of the Okanagan Valley in British Columbia.

Sweeping Effects Modify Taylor’s Frozen Turbulence Hypothesis for Scalars in the Roughness Sublayer

AbstractTaylor’s frozen turbulence hypothesis (FTH) is investigated in the roughness sublayer of a sloped vineyard canopy using a spatial array of fine‐wire thermocouples and ultrasonic anemometers. The Ellipse Approximation (EA) method is applied to the measured space‐time temperature correlation function to delineate sweeping effects from advection velocity. Sweeping effects are explained primarily by the turbulence kinetic energy. Upon the removal of sweeping effects, the advection velocity is found to be commensurate with the mean velocity.

Alnus glutinosa and Orientus ishidae (Matsumura, 1902) share phytoplasma genotypes linked to the ‘Flavescence dorée’ epidemics

AbstractFlavescence dorée (FD) is a grapevine disease caused by associated phytoplasmas (FDp) which are epidemically spread by their main vector Scaphoideus titanus. The possible roles of alternative and secondary FDp plant hosts and vectors have gained interest in terms of better understanding of the FDp ecology and epidemiology. The findings of a survey conducted in the surroundings of three vineyards in the southern Swiss Alps aimed at studying the possible epidemiological role of the FDp secondary vector Orientus ishidae and the FDp host plant Alnus glutinosa are reported. This work demonstrates that O. ishidae is able to complete its biological cycle on A. glutinosa and to acquire FDp and 16SrV phytoplasmas very efficiently with an infection rate of 69% for the nymphal instars and 85% for the imagoes. A high prevalence of the map genotype M50 (map type FD1), which is included in the S. titanus—grapevine epidemiological cycle, was found in O. ishidae and A. glutinosa. Additionally, M12 (map type FD3), M44 and M47 were also sporadically detected. Surprisingly, the grapevines tested during this work were all infected by M54 (map type FD2) only, while the few S. titanus caught in the vineyard canopy were all FDp free. In conclusion, the occurrence of infected common alder stands and O. ishidae nearby vineyards do not seem to play a prominent role in FD epidemics in southern Switzerland. Nevertheless, wild vegetation acts as a reservoir of the FDp inoculum, which may locally trigger a FD emergence if S. titanus populations are established inside vineyards.

Assessment of Vineyard Canopy Characteristics from Vigour Maps Obtained Using UAV and Satellite Imagery.

Canopy characterisation is a key factor for the success and efficiency of the pesticide application process in vineyards. Canopy measurements to determine the optimal volume rate are currently conducted manually, which is time-consuming and limits the adoption of precise methods for volume rate selection. Therefore, automated methods for canopy characterisation must be established using a rapid and reliable technology capable of providing precise information about crop structure. This research providedregression models for obtaining canopy characteristics of vineyards from unmanned aerial vehicle (UAV) and satellite images collected in three significant growth stages. Between 2018 and 2019, a total of 1400 vines were characterised manually and remotely using a UAV and a satellite-based technology. The information collected from the sampled vines was analysed by two different procedures. First, a linear relationship between the manual and remote sensing data was investigated considering every single vine as a data point. Second, the vines were clustered based on three vigour levels in the parcel, and regression models were fitted to the average values of the ground-based and remote sensing-estimated canopy parameters. Remote sensing could detect the changes in canopy characteristics associated with vegetation growth. The combination of normalised differential vegetation index (NDVI) and projected area extracted from the UAV images is correlated with the tree row volume (TRV) when raw point data were used. This relationship was improved and extended to canopy height, width, leaf wall area, and TRV when the data were clustered. Similarly, satellite-based NDVI yielded moderate coefficients of determination for canopy width with raw point data, and for canopy width, height, and TRV when the vines were clustered according to the vigour. The proposed approach should facilitate the estimation of canopy characteristics in each area of a field using a cost-effective, simple, and reliable technology, allowing variable rate application in vineyards.

Automated grapevine flower detection and quantification method based on computer vision and deep learning from on-the-go imaging using a mobile sensing platform under field conditions

Grape yield forecasting is a valuable economic and quality issue for the grape and wine industry. The number of flowers at bloom could be used as an early indicator towards crop forecast in viticulture. The purpose of this work was to develop a non-invasive method for grapevine flower counting by on-the-go image acquisition, using a combination of deep learning and computer vision technology. A mobile sensing platform was used at 5 km/h to automatically capture Red Green Blue (RGB) images of vineyard canopy at night using artificial illumination under field conditions. For the image data set, 96 vines from six grapevine varieties were selected. For ground-truthing, the number of flowers per inflorescence was counted on a set of clusters before flowering, while yield per vine was weighted at harvest. The developed algorithm comprised two general steps: inflorescences’ segmentation, and individual flower detection. In both steps, the best results were obtained using the deep fully convolutional neural network SegNet architecture with a VGG19 network as the encoder, with F1 score values of 0.93 and 0.73 in the inflorescences segmentation and the individual flower detection steps, respectively. These values showed the high accuracy of the network. A determination coefficient (R2) of 0.91 between the detected number of flowers and the actual number of flowers per vine was obtained. In addition, a linear regression model was trained to estimate the actual number of flowers from the number of detected flowers. A root mean squared error (RMSE) of 590 flowers per vine and a normalized root mean squared error (NRMSE) of 23.7% was obtained. An R2 above 0.70 was achieved between the estimated number of actual flowers and the final yield, per vine. These results show that the number of flowers per vine can be estimated using machine vision and deep learning. The developed imaging platform can be used by the wine industry in commercial vineyards for a satisfactory early crop yield forecasting.

Monitoring Vineyard Canopy Management Operations Using UAV-Acquired Photogrammetric Point Clouds

Canopy management operations, such as shoot thinning, leaf removal, and shoot trimming, are among the most relevant agricultural practices in viticulture. However, the supervision of these tasks demands a visual inspection of the whole vineyard, which is time-consuming and laborious. The application of photogrammetric techniques to images acquired with an Unmanned Aerial Vehicle (UAV) has proved to be an efficient way to measure woody crops canopy. Consequently, the objective of this work was to determine whether the use of UAV photogrammetry allows the detection of canopy management operations. A UAV equipped with an RGB digital camera was used to acquire images with high overlap over different canopy management experiments in four vineyards with the aim of characterizing vine dimensions before and after shoot thinning, leaf removal, and shoot trimming operations. The images were processed to generate photogrammetric point clouds of every vine that were analyzed using a fully automated object-based image analysis algorithm. Two approaches were tested in the analysis of the UAV derived data: (1) to determine whether the comparison of the vine dimensions before and after the treatments allowed the detection of the canopy management operations; and (2) to study the vine dimensions after the operations and assess the possibility of detecting these operations using only the data from the flight after them. The first approach successfully detected the canopy management. Regarding the second approach, significant differences in the vine dimensions after the treatments were detected in all the experiments, and the vines under the shoot trimming treatment could be easily and accurately detected based on a fixed threshold.

AgriLogger: A New Wireless Sensor for Monitoring Agrometeorological Data in Areas Lacking Communication Networks.

The use of wireless technologies in the field of agriculture, or so-called smart or precision agriculture, is considered as one of the main efforts applied nowadays to multiply the food production on earth. However, wireless sensor network (WSN) technology is still at its early development stage and its application in agriculture and food industry is still rare due to the lack of farmers’ awareness and outreach about the matter. This paper presents a new agro-sensor named AgriLogger with an aim to collect, store for long periods and transmit agrometeorological data represented by temperature and relative humidity in remote areas hard to reach and not served by telecommunication networks. The sensor exhibits long battery life, in the order of 10 years, thanks to low consumption technologies and to hardware sleep/wake up approach. It can be remotely placed on preselected sites through a customized drone. This latter, equipped with a dedicated payload, can then return on the sites where sensors have been placed, and, while hovering, wakes up the single devices and uploads their collected data through local wireless network. Field tests have demonstrated that the sensor, after being placed manually in two different positions, inside and outside a vineyard canopy, is able to collect and store successfully agrometeorological data like temperature and relative humidity. Moreover, the use of a drone potentially allows the collection of data from remote areas and, therefore, is able to provide a periodical monitoring of agro-ecological conditions.

On‐the‐go assessment of vineyard canopy porosity, bunch and leaf exposure by image analysis

Background and Aims Canopy assessment of the fruiting zone can lead to more informed vineyard management decisions. A non‐destructive, image‐based system capable of operating on‐the‐go was developed to assess canopy porosity, and leaf and bunch exposure of red grape cultivars in the vineyard. Methods and Results On‐the‐go (7 km/h) night time images of a vertically shoot positioned commercial vineyard canopy were acquired with an automated red green blue imaging system, coupled to a GPS and controlled artificial lighting. The reference method was point quadrat analysis. Sound correlations between the image analysis and point quadrat analysis results for the proportion of gaps (R2 > 0.85; P 0.57; P < 0.001) were obtained for both sides of the canopy. For the bunch to canopy area ratio the best relationship was found on the western side of the canopy (R2 = 0.79; P < 0.001). Also maps of the three canopy variables were built in a commercial vineyard to compare their spatial variability on the east and west sides across the whole vineyard plot. Conclusions The developed imaging system, capable of operating on‐the‐go, can yield quantitative, objective and reliable knowledge of what a grapegrower would assess by subjective, qualitative visual inspection of the grapevine canopy. The information can be used to help make better informed decisions about leaf removal, and if mapped may help to delineate zones amenable to homogeneous management. Significance of the Study The new developed computer vision system can be mounted on any vehicle, such as a tractor, all terrain vehicle and robot, for a rapid and objective monitoring of the vineyard canopy around the fruiting zone in red cultivars and vertically shoot positioned trained vines. Moreover, the maps generated could be used by a new generation of variable rate viticultural machinery to spatially optimise vineyard cultural practices.

Comparison of Unsupervised Algorithms for Vineyard Canopy Segmentation from UAV Multispectral Images

Technical resources are currently supporting and enhancing the ability of precision agriculture techniques in crop management. The accuracy of prescription maps is a key aspect to ensure a fast and targeted intervention. In this context, remote sensing acquisition by unmanned aerial vehicles (UAV) is one of the most advanced platforms to collect imagery of the field. Besides the imagery acquisition, canopy segmentation among soil, plants and shadows is another practical and technical aspect that must be fast and precise to ensure a targeted intervention. In this paper, algorithms to be applied to UAV imagery are proposed according to the sensor used that could either be visible spectral or multispectral. These algorithms, called HSV-based (Hue, Saturation, Value), DEM (Digital Elevation Model) and K-means, are unsupervised, i.e., they perform canopy segmentation without human support. They were tested and compared in three different scenarios obtained from two vineyards over two years, 2017 and 2018 for RGB (Red-Green-Blue) and NRG (Near Infrared-Red-Green) imagery. Particular attention is given to the unsupervised ability of these algorithms to identify vines in these different acquisition conditions. This ability is quantified by the introduction of over- and under- estimation indexes, which are the algorithm’s ability to over-estimate or under-estimate vine canopies. For RGB imagery, the HSV-based algorithms consistently over-estimate vines, and never under-estimate them. The k-means and DEM method have a similar trend of under-estimation. While for NRG imagery, the HSV is the more stable algorithm and the DEM model slightly over-estimates the vines. HSV-based algorithms and the DEM algorithm have comparable computation time. The k-means algorithm increases computational demand as the quality of the DEM decreases. The algorithms developed can isolate canopy vegetation data, which is useful information about the current vineyard state, and can be used as a tool to be efficiently applied in the crop management procedure within precision viticulture applications.

An intercomparison of radiation partitioning models in vineyard canopies

Multiple radiation transfer models with unique clumping indices (a total of five approaches) were evaluated on two Pinot Noir vineyards in Central California over 3 years. In the first approach, a basic clumping index meant for heterogeneous randomly placed clumped canopies was combined with the Campbell and Norman transfer model (C&N–H). The other four approaches, namely, the Campbell and Norman with rectangular hedgerow clumping index (C&N–R), Campbell and Norman with a geometric elliptical hedgerow model (C&N–E), the 4-stream scattering by arbitrary inclined leaves model (4SAIL) with row-crop clumping index, and the discrete anisotropic radiative transfer (DART) models, account for the unique canopy coverage distribution of the vineyard row-structured canopies. Each modeling approach varied in its complexity to predict transmitted solar radiation at ground level and the outputs were compared to solar radiation observed at the surface with an array of pyranometers. All five modeling approaches showed good agreement with the observed values [correlation coefficients (r) ranged from 0.95 to 0.97]. Model performance varied throughout the season due to their sensitivity to canopy growth. Although r values showed good agreement among all approaches, the C&N–E and DART models showed a better “goodness of fit” with lower root mean squared and bias values.

Vineyard water status assessment using on-the-go thermal imaging and machine learning.

The high impact of irrigation in crop quality and yield in grapevine makes the development of plant water status monitoring systems an essential issue in the context of sustainable viticulture. This study presents an on-the-go approach for the estimation of vineyard water status using thermal imaging and machine learning. The experiments were conducted during seven different weeks from July to September in season 2016. A thermal camera was embedded on an all-terrain vehicle moving at 5 km/h to take on-the-go thermal images of the vineyard canopy at 1.2 m of distance and 1.0 m from the ground. The two sides of the canopy were measured for the development of side-specific and global models. Stem water potential was acquired and used as reference method. Additionally, reference temperatures Tdry and Twet were determined for the calculation of two thermal indices: the crop water stress index (CWSI) and the Jones index (Ig). Prediction models were built with and without considering the reference temperatures as input of the training algorithms. When using the reference temperatures, the best models casted determination coefficients R2 of 0.61 and 0.58 for cross validation and prediction (RMSE values of 0.190 MPa and 0.204 MPa), respectively. Nevertheless, when the reference temperatures were not considered in the training of the models, their performance statistics responded in the same way, returning R2 values up to 0.62 and 0.65 for cross validation and prediction (RMSE values of 0.190 MPa and 0.184 MPa), respectively. The outcomes provided by the machine learning algorithms support the use of thermal imaging for fast, reliable estimation of a vineyard water status, even suppressing the necessity of supervised acquisition of reference temperatures. The new developed on-the-go method can be very useful in the grape and wine industry for assessing and mapping vineyard water status.

Forward-Looking Infrared Cameras for Micrometeorological Applications within Vineyards.

We apply the principles of atmospheric surface layer dynamics within a vineyard canopy to demonstrate the use of forward-looking infrared cameras measuring surface brightness temperature (spectrum bandwidth of 7.5 to 14 μm) at a relatively high temporal rate of 10 s. The temporal surface brightness signal over a few hours of the stable nighttime boundary layer, intermittently interrupted by periods of turbulent heat flux surges, was shown to be related to the observed meteorological measurements by an in situ eddy-covariance system, and reflected the above-canopy wind variability. The infrared raster images were collected and the resultant self-organized spatial cluster provided the meteorological context when compared to in situ data. The spatial brightness temperature pattern was explained in terms of the presence or absence of nighttime cloud cover and down-welling of long-wave radiation and the canopy turbulent heat flux. Time sequential thermography as demonstrated in this research provides positive evidence behind the application of thermal infrared cameras in the domain of micrometeorology, and to enhance our spatial understanding of turbulent eddy interactions with the surface.

Assessment of Vineyard Canopy Porosity Using Machine Vision

Canopy porosity is an important viticultural factor because canopy gaps favor fruit exposure and air circulation, both of which benefit fruit quality and health. Point quadrat analysis (PQA) is standard for assessing canopy gaps but has limited utility because the method is laborious and time consuming. A new, objective, noninvasive, image-based method was developed and compared with PQA to assess the percent canopy gaps in vineyards with diverse viticultural conditions and grape varieties in New Zealand, Croatia, and Spain. The determination coefficient (R2) of the regressions between the percent gaps using both methods exceeded 0.90 (p

Wind-Flow Dynamics Over a Vineyard

Wind-flow dynamics has been extensively studied over horizontally uniform canopies, but agricultural plantations structured in rows such as vineyards have received less attention. Here, the wind flow over a vineyard is studied in neutral stratification from both large-eddy simulation (LES) and in situ measurements. The impact of row structure on the wind dynamics is investigated over a range of wind directions from cross-row to down-row, and a typical range of row aspect ratio (row separation/height ratio). It is shown that the mean flow over a vineyard is similar to that observed in uniform canopies, especially for wind directions from cross-row to diagonal. For down-row winds, the mean flow exhibits noticeable spatial variability across each elementary row-gap pattern, as the wind is channeled in the inter-row. This spatial variability increases with the aspect ratio. With down-row winds the turbulent structures are also more intermittent and generate larger turbulent kinetic energy and momentum flux. The displacement height and roughness length of the vineyard vary with the aspect ratio in a way similar to their variation with canopy density in uniform canopies. Both parameters take smaller values in down-row wind flow, for which the canopy appears more open. The analysis of velocity spectra and autocorrelation functions shows that vineyard canopies share similar features to uniform canopies in terms of turbulent coherent structures, with only minor changes with wind direction.

Advanced Technologies for the Improvement of Spray Application Techniques in Spanish Viticulture: An Overview

Spraying techniques have been undergoing continuous evolution in recent decades. This paper presents part of the research work carried out in Spain in the field of sensors for characterizing vineyard canopies and monitoring spray drift in order to improve vineyard spraying and make it more sustainable. Some methods and geostatistical procedures for mapping vineyard parameters are proposed, and the development of a variable rate sprayer is described. All these technologies are interesting in terms of adjusting the amount of pesticides applied to the target canopy.