Detection and segmentation of vine canopy in ultra-high spatial resolution RGB imagery obtained from unmanned aerial vehicle (UAV): a case study in a commercial vineyard

The use of Unmanned Aerial Vehicles (UAVs) in viticulture permits the capture of aerial Red-Green-Blue (RGB) images with an ultra-high spatial resolution. Recent studies have demonstrated that RGB images can be used to monitor spatial variability of vine biophysical parameters. However, for estimating these parameters, accurate and automated segmentation methods are required to extract relevant information from RGB images. Manual segmentation of aerial images is a laborious and time-consuming process. Traditional classification methods have shown satisfactory results in the segmentation of RGB images for diverse applications and surfaces, however, in the case of commercial vineyards, it is necessary to consider some particularities inherent to canopy size in the vertical trellis systems (VSP) such as shadow effect and different soil conditions in inter-rows (mixed information of soil and weeds). Therefore, the objective of this study was to compare the performance of four classification methods (K-means, Artificial Neural Networks (ANN), Random Forest (RForest) and Spectral Indices (SI)) to detect canopy in a vineyard trained on VSP. Six flights were carried out from post-flowering to harvest in a commercial vineyard cv. Carménère using a low-cost UAV equipped with a conventional RGB camera. The results show that the ANN and the simple SI method complemented with the Otsu method for thresholding presented the best performance for the detection of the vine canopy with high overall accuracy values for all study days. Spectral indices presented the best performance in the detection of Plant class (Vine canopy) with an overall accuracy of around 0.99. However, considering the performance pixel by pixel, the Spectral indices are not able to discriminate between Soil and Shadow class. The best performance in the classification of three classes (Plant, Soil, and Shadow) of vineyard RGB images, was obtained when the SI values were used as input data in trained methods (ANN and RForest), reaching overall accuracy values around 0.98 with high sensitivity values for the three classes

Calibration and validation of an aerodynamic method to estimate the spatial variability of sensible and latent heat fluxes over a drip irrigated Merlot vineyard

A study was carried out to calibrate and validate the aerodynamic temperature method for estimating the spatial variability of the sensible (H) and latent (LE) heat fluxes over a drip-irrigated merlot vineyard located in the Maule Region, in Chile. For this study, measurement of energy balance components and meteorological data were collected from the 2006 to 2010 growing seasons. The experimental plot was composed of a 4.25 ha of ‘Merlot’ vineyard, which was equipped with an Eddy- Covariance system and an automatic weather station. The k-fold cross-validation method was utilized to tune and validate a vineyard surface aerodynamic temperature (Taero) model, considering all of the days when Landsat scenes and ground measurements of meteorological data and surface energy balance (SEB) were available. Then, the satellite-based estimations of Taero were utilized to calculate the surface aerodynamic resistance (rah) and, subsequently, heat fluxes of H and LE. Results indicated that the estimated H and rah values were not significantly different to those measured in the vineyard (95% significance level) showing a root mean square (RMSE) and mean absolute error (MAE) between 34–29 W m−2 and 1.01–0.78 s m−1, respectively. Satellite-based computations of LE were somewhat higher than those measured at the time of satellite overpass (RMSE = 63 W m−2; MAE = 56 W m−2), presumably due to the biases embedded in the net radiation (Rn) and soil heat flux (G) computations. The proposed SEB method based on Taero is very simple to implement, presenting similar accuracies on ET mapping to those computed by complex satellitebased models.Sociedad Argentina de Informática e Investigación Operativ

Infra-Red thermal image analysis for grapevines

Trabajo presentado en el 18th International Symposium of the Group of International Experts of vitivinicultural Systems for CoOperation (GIESCO 2013), celebrado en Oporto del 7 al 11 de julio de 2013.-- Número fuera de serie.Infrared thermal images (IRTI) have been used for grapevine research since the early 90’s. Even though its promising results in the assessment of canopy stomatal conductance and plant water status, from the beginning and recent research publications, it has not been fully applied on a commercial scale yet. It is believed that the bottleneck for this technology is the lack of reliable automation tools for IRTI analysis. Accurate and reliable automation technique s will allow the use of this technique to assess the spatial variability of physiological processes within the canopy using infrared cameras mounted on moving vehicles, drones, octocopters or robots. Automated analysis systems are requirement of The Vineyard of The Future initiative, which is an international effort to establis h fully monitored vineyards in the most prominent viticultural and winemaking areas in the world. In this work, a semi-automated IRTI analyses performed using a code written in MATLAB® for estimate dry and wet references excluding non-leaf temperatures was compared with evaporimeter (EvapoSensor, Skye Instruments Ltd, Powys, UK) measurements used to provide dry and wet references from IRTIs. Results obtained from this research (grapevines cv. Tempranillo) showed good and statistically significant correlations between temperatur e references obtained from IRTI analysis and measured values. This work constitutes one additional step forward to the implementation of thermal imaging as an automated routine technique for physiological vineyard assess ment from proximal sensing and unmanned aerial vehicles (UAV) platforms.The research leading to this report was supported by the Spanish project “STRESSIMAGING HPRN-CT-2002-00254” and Chilean projects CONICYT (Nº 79090035) and Programa de Investigación sobre Adaptación de la Agricultura al Cambio Climático - PIEI (Universidad de Talca).Peer Reviewe

Using artificial intelligence (AI) for grapevine disease detection based on images

Nowadays, diseases are one of the major threats to sustainable viticulture. Manual detection through visual surveys, usually done by agronomists, relies on symptom identification and requires an enormous amount of time. Detection in field conditions remains difficult due to the lack of infrastructure to perform detailed and rapid field scouting covering the whole vineyard. In general, symptoms of grapevine diseases can be seen as spots and patterns on leaves. In this sense, computer vision technologies and artificial intelligence (AI) provide an excellent alternative to improve the current disease detection and quantification techniques using images of leaves and canopy. These novel methods can minimize the time spent on symptom detection, which helps in the control and quantification of the disease severity. In this article, we present some results of deep learning-based approaches used for detecting automatically leaves with downy mildew symptoms from RGB images acquired under laboratory and field conditions. The results obtained so far with AI approaches for detecting leaves with downy mildew symptoms are promising, and they put in evidence of the huge potential of these techniques for practical applications in the context of modern and sustainable viticulture

Inteligencia artificial y visión por ordenador para evaluar los componentes del rendimiento de la vid en viñedos comerciales

Resumen La estimación del rendimiento es muy importante para la industria vitivinícola, ya que proporciona información útil para la gestión de viñedos y bodegas. Los efectos del cambio climático, como el aumento de las temperaturas y la menor disponibilidad de agua, pueden afectar a los componentes del rendimiento del viñedo. En general, la previsión tradicional del rendimiento se basaba en el recuento manual y destructivo de los racimos y el peso de las bayas. Los métodos convencionales no proporcionan una estimación precisa y son lentos, caros y laboriosos. En este estudio, se utilizaron métodos novedosos que emplean tecnologías digitales como el uso de la detección próxima, la visión por ordenador y la inteligencia artificial para la estimación del rendimiento en viñedos comerciales. La visión por ordenador se utilizó para la detección automática de diferentes características del dosel y para la calibración de ecuaciones de regresión para la predicción del rendimiento por cepa. La inteligencia artificial se utilizó para el recuento automático de racimos. Los resultados mostraron que el algoritmo de aprendizaje profundo fue capaz de detectar racimos con una alta precisión. En conclusión, nuestros resultados demostraron la aplicabilidad de estos nuevos métodos para evaluar los componentes del rendimiento en viñedos comerciales

Selecting canopy zones and thresholding approaches to assess grapevine water status by using aerial and ground-based thermal imaging

Aerial and terrestrial thermography has become a practical tool to determine water stress conditions in vineyards. However, for proper use of this technique it is necessary to consider vine architecture (canopy zone analysis) and image thresholding approaches (determination of the upper and lower baseline temperature values). During the 2014–2015 growing season, an experimental study under different water conditions (slight, mild, moderate, and severe water stress) was carried out in a commercial vineyard (Vitis vinifera L., cv. Carménè). In this study thermal images were obtained from different canopy zones by using both aerial (>60 m height) and ground-based (sunlit, shadow and nadir views) thermography. Using customized code that was written specifically for this research, three different thresholding approaches were applied to each image: (i) the standard deviation technique (SDT); (ii) the energy balance technique (EBT); and (iii) the field reference temperature technique (FRT). Results obtained from three different approaches showed that the EBT had the best performance. The EBT was able to discriminate over 95% of the leaf material, while SDT and FRT were able to detect around 70% and 40% of the leaf material, respectively. In the case of canopy zone analysis, ground-based nadir images presented the best correlations with stomatal conductance (gs) and stem water potential (Ψstem), reaching determination coefficients (r2) of 0.73 and 0.82, respectively. The best relationships between thermal indices and plant-based variables were registered during the period of maximum atmospheric demand (near veraison) with significant correlations for all methods

Calibration of the surface renewal method (SR) under different meteorological conditions in an avocado orchard

CITATION: Morán, Andrés et al. 2020. Calibration of the surface renewal method (SR) under different meteorological conditions in an avocado orchard. Agronomy, 10(5):730, doi.org/10.3390/agronomy10050730.The original publication is available at: https://www.mdpi.comThe surface renewal method (SR) allows estimating the sensible heat flux (H) using high-frequency thermocouples. Traditionally, SR has been compared and calibrated using standard instruments such as the Eddy covariance system (EC). Calibration involves correcting H measured with SR (H’SR) by means of the calibration factor (α). However, several studies show that α is not constant and could depend on canopy architecture, measurement height, atmospheric stability, and weather conditions. In avocado orchards, there is not enough information about energy fluxes and the application of the SR method. Therefore, the objective of this study is to calibrate the SR method in a mature avocado orchard considering the effect of meteorological conditions on the determination of α. The components of the surface energy balance were measured using an EC system in a commercial avocado orchard (cv. Hass) located in the Aconcagua Valley, Valparaíso Region, Chile. To evaluate the effect of the meteorological conditions on the determination of α, the dataset was classified into nine categories based on solar radiation and wind intensity. The results show that α varies according to meteorological conditions, with significant differences for cloudy days. The use of the variable α reduced the error in estimating H, so, this methodology can be used to have a more precise approximation of the energy balance and therefore to the water requirements.Publisher's versio

Effects of Three Irrigation Strategies on Gas Exchange Relationships, Plant Water Status, Yield Components and Water Productivity on Grafted Carménère Grapevines

In the Chilean viticultural industry, Carménère is considered an emblematic cultivar that is cultivated mainly in arid and semi-arid zones. For this reason, it is necessary to use precise irrigation scheduling for improving water use efficiency (WUE), water productivity (WP), yield and wine quality. This study evaluated the effects of three deficit irrigation strategies on gas exchange variables, WUE, WP and yield components in a drip-irrigated Carménère vineyard growing under semi-arid climatic conditions during two consecutive seasons (2011/12 and 2012/13). The irrigation strategies were applied in completely randomized design from fruit set (S) to harvest (H). The first irrigation strategy (T1) involved continuous irrigation at 100% of actual evapotranspiration (ETa) from S to the veraison (V) period and at 80% of ETa from V to H. The second irrigation strategy (T2) involved irrigation at 50% of ETa from S to H and the third one (T3) involved no-irrigation from S to V and at 30% of ETa from V to H. The results indicated that there was a significant non-linear correlation between net CO2 assimilation (AN) and stomatal conductance (gs), which resulted in three zones of water stress (zone I = gs > 0.30 mol H2O m-2s-1; zone II = between 0.06 and 0.30 mol H2O m-2s-1; and zone III = gs < 0.06 mol H2O m-2s-1). The use of less water by T2 and T3 had a significant effect on yield components, with a reduction in the weight and diameter of grapes. A significant increase in WP (7.3 kg m-3) occurred in T3, which resulted in values of WUE that were significantly higher than those from T1 and T2. Also, a significant non-linear relationship between the integral water stress (SIΨ) and WP (R2 = 0.74) was established. The results show that grafted Carménère vines were tolerant to water stress although differences between cultivars/genotypes still need to be evaluated

Effect of the illumination angle on NDVI data composed of mixed surface values obtained over vertical‐shoot‐positioned vineyards

CITATION: Towers, P. C. & Poblete‐Echeverria, C. 2021. Effect of the illumination angle on NDVI data composed of mixed surface values obtained over vertical‐shoot‐positioned vineyards. Remote Sensing, 13(5):855, doi:/10.3390/rs13050855.The original publication is available at http://www.mdpi.comPublication of this article was funded by the Stellenbosch University Open Access FundAccurate quantification of the spatial variation of canopy size is crucial for vineyard management in the context of Precision Viticulture. Biophysical parameters associated with canopy size, such as Leaf Area Index (LAI), can be estimated from Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI), but in Vertical-Shoot-Positioned (VSP) vineyards, common satellite, or aerial imagery with moderate-resolution capture information at nadir of pixels whose values are a mix of canopy, sunlit soil, and shaded soil fractions and their respective spectral signatures. VI values for each fraction are considerably different. On a VSP vineyard, the illumination direction for each specific row orientation depends on the relative position of sun and earth. Respective proportions of shaded and sunlit soil fractions change as a function of solar elevation and azimuth, but canopy fraction is independent of these variations. The focus of this study is the interaction of illumination direction with canopy orientation, and the corresponding effect on integrated NDVI. The results confirm that factors that intervene in determining the direction of illumination on a VSP will alter the integrated NDVI value. Shading induced considerable changes in the NDVI proportions affecting the final integrated NDVI value. However, the effect of shading decreases as the row orientation approaches the solar path. Therefore, models of biophysical parameters using moderate-resolution imagery should consider corrections for variations caused by factors affecting the angle of illumination to provide more general solutions that may enable canopy data to be obtained from mixed, integrated vine NDVI.https://www.mdpi.com/2072-4292/13/5/855Publisher's versio