Browsing by Author "Poblete-Echeverria, Carlos"

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    Calibration of the surface renewal method (SR) under different meteorological conditions in an avocado orchard
    (MDPI, 2020-05-19) Moran, Andres; Ferreyra, Raul; Selles, Gabriel; Salgado, Eduardo; Caceres-Mella, Alejandro; Poblete-Echeverria, Carlos
    The 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.
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    Comparison of vegetation indices for leaf area index estimation in vertical shoot positioned vine canopies with and without grenbiule hail-protection netting
    (MDPI, 2019-05-07) Towers, Pedro C.; Strever, Albert; Poblete-Echeverria, Carlos
    Leaf area per unit surface (LAI—leaf area index) is a valuable parameter to assess vine vigour in several applications, including direct mapping of vegetative–reproductive balance (VRB). Normalized difference vegetation index (NDVI) has been successfully used to assess the spatial variability of estimated LAI. However, sometimes NDVI is unsuitable due to its lack of sensitivity at high LAI values. Moreover, the presence of hail protection with Grenbiule netting also affects incident light and reflection, and consequently spectral response. This study analyses the effect of protective netting in the LAI–NDVI relationship and, using NDVI as a reference index, compares several indices in terms of accuracy and sensitivity using linear and logarithmic models. Among the indices compared, results show NDVI to be the most accurate, and ratio vegetation index (RVI) to be the most sensitive. The wide dynamic range vegetation index (WDRVI) presented a good balance between accuracy and sensitivity. Soil-adjusted vegetation index 2 (SAVI2) appears to be the best estimator of LAI with linear models. Logarithmic models provided higher determination coefficients, but this has little influence over the normal range of LAI values. A similar NDVI–LAI relationship holds for protected and unprotected canopies in initial vegetation stages, but different functions are preferable once the canopy is fully developed, in particular, if tipping is performed.
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    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
    (MDPI, 2017) Poblete-Echeverria, Carlos; Olmedo, Guillermo Federico; Ingram, Ben; Bardeen, Matthew
    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.
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    Effect of missing vines on total leaf area determined by NDVI calculated from sentinel satellite data : progressive vine removal experiments
    (MDPI, 2020-05-23) Velez, Sergio; Barajas, Enrique; Rubio, Jose Antonio; Vacas, Ruben; Poblete-Echeverria, Carlos
    Remote Sensing (RS) allows the estimation of some important vineyard parameters. There are several platforms for obtaining RS information. In this context, Sentinel satellites are a valuable tool for RS since they provide free and regular images of the earth’s surface. However, several problems regarding the low-resolution of the imagery arise when using this technology, such as handling mixed pixels that include vegetation, soil and shadows. Under this condition, the Normalized Difference Vegetation Index (NDVI) value in a particular pixel is an indicator of the amount of vegetation (canopy area) rather than the NDVI from the canopy (as a vigour expression), but its reliability varies depending on several factors, such as the presence of mixed pixels or the effect of missing vines (a vineyard, once established, generally loses grapevines each year due to diseases, abiotic stress, etc.). In this study, a vine removal simulation (greenhouse experiment) and an actual vine removal (field experiment) were carried out. In the field experiment, the position of the Sentinel-2 pixels was marked using high-precision GPS. Controlled removal of vines from a block of cv. Cabernet Sauvignon was done in four steps. The removal of the vines was done during the summer of 2019, matching with the start of the maximum vegetative growth. The Total Leaf Area (TLA) of each pixel was calculated using destructive field measurements. The operations were planned to have two satellite images available between each removal step. As a result, a strong linear relationship (R2 = 0.986 and R2 = 0.72) was obtained between the TLA and NDVI reductions, which quantitatively indicates the effect of the missing vines on the NDVI values.
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    Effects of three irrigation strategies on gas exchange relationships, plant water status, yield components and water productivity on grafted carmenere grapevines
    (Frontiers Media, 2018-07-12) Zuniga, Mauricio; Ortega-Farias, Samuel; Fuentes, Sigfredo; Riveros-Burgos, Camilo; Poblete-Echeverria, Carlos
    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.
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    Investigating 2-D and 3-D proximal remote sensing techniques for vineyard yield estimation
    (MDPI, 2019-08-22) Hacking, Chris; Poona, Nitesh; Manzan, Nicola; Poblete-Echeverria, Carlos
    Vineyard yield estimation provides the winegrower with insightful information regarding the expected yield, facilitating managerial decisions to achieve maximum quantity and quality and assisting the winery with logistics. The use of proximal remote sensing technology and techniques for yield estimation has produced limited success within viticulture. In this study, 2-D RGB and 3-D RGB-D (Kinect sensor) imagery were investigated for yield estimation in a vertical shoot positioned (VSP) vineyard. Three experiments were implemented, including two measurement levels and two canopy treatments. The RGB imagery (bunch- and plant-level) underwent image segmentation before the fruit area was estimated using a calibrated pixel area. RGB-D imagery captured at bunch-level (mesh) and plant-level (point cloud) was reconstructed for fruit volume estimation. The RGB and RGB-D measurements utilised cross-validation to determine fruit mass, which was subsequently used for yield estimation. Experiment one’s (laboratory conditions) bunch-level results achieved a high yield estimation agreement with RGB-D imagery (r2 = 0.950), which outperformed RGB imagery (r2 = 0.889). Both RGB and RGB-D performed similarly in experiment two (bunch-level), while RGB outperformed RGB-D in experiment three (plant-level). The RGB-D sensor (Kinect) is suited to ideal laboratory conditions, while the robust RGB methodology is suitable for both laboratory and in-situ yield estimation.
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    Measuring internal maturity parameters contactless on intact table grape bunches using NIR spectroscopy
    (Frontiers Media, 2019) Daniels, Andries J.; Poblete-Echeverria, Carlos; Opara, Umezuruike L.; Nieuwoudt, Helene H.
    The determination of internal maturity parameters of table grape is usually done destructively using manual methods that are time-consuming. The possibility was investigated to determine whether key fruit attributes, namely, total soluble solids (TSS); titratable acidity (TA), TSS/TA, pH, and BrimA (TSS – k x TA) could be determined on intact table grape bunches using Fourier transform near-infrared (FT-NIR) spectroscopy and a contactless measurement mode. Partial Least Squares (PLS) regression models were developed for the maturity and sensory quality parameters using grapes obtained from two consecutive harvest seasons. Statistical indicators used to evaluate the models were the number of latent variables (LVs) used to build the model, the prediction correlation coefficient (R²p) and root mean square error of prediction (RMSEP). For the respective parameters TSS, TA, TSS/TA, pH, and BrimA, the LVs were 21, 23, 5, 7, and 24, the R²p = 0.71, 0.33, 0.57, 0.28, and 0.77, and the RMSEP = 1.52, 1.09, 7.83, 0.14, and 1.80. TSS performed best when moving smoothing windows (MSW) + multiplicative scatter correction (MSC) was used as spectral pre-processing technique, TA with standard normal variate (SNV), TSS/TA with Savitzky-Golay first derivative (SG1d), pH with SG1d, and BrimA with MSC. This study provides the first steps towards a completely nondestructive and contactless determination of internal maturity parameters of intact table grape bunches.
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    Spectral knowledge (SK-UTALCA) : software for exploratory analysis of high-resolution spectral reflectance data on plant breeding
    (Frontiers Media, 2017) Lobos, Gustavo A.; Poblete-Echeverria, Carlos
    This article describes public, free software that provides efficient exploratory analysis of high-resolution spectral reflectance data. Spectral reflectance data can suffer from problems such as poor signal to noise ratios in various wavebands or invalid measurements due to changes in incoming solar radiation or operator fatigue leading to poor orientation of sensors. Thus, exploratory data analysis is essential to identify appropriate data for further analyses. This software overcomes the problem that analysis tools such as Excel are cumbersome to use for the high number of wavelengths and samples typically acquired in these studies. The software, Spectral Knowledge (SK-UTALCA), was initially developed for plant breeding, but it is also suitable for other studies such as precision agriculture, crop protection, ecophysiology plant nutrition, and soil fertility. Various spectral reflectance indices (SRIs) are often used to relate crop characteristics to spectral data and the software is loaded with 255 SRIs which can be applied quickly to the data. This article describes the architecture and functions of SK-UTALCA and the features of the data that led to the development of each of its modules.
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    Spectral reflectance modeling by wavelength Selection : studying the scope for blueberry physiological breeding under contrasting water supply and heat conditions
    (MDPI, 2019) Lobos, Gustavo A.; Escobar-Opazo, Alejandro; Estrada, Felix; Romero-Bravo, Sebastian; Garriga, Miguel; del Pozo, Alejandro; Poblete-Echeverria, Carlos; Gonzalez-Talice, Jaime; Gonzalez-Martinez, Luis; Caligari, Peter
    To overcome the environmental changes occurring now and predicted for the future, it is essential that fruit breeders develop cultivars with better physiological performance. During the last few decades, high-throughput plant phenotyping and phenomics have been developed primarily in cereal breeding programs. In this study, plant reflectance, at the level of the leaf, was used to assess several physiological traits in five Vaccinium spp. cultivars growing under four controlled conditions (no-stress, water deficit, heat stress, and combined stress). Two modeling methodologies [Multiple Linear Regression (MLR) and Partial Least Squares (PLS)] with or without (W/O) prior wavelength selection (multicollinearity, genetic algorithms, or in combination) were considered. PLS generated better estimates than MLR, although prior wavelength selection improved MLR predictions. When data from the environments were combined, PLS W/O gave the best assessment for most of the traits, while in individual environments, the results varied according to the trait and methodology considered. The highest validation predictions were obtained for chlorophyll a/b (R²Val ≤ 0.87), maximum electron transport rate (R²Val ≤ 0.60), and the irradiance at which the electron transport rate is saturated (R²Val ≤ 0.59). The results of this study, the first to model modulated chlorophyll fluorescence by reflectance, confirming the potential for implementing this tool in blueberry breeding programs, at least for the estimation of a number of important physiological traits. Additionally, the differential effects of the environment on the spectral signature of each cultivar shows this tool could be directly used to assess their tolerance to specific environments.
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    Thermal imaging reliability for estimating grain yield and carbon isotope discrimination in wheat genotypes : importance of the environmental conditions
    (MDPI, 2019-06-13) Romero-Bravo, Sebastian; Mendez-Espinoza, Ana Maria; Garriga, Miguel; Estrada, Felix; Escobar, Alejandro; Gonzalez-Martinez, Luis; Poblete-Echeverria, Carlos; Sepulveda, Daniel; Matus, Ivan; Castillo, Dalma; del Pozo, Alejandro; Lobos, Gustavo A.
    Canopy temperature (Tc) by thermal imaging is a useful tool to study plant water status and estimate other crop traits. This work seeks to estimate grain yield (GY) and carbon discrimination (Δ13C) from stress degree day (SDD = Tc − air temperature, Ta), considering the effect of a number of environmental variables such as the averages of the maximum vapor pressure deficit (VPDmax) and the ambient temperature (Tmax), and the soil water content (SWC). For this, a set of 384 and a subset of 16 genotypes of spring bread wheat were evaluated in two Mediterranean-climate sites under water stress (WS) and full irrigation (FI) conditions, in 2011 and 2012, and 2014 and 2015, respectively. The relationship between the GY of the 384 wheat genotypes and SDD was negative and highly significant in 2011 (r2 = 0.52 to 0.68), but not significant in 2012 (r2 = 0.03 to 0.12). Under WS, the average GY, Δ13C, and SDD of wheat genotypes growing in ten environments were more associated with changes in VPDmax and Tmax than with the SWC. Therefore, the amount of water available to the plant is not enough information to assume that a particular genotype is experiencing a stress condition.
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    Vineyard yield estimation using 2-D proximal sensing : a multitemporal approach
    (International Viticulture and Enology Society, 2020-10-23) Hacking, Chris; Poona, Nitesh; Poblete-Echeverria, Carlos
    Vineyard yield estimation is a fundamental aspect in precision viticulture that enables a better understanding of the inherent variability within a vineyard. Yield estimation conducted early in the growing season provides insightful information to ensure the best fruit quality for the maximum desired yield. Proximal sensing techniques provide non-destructive in situ data acquisition for yield estimation during the growing season. This study aimed to determine the ideal phenological stage for yield estimation using 2-dimensional (2-D) proximal sensing and computer vision techniques in a vertical shoot positioned (VSP) vineyard. To achieve this aim, multitemporal digital imagery was acquired weekly over a 12-week period, with a final acquisition two days prior to harvest. Preceding the multitemporal analysis for yield estimation, an unsupervised k-means clustering (KMC) algorithm was evaluated for image segmentation on the final dataset captured before harvest, yielding bunch-level segmentation accuracies as high as 0.942, with a corresponding F1-score of 0.948. The segmentation yielded a pixel area (cm2), which served as input to across-validation model for calculating bunch mass (g). The ‘calculated mass’ was linearly regressed against the ‘actual mass’, indicating the capability for estimating vineyard yield. Results of the multitemporal analysis showed that the final stage of berry ripening was the ideal phenological stage for yield estimation, achieving a global r2 of 0.790.