Estimation of grapevine predawn leaf water potential based on hyperspectral reflectance data in Douro wine region

Hyperspectral data collected through a handheld spectroradiometer (400-1010 nm) were tested for assessing the grapevine predawn leaf water potential (ѱpd) measured by a Scholander chamber in two test sites of Douro wine region. The study was implemented in 2017, being a year with very hot and dry summer, conditions prone to severe water shortage. Three grapevine cultivars, 'Touriga Nacional', 'Touriga Franca' and 'Tinta Barroca' were sampled both in rainfed and irrigated vineyards, with a total of 325 plants assessed in four post-flowering dates. A large set of vegetation indices computed with the hyperspectral data and optimized for the ѱpd values, as well as structural variables, were used as predictors in the model. From a total of 631 possible predictors, four variables were selected based on a stepwise forward procedure and the Wald statistics: irrigation treatment, test site, Anthocyanin Reflectance Index Optimized (ARIopt_656,647) and Normalized Ratio Index (NRI711,700). An ordinal logistic regression model was calibrated using 70 % of the dataset randomly selected and the 30 of the remaining observations where used in model validation. The overall model accuracy obtained with the validation dataset was 73.2 %, with the class of ѱpd corresponding to the high-water deficit presenting a positive prediction value of 79.3 %. The accuracy and operability of this predictive model indicates good perspectives for its use in the monitoring of grapevine water status, and to support the irrigation tasks

Spectral and thermal data as a proxy for leaf protective energy dissipation under kaolin application in grapevine cultivars

Research ArticleThe dynamic effects of kaolin clay particle film application on the temperature and spectral reflectance of leaves of two autochthonous cultivars (Touriga Nacional (TN, n=32) and Touriga Franca (TF, n=24)) were studied in the Douro wine region. The study was implemented in 2017, in conditions prone to multiple environmental stresses that include excessive light and temperature as well as water shortage. Light reflectance from kaolin-sprayed leaves was higher than the control (leaves without kaolin) on all dates. Kaolin’s protective effect over leaves’ temperatures was low on the 20 days after application and ceased about 60 days after its application. Differences between leaves with and without kaolin were explained by the normalized maximum leaf temperature (T_max_f_N), reflectance at 400 nm, 532 nm, and 737 nm, as assessed through TN data. The wavelengths of 532 nm and 737 nm are associated with plant physiological processes, which support the selection of these variables for assessing kaolin’s effects on leaves. The application of principal component analysis to the TF data, based on these four variables (T_max_f_N and reflectances: 400, 532, 737 nm) selected for TN, explained 83.56% of data variability (considering two principal components), obtaining a clear differentiation between leaves with and without kaolin. The T_max_f_N and the reflectance at 532 nm were the variables with a greater contribution for explaining data variability. The results improve the understanding of the vines’ response to kaolin throughout the grapevine cycle and support decisions about the re-application timinginfo:eu-repo/semantics/publishedVersio

NIR attribute selection for the development of vineyard water status predictive models

Near-Infrared spectroscopy (NIR) returns full spectra in the region between 750 and 2500 nm. Although a full spectrum provides extremely informative data, sometimes this enormous amount of detail is redundant and does not bring any additional information. In this work, different attribute selection methods for the development of vineyard water status predictive models are presented. Spectra from grapevine leaves were collected onthe- go (from a moving vehicle) along nine dates during the 2015 season in a commercial vineyard using a NIR spectrometer (1200e2100 nm). Contemporarily, the stem water potential (Jstem) was also measured in the monitored vines. A manual selection, based on Variable Importance in Projection scores (VIP scores) to choose the spectrum intervals including the most important wavelengths (interval selection), the locally most important wavelengths in the spectrum (peak selection), as well as the Interval Partial Least Squares (IPLS) were tested as attribute selection methods. The results obtained for the estimation of Jstem using the whole spectrum (R2 P ¼ 0.84, RMSEP ¼ 0.167 MPa) were comparable to those yielded by the three attribute selection methods: the interval selection method (R2 P ¼ 0.80, RMSEP ¼ 0.186 MPa), the peak selection method (R2 P ¼ 0.77, RMSEP ¼ 0.201 MPa) and the IPLS (R2 P ~ 0.62e0.79, RMSEP ~ 0.186e0.252 MPa). The highest simplification was provided by two IPLS models with three wavelengths and bandwidths of 20 and 4 nm that yielded R2 P~0.78 and RMSEP~ 0.190 MPa. These results corroborate the suitability of a highly reduced selection of NIR wavelengths for the prediction of grapevine water status, and its utility to develop simpler multispectral devices for vineyard water status estimation. © 2023 The Author(s). Published by Elsevier Ltd on behalf of IAgrE. This is an open access article under the CC BY-NC-ND license.PID2019-108330RA-I00MCIN/AEI/10.13039/50110001103

Evaluation of Point Hyperspectral Reflectance and Multivariate Regression Models for Grapevine Water Status Estimation

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A systematic literature review

Barriguinha, A., Neto, M. D. C., & Gil, A. (2021). Vineyard yield estimation, prediction, and forecasting: A systematic literature review. Agronomy, 11(9), 1-27. [1789]. https://doi.org/10.3390/agronomy11091789Purpose—knowing in advance vineyard yield is a critical success factor so growers and winemakers can achieve the best balance between vegetative and reproductive growth. It is also essential for planning and regulatory purposes at the regional level. Estimation errors are mainly due to the high inter-annual and spatial variability and inadequate or poor performance sampling methods; therefore, improved applied methodologies are needed at different spatial scales. This paper aims to identify the alternatives to traditional estimation methods. Design/methodology/approach—this study consists of a systematic literature review of academic articles indexed on four databases collected based on multiple query strings conducted on title, abstract, and keywords. The articles were reviewed based on the research topic, methodology, data requirements, practical application, and scale using PRISMA as a guideline. Findings—the methodological approaches for yield estimation based on indirect methods are primarily applicable at a small scale and can provide better estimates than the traditional manual sampling. Nevertheless, most of these approaches are still in the research domain and lack practical applicability in real vineyards by the actual farmers. They mainly depend on computer vision and image processing algorithms, data-driven models based on vegetation indices and pollen data, and on relating climate, soil, vegetation, and crop management variables that can support dynamic crop simulation models. Research limitations—this work is based on academic articles published before June 2021. Therefore, scientific outputs published after this date are not included. Originality/value—this study contributes to perceiving the approaches for estimating vineyard yield and identifying research gaps for future developments, and supporting a future research agenda on this topic. To the best of the authors’ knowledge, it is the first systematic literature review fully dedicated to vineyard yield estimation, prediction, and forecasting methods.publishersversionpublishe

GeoAI approach to Vineyard Yield Estimation

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsKnowing in advance vineyard yield is a key issue for growers, winemakers, policy makers, and regulators being fundamental to achieve the best balance between vegetative and reproductive growth, and to allow more informed decisions like thinning, irrigation and nutrient management, schedule harvest, optimize winemaking operations, program crop insurance, fraud detection and grape picking workforce demand. In a long-term scenario of perceived climate change, it is also essential for planning and regulatory purposes at the regional level. Estimating yield is complex and requires knowing driving factors related to climate, plant, and crop management that directly influence the number of clusters per vine, berries per cluster, and berry weight. These three yield components explain 60%, 30%, and 10% of the yield. The traditional methods are destructive, labor-demanding, and time-consuming, with low accuracy primarily due to operator errors and sparse sampling (compared to the inherent spatial variability in a production vineyard). Those are supported by manual sampling, where yield is estimated by sampling clusters weight and the number of clusters per vine, historical data, and extrapolation considering the number of vines in a plot. As the extensive research in the area clearly shows, improved applied methodologies are needed at different spatial scales. The methodological approaches for yield estimation based on indirect methods are primarily applicable at small scale and can provide better estimates than the traditional manual sampling. They mainly depend on computer vision and image processing algorithms, data-driven models based on vegetation indices and pollen data, and on relating climate, soil, vegetation, and crop management variables that can support dynamic crop simulation models. Despite surpassing the limitations assigned to traditional manual sampling methods with the same or better results on accuracy, they still lack a fundamental key aspect: the real application in commercial vineyards. Another gap is the lack of solutions for estimating yield at broader scales (e.g., regional level). The perception is that decisions are more likely to take place on a smaller scale, which in some cases is inaccurate. It might be the case in regulated areas and areas where support for small viticulturists is needed and made by institutions with proper resources and a large area of influence. This is corroborated by the fact that data-driven models based on Trellis Tension and Pollen traps are being used for yield estimation at regional scales in real environments in different regions of the world. The current dissertation consists of the first study to identify through a systematic literature review the research approaches for predicting yield in vineyards for wine production that can serve as an alternative to traditional estimation methods, to characterize the different new approaches identifying and comparing their applicability under field conditions, scalability concerning the objective, accuracy, advantages, and shortcomings. In the second study following the identified research gap, a yield estimation model based on Geospatial Artificial Intelligence (GeoAI) with remote sensing and climate data and a machine-learning approach was developed. Using a satellite-based time-series of Normalized Difference Vegetation Index (NDVI) calculated from Sentinel 2 images and climate data acquired by local automatic weather stations, a system for yield prediction based on a Long Short-Term Memory (LSTM) neural network was implemented. The results show that this approach makes it possible to estimate wine grape yield accurately in advance at different scales

Vineyard Terrace Segmentation in the Douro Region Based on Satellite Imagery

The Alto Douro Wine region holds the distinction of being a UNESCO World Heritage Site, known for its traditional vineyard terraces that contribute to its cultural significance. These terraces, engineered to support vine cultivation on the challenging slopes of the Douro valley, were affected by the Phylloxera pest outbreak in the 19th century, resulting in terrace reconstructions for disease control. Preserving this cultural landscape requires periodic evaluations of the terraces, but current manual field assessments are time-consuming, costly, and prone to errors, leading to infrequent updates. To address these challenges, this dissertation studies alternative approaches using multispectral and SAR satellite imagery, and machine learning to detect and identify vineyards within the terraces, aiming to reduce costs and increase assessment frequency. The study begins with a review of remote sensing and satellite imaging technologies, followed by a literature review on similar applications and techniques. Data acquisition details are provided, and three segmentation methodologies are explored: band indices, traditional machine learning (support vector machines and random forests) and deep learning (convolutional neural networks). The deep learning approach, particularly the modified DeepLabV3 model with the ResNet-101 backbone yields the most promising results, despite generalization limitations. Combining the segmented vineyard mask with a slope mask derived from SAR altimetry data increases confidence in identifying vineyards within terraces, offering rough estimations on possible locations of vineyard terraces in the Douro region. In conclusion, this study presents an alternative and cost-effective approach to preserve the heritage landscape of the Alto Douro Wine region. By leveraging satellite imagery and machine learning, it offers a practical and preliminary means for periodic evaluations, supporting the sustainable conservation of this culturally significant region

Viñedos en terrazas en la región vitivinícola del Duero, Portugal: una perspectiva de la gestión del suelo y el agua

The Douro vineyards are a striking example of soil protection materialized in a strongly humanized landscape, where terraces cover a large part of the region. The paper aims at presenting a perspective on soil and water management improvements for Douro terraced vineyards, as a response to actual responsibilities determined by the UNESCO World Heritage statute in preserving a cultural, living and evolutional landscape. After stressing the importance of Douro terraced vineyards in the Portuguese Continental territory and the natural constraints for crop production characterizing the Douro valley, terrace types present in Douro landscapes are described, together with soil changes with terracing operations. Besides the rehabilitation of drystone structures as part of the preservation interventions on the region’s cultural heritage, critical risk areas in recently terraced hillslopes are identified as a priority for soil protection and water management interventions. These are the vineyard areas most expose to direct impact of erosive rainfalls and comprise the inter-row lanes, especially in non-terraced vineyards, the earthen bare risers in recent terraces, and the farm road and drainage networks, spatially coincident, in steep extensively planted hillslopes. Innovative soil and water management practices have to be developed and locally tested in close dialog with regional actors.info:eu-repo/semantics/publishedVersio