Details
Name
Mário CunhaCluster
Industrial and Systems EngineeringRole
Senior ResearcherSince
01st February 2018
Nationality
PortugalCentre
Robotics in Industry and Intelligent SystemsContacts
+351220413317
mario.cunha@inesctec.pt
2020
Authors
Pocas, I; Tosin, R; Goncalves, I; Cunha, M;
Publication
Agricultural and Forest Meteorology
Abstract
The predawn leaf water potential (?pd) is an eco-physiological indicator widely used for assessing vines water status and thus supporting irrigation management in several wine regions worldwide. However, the ?pd is measured in a short time period before sunrise and the collection of a large sample of points is necessary to adequately represent a vineyard, which constitute operational constraints. In the present study, an alternative method based on hyperspectral data derived from a handheld spectroradiometer and machine learning algorithms was tested and validated for assessing grapevine water status. Two test sites in Douro wine region, integrating three grapevine cultivars, were studied for the years of 2014, 2015, and 2017. Four machine learning regression algorithms were tested for predicting the ?pd as a continuous variable, namely Random Forest (RF), Bagging Trees (BT), Gaussian Process Regression (GPR), and Variational Heteroscedastic Gaussian Process Regression (VH-GPR). Three predicting variables, including two vegetation indices (NRI554,561 and WI900,970) and a time-dynamic variable based on the ?pd (?pd_0), were applied for modelling the response variable (?pd). Additionally, the predicted values of ?pd were aggregated into three classes representing different levels of water deficit (low, moderate, and high) and compared with the corresponding classes of ?pd observed values. A root mean square error (RMSE) and a mean absolute error (MAE) lower or equal than 0.15 MPa and 0.12 MPa, respectively, were obtained with an external validation data set (n = 71 observations) for the various algorithms. When the modelling results were assessed through classes of values, a high overall accuracy was obtained for all the algorithms (82–83%), with prediction accuracy by class ranging between 79% and 100%. These results show a good performance of the predictive models, which considered a large variability of climatic, environmental, and agronomic conditions, and included various grape cultivars. By predicting both continuous values of ?pd and classes of ?pd, the approach presented in this study allowed obtaining 2-levels of accurate information about vines water status, which can be used to feed management decisions of different types of stakeholders. © 2019 Elsevier B.V.
2020
Authors
Tosin, R; Pocas, I; Goncalves, I; Cunha, M;
Publication
VITIS
Abstract
Hyperspectral data collected through a handheld spectroradiometer (400-1010 nm) were tested for assessing the grapevine predawn leaf water potential (psi(pd)) 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 psi(pd) 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 (ARI(opt_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 psi(pd) 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.
2020
Authors
Pocas, I; Calera, A; Campos, I; Cunha, M;
Publication
Agricultural Water Management
Abstract
The advances achieved during the last 30 years demonstrate the aptitude of the remote sensing-based vegetation indices (VI) for the assessment of crop evapotranspiration (ETc) and irrigation requirements in a simple, robust and operative manner. The foundation of these methodologies is the well-established relationship between the VIs and the basal crop coefficient (Kcb), resulting from the ability of VIs to measure the radiation absorbed by the vegetation, as the main driver of the evapotranspiration process. In addition, VIs have been related with single crop coefficient (Kc), assuming constant rates of soil evaporation. The direct relationship between VIs and ET is conceptually incorrect due to the effect of the atmospheric demand on this relationship. The rising number of Earth Observation Satellites potentiates a data increase to feed the VI-based methodologies for estimating and mapping either the Kc or Kcb, with improved temporal coverage and spatial resolution. The development of operative platforms, including satellite constellations like Sentinels and drones, usable for the assessment of Kcb through VIs, opens new possibilities and challenges. This work analyzes some of the questions that remain inconclusive at scientific and operational level, including: (i) the diversity of the Kcb-VI relationships defined for different crops, (ii) the integration of Kcb-VI relationships in more complex models such as soil water balance, and (iii) the operational application of Kcb-VI relationships using virtual constellations of space and aerial platforms that allow combining data from two or more sensors. © 2020 Elsevier B.V.
2020
Authors
Mananze, S; Pocas, I; Cunha, M;
Publication
Remote Sensing
Abstract
2020
Authors
Mendes, J; Pinho, TM; dos Santos, FN; Sousa, JJ; Peres, E; Boaventura Cunha, J; Cunha, M; Morais, R;
Publication
Agronomy
Abstract
Supervised Thesis
2019
Author
Sosdito Estevão Mananze
Institution
UP-FCUP
2019
Author
Sandra Cristina Pereira Afonso
Institution
UP-FCUP
2019
Author
Mafalda Alexandra Reis Pereira
Institution
UP-FCUP
2019
Author
Joana Soraia Rosas Pereira
Institution
UP-FCUP
2019
Author
Renan Tosin
Institution
UP-FCUP
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