Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
About

About

Graduated and MSc in Agricultural Engineering with a strong emphasis on internationalization. He has been studied at the renowned São Paulo State University Júlio de Mesquita Filho - Campus of Botucatu - Faculty of Agronomic Sciences, Brazil, also at the prestigious The University of Adelaide, Australia where he was honoured with the "Joanne Kanas Memorial Medal". Accomplished his master's thesis at The Faculty of Sciences of The University of Porto (FCUP) in modelling biophysical parameters of Vitis vinifera L., where techniques of remote sensing and machine learning techniques were applied, obtained the highest grade (20/20). Participated in the “WineSpectra” and "VineSpec" projects that involve transference of knowledge between FCUP and companies of the wine sector, which had the main goal of modelling the grapevine water status in the Douro Wine Region, based on remote sensing techniques. He is currently a PhD student in Agricultural Sciences, holder of a FCT scholarship (SFRH / BD / 145182/2019) whose theme involves the application of systems biology in the context of precision agriculture. At the beginning of his PhD, he was selected to participate in the competitive “9th Advanced Training Course on Land Remote Sensing: Agriculture” promoted by the European Space Agency (ESA) hosted at the renowned Université catholique de Louvain, in Louvain-la-Neuve, Belgium, during the period from 16 to 20 September 2019. He is involved in the “Metbots” project, which commits part of his PhD work. He is also an Invited Assistant at FCUP, where he teaches the subject of Applications to Agriculture, from the Master in Remote Sensing, and also agriculture subjects, from the Agricultural Engineering and Landscape Architecture degrees. Developed knowledge about the research process (technology, implementation, and communication of results) both in academic and business context.

Interest
Topics
Details

Details

001
Publications

2021

Assessing predawn leaf water potential based on hyperspectral data and pigment’s concentration of Vitis vinifera L. in the Douro Wine Region

Authors
Tosin, R; Pocas, I; Novo, H; Teixeira, J; Fontes, N; Graca, A; Cunha, M;

Publication
Scientia Horticulturae

Abstract

2020

Toward a generalized predictive model of grapevine water status in Douro region from hyperspectral data

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

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

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.

2019

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

Authors
Tosin, R; Pocas, I; Cunha, M;

Publication
OPEN AGRICULTURE

Abstract
The 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 timing.