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About

Ricardo Braga holds the following academic degrees: Bachelor degree in Agronomy / Agricultural Engineering (1993) from Instituto Superior de Agronomia / University of Lisbon, Portugal; Master of Science in Tropical Agricultural Production (1996) from Instituto Superior de Agronomia / University of Lisbon, Portugal; Doctor of Philosophy in Agricultural Operations Management (2000) from University of Florida, USA. He currently is Assistant Professor (2013 - ) at Instituto Superior de Agronomia, University of Lisbon, where he teaches courses at the Bachelor and Master levels in general agriculture and machinery, precision agriculture and modeling of agricultural systems. He has participated in many projects in the areas of precision agriculture, crop management optimization, and technology dissemination and adoption.

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Publications

2022

Overcoming the challenge of bunch occlusion by leaves for vineyard yield estimation using image analysis

Authors
Victorino, G; Braga, RP; Santos-Victor, J; Lopes, CM;

Publication
OENO One

Abstract
Accurate yield estimation is of utmost importance for the entire grape and wine production chain, yet it remains an extremely challenging process due to high spatial and temporal variability in vineyards. Recent research has focused on using image analysis for vineyard yield estimation, with one of the major obstacles being the high degree of occlusion of bunches by leaves. This work uses canopy features obtained from 2D images (canopy porosity and visible bunch area) as proxies for estimating the proportion of occluded bunches by leaves to enable automatic yield estimation on non-disturbed canopies. Data was collected from three grapevine varieties, and images were captured from 1 m segments at two phenological stages (veraison and full maturation) in non-defoliated and partially defoliated vines. Visible bunches (bunch exposure; BE) varied between 16 and 64 %. This percentage was estimated using a multiple regression model that includes canopy porosity and visible bunch area as predictors, yielding a R2 between 0.70 and 0.84 on a training set composed of 70 % of all data, showing an explanatory power 10 to 43 % higher than when using the predictors individually. A model based on the combined data set (all varieties and phenological stages) was selected for BE estimation, achieving a R2 = 0.80 on the validation set. This model did not show validation metrics differences when applied on data collected at veraison or full maturation, suggesting that BE can be accurately estimated at any stage. Bunch exposure was then used to estimate total bunch area (tBA), showing low errors (< 10 %) except for the variety Arinto, which presents specific morphological traits such as large leaves and bunches. Finally, yield estimation computed from estimated tBA presented a very low error (0.2 %) on the validation data set with pooled data. However, when performed on every single variety, the simplified approach of area-to-mass conversion was less accurate for the variety Syrah. The method demonstrated in this work is an important step towards a fully automated non-invasive yield estimation approach, as it offers a solution to estimate bunches that are not visible to imaging sensors.

2021

A Simple Application for Computing Reference Evapotranspiration with Various Levels of Data Availability-ETo Tool

Authors
Rodrigues, GC; Braga, RP;

Publication
AGRONOMY-BASEL

Abstract
Reference evapotranspiration (ETo) estimations may be used to improve the efficiency of irrigated agriculture. However, its computation can be complex and could require numerous weather data that are not always available for many locations. Different methods are available to estimate ETo when limited data are available, and the assessment of the most accurate one can be difficult and time consuming. There are some standalone softwares available for computing ETo but none of them allow for the comparison of different methods for the same or different datasets simultaneously. This paper aims to present an application for estimating ETo using several methods that require different levels of data availability, namely FAO-56 Penman-Monteith (PM), the Original and the three modified Hargreaves-Samani (HS and MHS1, MHS2 and MHS3), Trajkovic (TR) and the single temperature procedure (MaxTET). Also, it facilitates the comparison of the accuracy estimation of two selected methods. From an example case, for where the application was used to compute ETo for three different locations, results show that the application can easily and successfully estimate ETo using the proposed methods, allowing for statistical comparison of those estimations. HS proves to be the most accurate method for the studied locations; however, the accuracy of all methods tends to be lower for costal locations than for more continental sites. With this application, users can select the best ETo estimation methods for a specific location and use it for irrigation purposes.

2021

A Simple Procedure to Estimate Reference Evapotranspiration during the Irrigation Season in a Hot-Summer Mediterranean Climate

Authors
Rodrigues, GC; Braga, RP;

Publication
SUSTAINABILITY

Abstract
The Food and Agricultural Organization of the United Nations (FAO) Penman-Monteith (PM) method is widely regarded as the most effective reference evapotranspiration (ETo) estimator; however, it requires a wide range of data that may be scarce in some rural regions. When feasible relative humidity, solar radiation and wind speed data are unavailable, a temperature-based method may be useful to estimate ETo and provide suitable data to support irrigation management. This study has evaluated the accuracy of two ETo estimations methods: (1) a locally and monthly adjusted Hargreaves-Samani (HS) equation; (2) a simple procedure that only uses maximum temperature and a temperature adjustment coefficient (MaxTET). Results show that, if a monthly adjusted radiation adjustment coefficient (k(Rs)) is calibrated for each site, acceptable ETo estimations (RMSE and R-2 equal to 0.79 for the entire region) can be achieved. Results also show that a procedure to estimate ETo based only on maximum temperature performs acceptably, when compared with ETo estimation using PM equation (RMSE = 0.83 mm day(-1) and R-2 = 0.77 for Alentejo). When comparing these results with the ones attained when adopting a monthly adjusted HS method, the MaxTET procedure proves to be an accurate ETo estimator. Results also show that both methods can be used to estimate ETo when weather data are scarce.

2021

Variable-rate mechanical pruning: a new way to prune vines

Authors
Botelho, M; Cruz, A; Mourato, C; Castelo-Branco, J; Ricardo-da-Silva, J; Castro, R; Ribeiro, H; Braga, R;

Publication
Acta Horticulturae

Abstract

2021

Estimation of Daily Reference Evapotranspiration from NASA POWER Reanalysis Products in a Hot Summer Mediterranean Climate

Authors
Rodrigues, GC; Braga, RP;

Publication
AGRONOMY-BASEL

Abstract
This study aims at assessing the accuracy of estimating daily reference evapotranspiration (ETo) computed with NASA POWER reanalysis products. Daily ETo estimated from local observations of weather variables in 14 weather stations distributed across Alentejo Region, Southern Portugal were compared with ETo derived from NASA POWER weather data, using raw and bias-corrected datasets. Three different methods were used to compute ETo: (a) FAO Penman-Monteith (PM); (b) Hargreaves-Samani (HS); and (c) MaxTET. Results show that, when using raw NASA POWER datasets, a good accuracy between the observed ETo and reanalysis ETo was observed in most locations (R-2 > 0.70). PM shows a tendency to over-estimating ETo with an RMSE as high as 1.41 mm d(-1), while using a temperature-based ET estimation method, an RMSE lower than 0.92 mm d(-1) is obtained. If a local bias correction is adopted, the temperature-based methods show a small over or underestimation of ETo (-0.40 mm d(-1) & LE; MBE < 0.40 mm d(-1)). As for PM, ETo is still underestimated for 13 locations (MBE < 0 mm d(-1)) but with an RMSE never higher than 0.77 mm d(-1). When NASA POWER raw data is used to estimate ETo, HS_Rs proved the most accurate method, providing the lowest RMSE for half the locations. However, if a data regional bias correction is used, PM leads to the most accurate ETo estimation for half the locations; also, when a local bias correction is performed, PM proved the be the most accurate ETo estimation method for most locations. Nonetheless, MaxTET proved to be an accurate method; its simplicity may prove to be successful not only when only maximum temperature data is available but also due to the low data required for ETo estimation.