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Sandro Augusto Magalhães
Sandro Augusto Costa Magalhães, Ph.D. born in Porto, Portugal, in 1995. In 2018, he graduated and got his master's degree in Electrical and Computers Engineering - Automation from the University of Porto, Faculty of Engineering (FEUP). His M.Sc. dissertation was entitled Trajectory Control for an Omnidirectional Robot, in the scope of the robot soccer team 5DPO from FEUP/INESC TEC. In 2024, he got a Ph.D. in Electrical and Computer Engineering from FEUP, with his Ph.D. thesis entitled Harvesting with Active Perception for Open-field Agricultural Robotics, funded by FCT through the Ph.D. scholarships program.
During his Ph.D. studies, Sandro Magalhães had an ERASMUS+ internship at Nottingham Trent University (NTU) in the Neuroscience Computing and Cognition Robotics group, researching topics for the optimization of AI algorithms using FPGAs.
He has been a researcher at INESC TEC since 2018 at the TRIBE Lab - Laboratory of Robotics and IoT for Precision Agriculture and Forestry. His main research topics are focused on challenges related to the active perception of fruit and other plant organs in open-field agricultural environments and manipulation strategies for agricultural operations. During his research experience, he has actively participated in various research projects that aim to identify the varieties of Vitis Vinifera using leaves' visual cues, such as the INCAFO project, or to identify unoccluded and occluded fruits under open-field environments, using planar visual cues, for precision monitoring using spectroscopy, in projects such as MetBots, PhenoBot, ROBOCARE and others.
Sandro Magalhães, Ph.D. is an Invited Assistant Professor at the School of Engineering, Institute Polytechnic of Porto (ISEP - IPP), lecturing laboratory classes related to circuit theory topics. He also co-supervised a couple of Master theses and some Bachelor projects.
He has contributed to the literature with more than 30 scientific papers related to the challenges previously introduced and 1 patent.
Publications
3D tomatoes' localisation with monocular cameras using histogram filters
Magalhães, SC;dos Santos, FN;Moreira, AP;Dias, J;
2023
CoRR
Automated Infield Grapevine Inflorescence Segmentation Based on Deep Learning Models
Moreira, G;Magalhães, SA;dos Santos, FN;Cunha, M;
2023
IECAG 2023
Tethered Unmanned Aerial Vehicles-A Systematic Review
Marques, MN;Magalhaes, SA;Dos Santos, FN;Mendonca, HS;
2023
ROBOTICS
Benchmarking Edge Computing Devices for Grape Bunches and Trunks Detection using Accelerated Object Detection Single Shot MultiBox Deep Learning Models
Magalhães, SC;dos Santos, FN;Machado, P;Moreira, AP;Dias, J;
2022
CoRR
