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About

About

Born at Porto, Portugal, November 7, 1962, graduated with a degree in Electrical Engineering  from the University of Porto in 1986. He then pursued graduate studies at the University of Porto, completing a M.Sc. degree in Electrical Engineering - Systems in 1991 and a Ph.D. degree in Electrical Engineering in 1998. From1986 to 1998 he also worked as an assistant lecturer in the Electrical Engineering Department of the University of Porto. He is currently an Associated Professor in Electrical Engineering, developing his research within the Robotic and Intelligent Systems Centre of INESC TEC (Centre Coordinator). His main research areas are Process Control and Robotics.

Interest
Topics
Details

Details

034
Publications

2023

Special Issue on Advances in Industrial Robotics and Intelligent Systems

Authors
Moreira, AP; Neto, P; Vidal, F;

Publication
APPLIED SCIENCES-BASEL

Abstract
Robotics and intelligent systems are key technologies to promote efficient and innovative applications in the most diverse domains (industry, healthcare, agriculture, construction, mobility, etc [...]

2023

Toward Grapevine Digital Ampelometry Through Vision Deep Learning Models

Authors
Magalhaes, SC; Castro, L; Rodrigues, L; Padilha, TC; de Carvalho, F; dos Santos, FN; Pinho, T; Moreira, G; Cunha, J; Cunha, M; Silva, P; Moreira, AP;

Publication
IEEE SENSORS JOURNAL

Abstract
Several thousand grapevine varieties exist, with even more naming identifiers. Adequate specialized labor is not available for proper classification or identification of grapevines, making the value of commercial vines uncertain. Traditional methods, such as genetic analysis or ampelometry, are time-consuming, expensive, and often require expert skills that are even rarer. New vision-based systems benefit from advanced and innovative technology and can be used by nonexperts in ampelometry. To this end, deep learning (DL) and machine learning (ML) approaches have been successfully applied for classification purposes. This work extends the state of the art by applying digital ampelometry techniques to larger grapevine varieties. We benchmarked MobileNet v2, ResNet-34, and VGG-11-BN DL classifiers to assess their ability for digital ampelography. In our experiment, all the models could identify the vines' varieties through the leaf with a weighted F1 score higher than 92%.

2023

Benchmarking edge computing devices for grape bunches and trunks detection using accelerated object detection single shot multibox deep learning models

Authors
Magalhaes, SC; dos Santos, FN; Machado, P; Moreira, AP; Dias, J;

Publication
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Abstract
Purpose: Visual perception enables robots to perceive the environment. Visual data is processed using computer vision algorithms that are usually time-expensive and require powerful devices to process the visual data in real-time, which is unfeasible for open-field robots with limited energy. This work benchmarks the performance of different heterogeneous platforms for object detection in real-time. This research benchmarks three architectures: embedded GPU-Graphical Processing Units (such as NVIDIA Jetson Nano 2 GB and 4 GB, and NVIDIA Jetson TX2), TPU-Tensor Processing Unit (such as Coral Dev Board TPU), and DPU-Deep Learning Processor Unit (such as in AMD-Xilinx ZCU104 Development Board, and AMD-Xilinx Kria KV260 Starter Kit). Methods: The authors used the RetinaNet ResNet-50 fine-tuned using the natural VineSet dataset. After the trained model was converted and compiled for target-specific hardware formats to improve the execution efficiency.Conclusions and Results: The platforms were assessed in terms of performance of the evaluation metrics and efficiency (time of inference). Graphical Processing Units (GPUs) were the slowest devices, running at 3 FPS to 5 FPS, and Field Programmable Gate Arrays (FPGAs) were the fastest devices, running at 14 FPS to 25 FPS. The efficiency of the Tensor Processing Unit (TPU) is irrelevant and similar to NVIDIA Jetson TX2. TPU and GPU are the most power-efficient, consuming about 5 W. The performance differences, in the evaluation metrics, across devices are irrelevant and have an F1 of about 70 % and mean Average Precision (mAP) of about 60 %.

2023

2D LiDAR-Based System for Canopy Sensing in Smart Spraying Applications

Authors
Baltazar, AR; Dos Santos, FN; De Sousa, ML; Moreira, AP; Cunha, JB;

Publication
IEEE ACCESS

Abstract
The efficient application of phytochemical products in agriculture is a complex issue that demands optimised sprayers and variable rate technologies, which rely on advanced sensing systems to address challenges such as overdosage and product losses. This work developed a system capable of processing different tree canopy parameters to support precision fruit farming and environmental protection using intelligent spraying methodologies. This system is based on a 2D light detection and ranging (LiDAR) sensor and a Global Navigation Satellite System (GNSS) receiver integrated into a sprayer driven by a tractor. The algorithm detects the canopy boundaries, allowing spray only in the presence of vegetation. The spray volume spared evaluates the system's performance compared to a Tree Row Volume (TRV) methodology. The results showed a 28% reduction in the overdosage of spraying product. The second step in this work was calculating and adjusting the amount of liquid to apply based on the tree volume. Considering this parameter, the saving obtained had an average value for the right and left rows of 78%. The volume of the trees was also monitored in a georeferenced manner with the creation of a occupation grid map. This map recorded the trajectory of the sprayer and the detected trees according to their volume.

2023

Special Issue on Advances in Industrial Robotics and Intelligent Systems

Authors
Moreira, AP; Neto, P; Vidal, F;

Publication
Robotics

Abstract
Robotics and intelligent systems are intricately connected, each exploring their respective capabilities and moving towards a common goal [...]

Supervised
thesis

2022

Harvesting with active perception for open-field agricultural robotics

Author
Sandro Augusto Costa Magalhães

Institution
UP-FEUP

2022

Multi-sensor approach for Power Lines Inspection with an Unmanned Aerial Vehicle

Author
Tiago André Miranda dos Santos

Institution
UP-FEUP

2022

Cockpit for High-Level Teleoperation, Monitoring and Task Assignment to a Set of Mobile Robots in Internal Logistics Applications

Author
Diogo Torres Correia

Institution
UP-FEUP

2022

Mapping, Localization and Trajectory Planning for Quadruped Robots in Highly Uneven Terrains

Author
Jorge Filipe Magalhães Ferreira

Institution
UP-FEUP

2022

Design and construction of cost effective VTOL drone for agricultural and forestry application

Author
Ahmad Safaee

Institution
UP-FEUP