About Me

Sandro Magalhães is a Ph.D. candidate of Electrical and Computer Engineering at the Faculty of Engineering, University of Porto. He is performing his Ph.D. research at the INESC TEC in the Centre for Industrial Robotics and Intelligent Systems - Laboratory of Robotics and IoT for Smart Precision Agriculture and Forestry. His research interests include robotics, machine learning, computer vision, and active perception in outdoor environments.

Sandro Magalhães also belongs to the 5DPO robotic soccer team from the Faculty of Engineering, University of Porto, and INESC TEC.

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Interests
  • Robotics
  • Artificial Intelligence
  • Perception
Education

  • PhD Electrical and Computers Engineering

    Faculty of Engineering, University of Porto

  • BSc + MEng Electrical and Computers Engineering -- Automation

    Faculty of Engineering, University of Porto

📚 My Research

Use this area to speak to your mission. I’m a research scientist in the Moonshot team at DeepMind. I blog about machine learning, deep learning, and moonshots.

I apply a range of qualitative and quantitative methods to comprehensively investigate the role of science and technology in the economy.

Please reach out to collaborate 😃

Featured Publications
Harvesting with active perception for open-field agricultural robotics
Robotics

Harvesting with active perception for open-field agricultural robotics

The global population is rapidly increasing, leading to significant challenges in hunger and undernourishment. With agricultural land being finite and only marginally expandable, it’s crucial to enhance productivity by adopting precision and intelligent farming techniques. Robotics technology emerges as a key solution to improve crop monitoring and harvesting efficiency. Throughout this thesis, we researched the application of advanced visual perception systems powered by deep learning for identifying fruits and other objects in agricultural scenes. We experimented with various deep learning models, including YOLO and SSD algorithms. These models were optimised on specialised hardware to ensure reliable, near-real-time performance. The models achieved detection F1 scores around 60% for tomatoes and grape bunches, with acceleration techniques boosting detection speeds up to 25 FPS on FPGAs. Further experiments leveraging the FGPAs’ programmable logic enabled us to achieve object detection rates at 6610.94 FPS using a MobileNet v2 classifier. For the estimation of fruits’ 3D positions using monocular cameras, we developed knowledge based algorithms, namely the MonoVisual3DFilter, and the Best Viewpoint Estimator (BVE) + Extended Kalman Filter (EKF). Both approaches yielded accurate results, with estimation errors within the range of 1 cm to 3 cm. We have made the datasets and some of the code developed during this project available to the public, adhering to the european open data principles, via ZENODO and GitLab platforms.

Recent Publications
Sandro Magalhães (2024). Harvesting with active perception for open-field agricultural robotics. Faculty of Engineering, University of Porto.
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Germano Moreira, Sandro Magalhães, Filipe Neves Dos Santos, Mário Cunha (2023). Automated Infield Grapevine Inflorescence Segmentation Based on Deep Learning Models. IECAG 2023.
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Miguel Nakajima Marques, Sandro Magalhães, Filipe Neves Dos Santos, Hélio Sousa Mendonça (2023). Tethered Unmanned Aerial Vehicles—A Systematic Review. Robotics.
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Sandro Magalhães, Luís Castro, Leandro Rodrigues, Tiago Cerveira Padilha, Frederico De Carvalho, Filipe Neves Dos Santos, Tatiana Pinho, Germano Moreira, Jorge Cunha, Mário Cunha, Paulo Silva, António Paulo Moreira (2023). Toward Grapevine Digital Ampelometry Through Vision Deep Learning Models. IEEE Sensors Journal.
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Isabel Pinheiro, Germano Moreira, Daniel Queirós Da Silva, Sandro Magalhães, António Valente, Paulo Moura Oliveira, Mário Cunha, Filipe Santos (2023). Deep Learning YOLO-Based Solution for Grape Bunch Detection and Assessment of Biophysical Lesions. Agronomy.
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