
About Project
Ferramenta avançada para operacionalização da agricultura de precisão
Acronym
SmartFarming
Responsible
Filipe Baptista Neves dos Santos
Status
Closed
Start
January 1, 2016
End
January 30, 2018
Effective End
January 30, 2018
Global Budget
€997,605.00
Financing
€133,041.00
Members
Team Leaders

Filipe Neves Santos
Filipe Neves dos Santos was born in São Paio de Oleiros, Portugal, in 1979. He olds a Licenciatura (5-year degree) in Electrical and Computer Engineering in 2003 from Instituto Superior de Engenharia do Porto (ISEP), a M.Sc. in Electrical and Computer Engineering from the Instituto Superior Técnico (IST) da Universidade Técnica de Lisboa, in 2007, and received the PhD degree in Electrical and Computer Engineering at the Faculdade de Engenharia (FEUP), Universidade do Porto, Portugal, in 2014. His professional passion is to develop autonomous robots and machinery to solve real problems, desires and needs of our society and to contribute for self-sustainability and fairness of the global economy. Actually, He is focused in developing and researching robotic solutions for agriculture and forestry sector, where is required a higher efficiency for our world self-sustainability. Considering his closer regional reality, he have setup the goal to promote agricultural robotic based projects and develop robots that can operate fully autonomously and safely in steep-slope scenarios, which is a common reality of North of Portugal and in other large number of world regions. As so, he is interested in explore and develop robots for specific agricultural and forestall tasks such as: monitoring (by ground), spraying, logistics, pruning, and selective harvesting. The successfully execution of these task is largely dependent on the robustness of specific robotic systems, such as: - Visual Perception; - Navigation (localization, mapping and path planning); and - Manipulation and end tools. For that reason Visual Perception and Navigation are his main research fields inside of robotics research. His formation in Electronics and Computer Engineer (Bachelor (old-one of 5 years) MSc (sensor fusion), PhD (semantic mapping) ), experience of 4 years as entrepreneur (technological startup), 8 year as robotics researcher, 5 years as manager (in supporting tasks in a family enterprise), and 6 year as electronics technician will help him to successfully contribute for the agricultural and forestall robotics future.
Carlos Ferreira
Associated Centres
Robotics in Industry and Intelligent Systems
At the Centre for Robotics and Intelligent Systems, we develop innovative solutions to leverage robotics in the industrial, agricultural, and forestry contexts, driving the digital transformation of the industry. We take a practical approach - from design to deployment - to test the navigation and localisation of mobile robots, explore advances in 2D/3D industrial vision and advanced detection, while also focusing on industrial and collaborative robotics, as well as human-robot interfaces. Our TRIBE LAB is fertile ground for innovative ideas about the agriculture of the future; we develop prototypes and promote excellence in agricultural robotics and IoT technology: with prototypes, advanced sensors (LiDAR, AI cameras), and rapid prototyping tools, we accelerate the development of solutions for the agroforestry sector. We are also present at the iiLab, where we combine applied research, technological demonstration, and controlled environment testing, promoting the integration of emerging technologies into industry. From intelligent robotic cells and cyber-physical systems to data analysis and AI, it is an innovation space where companies can experiment with and validate solutions for the factory of the future. With a multidisciplinary team, and following European agendas, our research work combines fundamental science and application, impacting the design of solutions for Industry 4.0, fostering competitiveness and the digital transformation of the sector.

Artificial Intelligence and Decision Support
Our Laboratory of Artificial Intelligence and Decision Support (LIAAD) conducts research in the fields of Artificial Intelligence, Machine Learning, Data Science, and Modelling. These areas are cross-cutting and apply to all sectors of society and the economy. The vast amounts of data being collected, alongside the ubiquity of digitalisation and sensorisation, are increasingly creating opportunities and challenges for automating decision support. The combination of Machine Learning and complex models is transforming the economy, healthcare, justice, industry, science, public administration, and education. This encourages us to invest in diverse technological and scientific approaches and perspectives. Our overarching strategy is to explore the flow and diversification of data, and to invest in research lines that will lead to the development of applied Artificial Intelligence foundations and models that are responsible and human centred.
