Joaquim João Sousa
Professor Auxiliar com Agregação da Universidade de Trás-os-Montes e Alto Douro (UTAD) e doutorado em Ciências da Engenharia Geográfica, pela Universidade do Porto e pela Universidade de Delft (Holanda), tendo apresenta a tese “Potential of integrating PSInSAR Methodologies in the Detection of Surface Deformation”. Atualmente, é Investigador (membro integrado) do Centre for Robotics in Industry and Intelligent Systems (CRISS), do INESC TEC/Polo UTAD, e investigador (colaborador) do CITAB (Centre for the Research and Technology of Agro-Environmental and Biological Sciences). Nos últimos anos tem-se dedicado, sobretudo, à utilização de Veículos Aéreos Não Tripulados (UAV) para aplicações agroflorestais. Utiliza imagens aéreas de elevada resolução, obtidas por diferentes sensores (RGB, NIR, Multiespectrais, Hiperespectrais e Térmicos) para, usando técnicas de processamento de imagem e desenvolvimento de algoritmos, extrair informações e parâmetros relevantes, sobretudo, na vinha, soutos e olivais. Estas técnicas são, no entanto, extensíveis à deteção e monitorização de grande parte das espécies arbóreas, que integram as nossas florestas, e de vegetação rasteira. É autor de várias publicações em revistas internacionais da especialidade do Remote Sensing. Participa em vários projetos de investigação, destacando-se o PARRA (Plataforma integrAda de monitoRização e avaliação da doença da flavescência douRada na vinha), em que é líder por parte da UTAD (SI I&DT, aviso Nº 08/SI/2015, Projeto em Co-Promoção, parceiros do projeto: TEKEVER ASDS - empresa líder, UTAD, Instituto Politécnico de Viana do Castelo, INIAV, Agrociência. Montante total atribuído 1.602.245,58€) e é membro do projeto Plataforma de Inovação da Vinha e do Vinho, linha Remote sensing and detection of grapevine diseases (Projeto I&DT pelo Norte2020, com um financiamento global de 4.500.000,00 €).
Projects
Fasten
Industry 4.0 has now extended its focus to a broader set of technologies rather than just CPS, and to the most vital processes included in the product and production systems lifecycle, rather than just to production. In all the dialects where the Industry 4.0 language is spoken, Industrial Internet of Things, Additive Manufacturing and Robotics from the technology side and Mass Customization, Product-Service Systems and Sustainable Manufacturing from the business side always represent key cornerstones and top priority challenges. FASTEN “mission” is to develop, demonstrate, validate, and disseminate an integrated and modular framework for efficiently producing custom-designed products. More specifically, FASTEN will demonstrate an open and standardized framework to produce and deliver tailored-designed products, capable to run autonomously and deliver fast and low cost additive manufactured products. This will be achieved by effectively pairing digital integrated service/products to additive manufacturing processes, on top of tools for decentralizing decision-making and data interchange. Sophisticated software technologies for self-learning, self-optimizing, and advanced control will be applied to build a full connected additive manufacturing system. ThyssenKrupp and Embraer are two of these companies that must overcome challenges of this nature, in order to cope with an increasing demand diversity, products with shorter life cycles, and the need for supplying low volumes per order, requiring flexible solutions capable to effectively manufacture and deliver personalized products.
ROBOCARE
Research and development of modular robotic technology for the introduction of advanced agronomic practices, reducing the reduction of labor burden and the increase in the ergonomics of the operations carried out and the consequent increase in labor productivity and economic profitability of crops.
Publications
Empowering intermediate cities: cost-effective heritage preservation through satellite remote sensing and deep learning
Rodríguez Antuñano, I;Sousa, JJ;Bakon, M;Ruiz Armenteros, AM;Martínez Sánchez, J;Riveiro, B;
2024
INTERNATIONAL JOURNAL OF REMOTE SENSING
Detection of Leak Areas in Vineyard Irrigation Systems Using UAV-Based Data
Pádua, L;Marques, P;Dinis, LT;Moutinho Pereira, J;Sousa, JJ;Morais, R;Peres, E;
2024
DRONES
Classification of Grapevine Varieties Using UAV Hyperspectral Imaging
López, A;Ogayar, CJ;Feito, FR;Sousa, JJ;
2024
REMOTE SENSING
Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
Guimaraes, N;Fraga, H;Sousa, JJ;Pádua, L;Bento, A;Couto, P;
2024
AGRIENGINEERING
Supervised theses
Real-time, Power-efficient Hardware acceleration of deep learning applications in Embedded Reconfigurable Devices for Advanced Driving Assistance Systems
Amir Hossein Farzamiyan
D - 2024
UP-FEUP