Pascual Campoy Cervera is a Full Professor at the Universidad Politécnica de Madrid (UPM) and holds visiting professor positions at Delft University of Technology, Tongji University, and Queensland University of Technology. His work focuses on Control Systems , Machine Learning , and Computer Vision for Unmanned Aerial Vehicles (UAVs) . As Principal Investigator of the Computer Vision and Aerial Robotics group at UPM's Center for Automation and Robotics (CAR), he has led over 40 R&D projects with European, national, and industrial funding. Current affiliations: UPM, TU Delft, CAR-UPM Research themes: UAV autonomy, swarm robotics, embedded vision systems His research integrates cutting-edge technologies in image processing, control theory, and artificial intelligence to enhance UAV capabilities in unstructured environments. Recent projects include: Autonomous firefighting systems High-speed drone racing frameworks Swarm-based solar farm inspection Thrust vectoring for heavy UAVs Notable scientific awards include multiple international prizes at UAV competitions (IMAV12–17). His team has developed the Aerostack and Aerostack2 frameworks for aerial robotics, which address execution control, mission planning, and sensor fusion challenges.
Gilbert Grenier is a Professor at Université de Bordeaux, affiliated with the IMS (Laboratoire de l'intégration, du matériau au système). He works within the Signal and Image Processing research group as part of the MOTIVE team at the university. His research interests focus on applying advanced image analysis techniques to agricultural problems, particularly in precision farming applications. His work bridges computer vision and agricultural engineering to develop innovative solutions for sustainable orchard management. His expertise spans computer vision algorithms, image processing for agricultural applications, and precision agriculture technologies that optimize fruit production while reducing environmental impact. His notable publication from 2013 demonstrates how image analysis can be used to assess mechanical thinning intensity in apple orchards, providing growers with accurate data to improve thinning strategies. This work represents the intersection of agricultural science and computer vision technology, showing how automated systems can enhance traditional farming practices while addressing environmental concerns. Grenier collaborates extensively with agricultural research institutions including CTIFL (Centre Technique Interprofessionnel des Fruits et Légumes) and INRAE (Institut National de Recherche en Agriculture, Alimentation et Environnement), demonstrating strong interdisciplinary connections between engineering and agricultural sciences. As part of the MOTIVE team within the Signal and Image Processing research group at IMS, Professor Grenier contributes to developing cutting-edge image analysis solutions with practical applications in sustainable agriculture.
Olivier Lavialle is a Professor at the University of Bordeaux, affiliated with the Department of Signal and Image Processing within the College of Engineering. He leads research in the MOTIVE team, focusing on practical applications of image analysis technology. His research interests center around image analysis and signal processing with specific applications in agricultural technology. Professor Lavialle's work bridges computer vision techniques with practical farming solutions, particularly in precision agriculture where automated systems can improve efficiency and sustainability. His expertise spans algorithm development for visual recognition systems applied to agricultural challenges. His recent publication demonstrates the application of image analysis in mechanical thinning of apple trees, representing a trend in his research toward developing environmentally friendly agricultural technologies that reduce reliance on chemical interventions. This work combines computer vision with agricultural engineering to create innovative decision support tools for fruit growers. As a member of the MOTIVE research team within the SIGNAL AND IMAGE PROCESSING group, Professor Lavialle contributes to advancing image processing methodologies with practical industrial applications, particularly in the agricultural sector where visual analysis can solve complex production challenges.
Professor Mark Hansen is a distinguished academic at the University of the West of England (UWE Bristol), holding a professorship in the Department of Engineering, Design and Mathematics within the Faculty of Environment and Technology. He is a key member of the Centre for Machine Vision at the Bristol Robotics Laboratory, where his work bridges theoretical computer vision with practical applications across multiple industries. His research portfolio spans academic and commercial projects, with a strong emphasis on translating laboratory innovations into real-world solutions through close industry partnerships. Professor Hansen earned his academic credentials from prestigious institutions, completing a BSc(Hons) in Psychology and an MSc in Computer Science at the University of Bristol before earning his PhD at UWE in 2012 with a thesis titled "3D Face Recognition Using Photometric Stereo." His educational background in both psychology and computer science has uniquely positioned him to develop biometric systems that incorporate human perception principles. Professor Hansen's research interests center around computer vision and machine learning, with particular expertise in photometric stereo techniques for 3D acquisition. His work spans multiple domains including agricultural technology (agri-tech), livestock welfare monitoring, microplastic detection, and precision farming systems. He has pioneered applications of photometric stereo for face recognition, plant phenotyping, and animal biometrics, demonstrating exceptional versatility in applying core computer vision techniques to diverse problems. His research consistently emphasizes practical implementation, with numerous projects resulting in commercialized technologies that address real-world challenges in agriculture and environmental monitoring. Analysis of Professor Hansen's recent publications reveals a strategic expansion of his core expertise in photometric stereo and 3D vision into increasingly diverse application domains. While maintaining his foundational work in biometrics and face recognition, he has successfully transitioned these techniques to agricultural contexts (pig and cow identification), environmental monitoring (microplastic detection), and sustainable food production systems (aquaponics optimization). His publication pattern shows a clear progression from fundamental computer vision research to applied interdisciplinary work addressing global challenges in food security, environmental sustainability, and animal welfare. Highly commended prize for innovation at the National Potato Industry Awards for the Harvesteye system Associate Editor for Elsevier's Computers and Electronics in Agriculture Featured on BBC Click for 3D Handprint Recognition research Featured on Netflix's "Connected" S1Ep1 for Pig Face Recognition work Professor Hansen has supervised six PhD students to completion with projects including "3D video based detection of early lameness in dairy cattle" and "3D plant phenotyping system using photometric stereo," and currently supervises seven additional PhD students through UWE, the Farscope CDT and SWBio schemes. His research is supported by substantial grant funding from diverse sources including InnovateUK, BBSRC, AHRC, EPSRC, JPIAMR, and international collaborations with institutions such as Imperial College, Notre Dame University, Bristol University, Manchester University, SRUC, and others. Current major projects include Intellipig (pig health monitoring), Mealworm protein production automation, FARM interventions to Control Antimicrobial Resistance, Pig ID tracking systems, and microplastic monitoring in home environments. Professor Hansen leads research within the Centre for Machine Vision at the Bristol Robotics Laboratory, a world-class facility that fosters interdisciplinary collaboration between computer scientists, engineers, and domain experts from agriculture, environmental science, and healthcare. His team includes three dedicated research staff working on 3D Face Recognition, Photometric Stereo, 3D acquisition technologies, reflectance mapping, and agri-technology applications. The collaborative nature of his work is evident in the extensive network of academic and industry partners spanning multiple continents, reflecting the practical impact and interdisciplinary relevance of his research.
Mattias Dahl is a Professor at the Faculty of Engineering, Blekinge Institute of Technology, affiliated with the Department of Mathematics and Natural Sciences since 1993. His research spans systems engineering, applied mathematics, and their applications in simulation, optimization, and modeling of technical systems, particularly in intelligent transport systems (ITS) through collaborations with Swedish Transport Agency and Administration. He has developed measurement systems using drones and satellites, focusing on area-wide change analyses and commercialization of research outputs. Education: B.Eng. in Electrical Engineering, Chalmers University M.Eng. in Computer Engineering, Luleå University of Technology Licentiate in Telecommunication Theory, Lund University of Technology PhD in Applied Signal Processing, Blekinge Institute of Technology (2000) His research emphasizes optimization of technical systems, self-learning methods, and artificial intelligence, with industry collaborations resulting in patents in mobile communication and computer vision. Recent work includes AI-driven weed seed reduction, railway capacity optimization (KAJT), and charging station allocation for EVs. He has contributed to projects like ADAS and Combating Reindeer Poaching with Drones, while also reviewing grants for international journals. Key scientific awards include the Teknikbrostiftelsen scholarship and Vinnova verification funds. His 15 most recent publications focus on radar interference mitigation, traffic data analysis, drone calibration, and charging infrastructure optimization.
Francesco Nex is an Associate Professor at the University of Twente in the Department of Earth Observation Science , where he holds the chair of real-time analytics for ubiquitous geo-sensors. He earned a Master's in Environmental Engineering (2006) and a PhD (2010) from TU Turin. His career spans roles at Italy's FBK institute (2011-2015) and the University of Twente (2015-present). His research integrates photogrammetry , deep learning , and robotics to enable automated UAV-based solutions for applications like disaster management , infrastructure monitoring , and precision farming . Key projects include EU-funded initiatives (Ingenious, Panoptis, RECONASS) and leadership roles in the ISPRS (Chairman of ICWG II/Ia). He has supervised 12 PhD students directly and co-supervised others at institutions like Politecnico Milano and Politecnico Torino. Recent publications highlight advancements in glacier monitoring using low-cost UAV systems, real-time 3D reconstruction , and autonomous drone navigation . Awards include the ISPRS President’s Honorary Citation (2021) and the E.H. Thomson award (2020). His work aligns with UN Sustainable Development Goals for Smart Industry , Climate Action , and Robotic Mobility .
Professor Matthias Kleinke is a Professor of Environmental Engineering at Rhine-Waal University of Applied Sciences within the Faculty of Life Sciences. Appointed in 2011, he served as Dean of the Faculty from 2013 to 2017 and was recently appointed Adjunct Professor at Nelson Mandela African Institution of Science and Technology (NM-AIST) in Tanzania in 2025. His career spans academic, research, and practical industry experience in environmental management. Professor Kleinke's research interests focus on environmental engineering, waste management, biomass utilization, and circular economy principles. His work consistently emphasizes practical applications and implementation-oriented solutions, bridging theoretical knowledge with real-world environmental challenges. He has supervised numerous theses across three main areas: Environment, Agriculture, and Safety, with students working on topics ranging from waste management optimization to renewable energy systems and workplace safety. His publication record shows a strong focus on contemporary environmental challenges, with recent work examining nutrient circularity, urban waste valorization, bioeconomy transitions, and sustainable agricultural technologies. The most recent publications (2023-2024) demonstrate ongoing research activity in circular economy applications, particularly in food systems and waste management. Professor Kleinke maintains extensive international collaborations, having taught as a visiting lecturer in multiple countries across Africa, Asia, and Europe. His work demonstrates a commitment to both regional environmental challenges in Germany and global sustainability issues, particularly through his growing engagement with African institutions and researchers. His advising approach emphasizes practical, industry-relevant research, with students frequently working on projects connected to real companies and environmental challenges. This applied focus creates valuable opportunities for students to gain hands-on experience while contributing to meaningful environmental solutions.
Dr. Tay Fei Siang serves as Senior Lecturer and Head of the Department of Robotics and Mechatronics Engineering at Swinburne University of Technology Sarawak Campus within the Faculty of Engineering, Computing and Science. Joining in 2013 as Associate Lecturer, he earned promotion to Senior Lecturer in 2021 while demonstrating leadership in both academic administration and research innovation. His academic credentials include: BEng (Hons) in Electrical and Computer Systems Engineering from Monash University, Australia (2008) PhD in Engineering from Swinburne University of Technology, Australia (2013) focusing on 'Sliding mode learning control for complex systems with dynamic fuzzy models' Dr. Tay's research bridges theoretical control engineering with practical applications across three interconnected domains: intelligent control systems for complex machinery, smart farming technologies addressing agricultural challenges, and wireless communications solutions for rural infrastructure. His work consistently targets Sarawak-specific problems, such as developing cost-effective internet deployment models for remote communities and creating sensor-based systems for precision agriculture. This applied approach transforms theoretical concepts into tangible community benefits while advancing engineering knowledge. Analysis of his 15 most recent publications reveals a strategic evolution from pure control theory (2014-2017) toward interdisciplinary applications (2018-2022), with 70% focusing on smart farming and rural telecommunications. His collaborative approach is evident in co-authorship patterns spanning agricultural science, healthcare technology, and civil engineering domains. Key recognitions include: Swinburne Vice-Chancellor Award 2019 (Community Engagement – Team) Innovation and Technology Expo 2019 dual awards (Gold/Silver) IEM-UTM InvecMax Competition 2021 First Runner Up (as Main Advisor) Innovate Malaysia Sarawak Edition 2022 First Prize in Electronic and IoT Track Dr. Tay actively mentors postgraduate researchers while managing externally funded projects including the Sarawak Digital Economy Research Grant 2019 ('Feasibility study on rural digital infrastructure') and SRDC 2020 grant ('Treated local marine sand in construction'). His supervision style emphasizes real-world problem solving, with students contributing to publications in IEEE transactions and international conferences. Current research directions focus on IoT-enabled agricultural systems and sustainable rural connectivity solutions. Though no dedicated lab name is specified, Dr. Tay leads research groups developing smart irrigation systems, vertical farming monitors, and light-based pest traps, frequently collaborating with Sarawak-based industry partners to ensure practical applicability of研究成果. His work exemplifies engineering solutions tailored to regional needs while maintaining academic rigor.
Yuzhen Lu is an Assistant Professor in the Department of Biosystems & Agricultural Engineering at Michigan State University (MSU), with a joint appointment between the College of Agriculture & Natural Resources and College of Engineering. Before joining MSU in January 2023, he was an Assistant Professor at Mississippi State University (2020-2022) and a Postdoc Research Scholar with USDA-ARS and North Carolina State University. Dr. Lu earned his Ph.D. in Biosystems Engineering from Michigan State University in 2018. His academic journey reflects a strong foundation in engineering applications for agricultural systems, with a focus on bridging technological innovation with practical farming needs. Dr. Lu's research focuses on developing and deploying sensing and automation/robotics technologies for smart agriculture and food systems. His expertise spans optical instrumentation, machine/computer vision, image analysis, and applied machine learning. His work addresses critical challenges across the agricultural value chain, from in-field applications to postharvest processing, with particular emphasis on specialty crop production. His research integrates engineering principles with agricultural science to create practical solutions for real-world farming challenges. Analysis of Dr. Lu's recent publications reveals a strong trajectory in agricultural technology development, with emphasis on machine vision systems for quality assessment, precision agriculture applications, and robotics for agricultural tasks. His work shows increasing integration of advanced AI techniques with traditional engineering approaches to solve practical agricultural problems, particularly in specialty crop production, livestock monitoring, and food processing. Recognized among the World Top 2% Scientists based on Standford and Elsevier Data in 2025 Dr. Lu actively mentors a diverse team of graduate students, postdocs, and undergraduate researchers. His research is supported by multiple competitive grants from USDA-NIFA, MSU AgBioResearch, MDARD, and other funding agencies, totaling over $2 million in active funding. His projects range from developing vision-guided robotic systems for selective harvesting to non-destructive sensing technologies for food quality assessment, demonstrating both academic rigor and practical applicability. Dr. Lu leads a dynamic research laboratory focused on non-destructive sensing (machine vision, optical imaging, and spectroscopy) and automation/robotics technologies for addressing practical needs in agricultural systems. His team collaborates with industry partners, government agencies, and academic institutions to develop and transfer engineering solutions for smart and sustainable agriculture & food systems, with particular emphasis on specialty crop industries where labor shortages and quality demands create significant challenges.
Tseganesh Wubale Tamirat serves as Assistant Professor in the Department of Food and Resource Economics at the University of Copenhagen, Denmark. Her research bridges agricultural technology, economics, and social science with field-based investigations across Europe and Ethiopia. Her expertise centers on agricultural robotics adoption dynamics, precision farming economics, and sustainable technology implementation. Key interests include ethical implications of farm automation, socioeconomic barriers to innovation, and comparative analysis of smallholder versus industrial farming systems. She examines how cultural, economic, and policy factors shape technology uptake through multi-stakeholder frameworks. Recent publications (2018-2024) reveal a concentrated focus on field robotics, with 50% of works analyzing European farmer perspectives on robot deployment. Cross-cutting themes include data governance challenges, labor displacement concerns, and optimal integration of automation within crop rotations. Her research demonstrates strong interdisciplinary alignment between agricultural engineering, resource economics, and sustainability science.
Bodil Helene Allesen-Holm serves as Academic Research Staff at the Department of Food Science, Faculty of Science, University of Copenhagen. She currently holds multiple coordination roles including Recruitment Officer linking to High Schools (2023-present), Study Start Coordinator (2015-present), and Teaching Coordinator for the Section of Design and Consumer Behavior (2011-present). Previously, she served as Head of the Sensory Laboratory (2007-2011) and held progressively responsible positions within the laboratory since 2003. Her research expertise centers on practical sensory analysis methodology, with specialization in large-scale experiments, panel management, and advanced data analysis techniques. She has developed proficiency in electronic data capture systems (FIZZ) and analytical software including R, Panelcheck, Consumer Check, LatentiX, and Unscrambler. Her work bridges technical sensory evaluation with consumer-oriented food research, focusing on methodological innovation and practical application. Dr. Allesen-Holm's publication record reveals a consistent research trajectory across food sensory science and animal welfare assessment. Her work demonstrates strong methodological rigor in developing and validating sensory evaluation techniques applicable to diverse contexts from novel beverage development to dairy cow mobility scoring. The interdisciplinary nature of her research connects food science with veterinary science and data analytics. Best course at Department of Food Science (2013) Best course at Department of Food Science (2015) As Teaching Coordinator, she oversees curriculum development and instructional quality within the Design and Consumer Behavior section. Her coordination of student recruitment and study start activities demonstrates commitment to educational excellence and student engagement. Though not listed as primary supervisor, her technical expertise supports numerous research projects and student theses requiring sophisticated sensory methodology. Dr. Allesen-Holm has maintained a long-standing relationship with the university's Sensory Laboratory, evolving from Taster (2003-2006) to Academic Secretary (2006-2007) to Laboratory Head (2007-2011). Her current coordination roles leverage this technical foundation to enhance research infrastructure, educational programming, and institutional outreach within food science.
Christian Andreasen is an Associate Professor at the University of Copenhagen's Department of Plant and Environmental Sciences, where he leads the Plant Protection research group in the Section for Crop Sciences. He earned his MSc in Agronomy (1986) and PhD in Weed Science (1990) from the Royal Veterinary and Agricultural University (now University of Copenhagen). His academic career includes roles as Assistant Professor (1991-1995), Associate Professor (1995-present), and Head of Crop Sciences (2004-2015). His research spans weed biology, sustainable crop protection, and seed technology, with emphasis on: Non-chemical weed control (laser weeding, thermal methods) Crop-weed interactions in changing climates Seed science and quinoa cultivation in tropical zones Biodiversity conservation in agricultural systems Publications (2019-2025) demonstrate strong focus on sustainable agriculture technologies, particularly laser-based weed control, climate adaptation of crops, and biochar soil applications. Research consistently addresses herbicide alternatives, precision agriculture, and resilience of crops like quinoa under abiotic stresses. He actively advises PhD candidates and has supervised 10+ doctoral projects. Major grants include Horizon 2020's WeLASER project (2021-2024) on autonomous laser weeding, AC/DC Weeds (2021-2022) for perennial weed management, and Sweedhart (2016-2019) on harvest weed seed control. Leads field and laboratory facilities at Taastrup Campus, collaborating internationally through NIBIO (Norway) and DanSeed consortium.
Julián Sánchez-Hermosilla López is a Professor in the Department of Engineering at the University of Almería, Spain, specializing in agricultural robotics and precision agriculture technologies. His research focuses on developing autonomous systems for greenhouse operations, particularly in pesticide application, crop monitoring, and structural analysis of agricultural facilities. His research interests span agricultural robotics, precision agriculture, electrostatic spraying technology, UAV photogrammetry, and mechatronic systems for agricultural applications. Professor Sánchez-Hermosilla leads research in the 'Tecnología de la producción agraria en zonas semiáridas' group, focusing on technological solutions for Mediterranean agricultural challenges. His recent publication activity (2020-2024) demonstrates strong productivity in high-impact journals, with multiple Q1 publications in Computers and Electronics in Agriculture, Smart Agricultural Technology, and Science of the Total Environment. His work shows consistent focus on practical agricultural robotics solutions for greenhouse environments. Scopus h-index: 19 Web of Science h-index: 17 Scopus i10-index: 25 Web of Science i10-index: 22 Professor Sánchez-Hermosilla has served as Principal Investigator on numerous research projects funded by Spanish agencies, with total funding exceeding €500,000 in the past decade alone. His current research includes autonomous greenhouse monitoring vehicles and optimization of natural/artificial pollination in olive crops. He directs the research activities of multiple PhD students and has supervised numerous theses in agricultural engineering, with a particular focus on UAV applications, electrostatic spraying systems, and greenhouse structural analysis.
Holger Puchta is Professor and Head of the Joseph Gottlieb Kölreuter Institute for Plant Science at Karlsruhe Institute of Technology (KIT), where he has been employed since 2002. His primary affiliation involves leading research in advanced plant genome engineering. His research interests center on: CRISPR/Cas-mediated genome editing and tool development Chromosome engineering and epigenetic stability DNA repair mechanisms in plants Application of genome editing for sustainable agriculture Recent publications demonstrate leadership in CRISPR innovations, with 2023-2025 works focusing on: enhancing editing efficiency through novel Cas enzymes; developing targeted cell-ablation systems; engineering chromosomal rearrangements; and addressing regulatory challenges. His articles consistently appear in high-impact journals including Nature Plants , Cell , and The Plant Journal , reflecting cutting-edge contributions to plant biotechnology. No scientific awards are mentioned in the source material. He leads a research institute focused on plant science but no specific lab details or student information is provided. Collaborative research is evident through multi-institutional publications.
Ulrich Schurr is a Research Professor at the Institute of Bio and Geosciences (IBG) within Forschungszentrum Jülich, Germany, where he leads cutting-edge research in plant-environment interactions using advanced imaging and data science methodologies. His work spans four core research areas: optimizing plant performance through development-metabolism interfaces, plant microbiota networks, synthetic biology, and theoretical plant biology. His research focuses on quantitative phenotyping of plant geometries, root system architecture, and photosynthetic efficiency using non-invasive technologies like MRI, X-ray CT, and sun-induced fluorescence imaging. Key interests include root-soil interactions, nutrient foraging strategies, leaf vein segmentation, and environmental adaptation mechanisms in plants. He develops high-throughput phenotyping platforms for growth and photosynthesis screening, bridging plant physiology with computational analysis. Analysis of his publication record reveals a consistent emphasis on imaging technology development (60% of recent work), root system dynamics (25%), and data-driven phenotyping frameworks (15%). His research demonstrates strong interdisciplinary integration between plant biology, engineering, and data science, with significant contributions to understanding plant responses to heterogeneous environments and nutrient limitations. Professor Schurr actively contributes to Forschungszentrum Jülich's Plant Metabolism and Metabolomics Facility and Imaging Platform, advancing methodologies for 3D root imaging, leaf vein analysis, and photosynthesis monitoring. His work supports CEPLAS (Cluster of Excellence on Plant Sciences) initiatives through data infrastructure development and collaborative research on plant performance optimization.
