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Marcia EndresView profile

Professor

• Dairy Cattle Production
• Animal Welfare
• Dairy Cattle Well-being
• +4 more

Marcia Endres is a Professor and Director of Graduate Studies in the Department of Animal Science at the University of Minnesota, College of Food, Agricultural and Natural Resource Sciences. She is based at Haecker Hall, Saint Paul, MN, and actively contributes to research, teaching, and extension in dairy sciences. Education: Ph.D., University of Minnesota M.S., Iowa State University D.V.M., Universidade Federal do Parana, Brazil Her research centers on dairy cattle well-being, behavior, and management , with a focus on precision dairy farming, robotic milking systems, and welfare assessment. She teaches courses such as ANSC 4093 - Tutorial in Animal Science and ANSC 4604 - Dairy Production Systems, integrating applied research into curriculum. Although specific article titles are not listed, her scholarly output spans refereed articles, book chapters, conference proceedings, extension publications, and research reports, reflecting strong engagement in both academic and outreach domains. Honors and Awards: Outstanding Faculty/Staff Award, Dept. of Animal Science, 2022 American Dairy Science Association DeLaval Dairy Extension Award, 2021 Dean's Distinguished Extension Award, Campus-based Faculty, 2021 Council of Graduate Students Outstanding Advisor of the Year, 2020 President of the Dairy Cattle Welfare Council, 2018–2019 Outstanding Ph.D. Student Award, Department of Animal Science, University of Minnesota, 1993–1994 Graduate School Doctoral Dissertation Fellowship, University of Minnesota, 1993–1994 Marcia Endres is a dedicated mentor and academic leader, recognized for her advising excellence and extension impact. She chairs the Precision Dairy Farming Conference and leads the University of Minnesota Extension Dairy Team. She organizes outreach events including the Summer Dairy Field Days and State Fair 4-H Dairy Workshop, bridging research with practical application in the dairy industry.

• Robotics
• Human-Robot Interaction (HRI)
• Artificial Intelligence
• +3 more

Marc Hanheide is a Professor of Intelligent Robotics and Interactive Systems at the University of Lincoln 's School of Computer Science. With a career spanning EU projects like VAMPIRE, COGNIRON, CogX, and STRANDS, his work focuses on long-term robotic behavior, human-robot spatial interaction, and cognitive system architectures. He has secured over 12 major grants from organizations including EPSRC, BBSRC, and the European Commission. Key Research Areas : Autonomous robotics, HRI, AI, cognitive systems, agricultural robotics Current Projects : STRANDS (long-term behavior), AgriFoRwArdS (robotics training), NCNR (nuclear robotics) Major Contributions : Human-aware navigation modules, topology optimization for robot fleets, causal analysis frameworks Scientific Awards: While no specific awards are listed, his numerous EPSRC grants and leadership in multi-institutional projects highlight his impact. He has over 172 publications and collaborates with institutions like CoR-Lab and CITEC.

Kevin T. TurnerView profile

Professor

• Mechanics
• Materials Science
• Manufacturing
• +6 more

Kevin T. Turner is the John Henry Towne Department Chair and Professor of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania's School of Engineering and Applied Science, with a secondary affiliation in Materials Science and Engineering. He leads the Turner Research Group, which investigates mechanics, materials, and manufacturing challenges, specializing in micro/nano-systems, adhesion, fracture mechanics, and advanced manufacturing. His research focuses on three primary thrusts: Materials with programmable mechanical properties (e.g., electroadhesives for robotics) Fracture and adhesion in structured/heterogeneous materials Printed and flexible sensors (including biodegradable cellulose-based variants) Key projects include tunable adhesion surfaces, architected materials for damage tolerance, and additive manufacturing stress control. Turner's recent publications (2022-2023) demonstrate a strong emphasis on adhesion mechanics, robotics applications, and nanomaterial design. Trends include bio-inspired structures, machine learning optimization, and interdisciplinary approaches bridging mechanics with biomedicine and agriculture. Computational methods like physics-informed neural networks are increasingly utilized for material property analysis. He directs an active research laboratory developing novel sensor technologies and materials systems, collaborating widely across engineering and applied science disciplines.

• Control of Dynamic Systems
• System Identification
• Optimal Estimation and Control
• +3 more

David M. Bevly is an Assistant Professor in the Department of Mechanical Engineering at Auburn University, where he has been on the faculty since 2001. He directs the GPS and Vehicle Dynamics Laboratory (GAVLAB) and teaches a range of courses spanning system dynamics, vehicle dynamics, optimal estimation and control, and GPS navigation. Education Ph.D. Mechanical Engineering, Stanford University, 2001 M.S. Mechanical Engineering, Massachusetts Institute of Technology, 1997 B.S. Mechanical Engineering, Texas A&M University, 1995 Research Interests His research integrates control theory with real-world vehicle applications, focusing on control of dynamic systems, optimal estimation and control, GPS and sensor fusion, vehicle dynamics and control, and vehicle state estimation . Combining low-cost GPS receivers with inertial sensors, he develops algorithms that enable precise state estimation for passenger cars, agricultural tractors, and off-road platforms, enhancing safety and autonomy. Publication Trends Between 1999 and 2004, Bevly authored or co-authored more than a dozen journal and conference papers that collectively advance the fields of GPS-aided vehicle control and precision agriculture. The works explore theoretical contributions—such as dead-reckoning error analysis and yaw-dynamic modeling—and demonstrate practical implementations on farm tractors and high-speed ground vehicles. Awards & Recognition Best Paper of Session, 2000 ION-GPS Meeting SAE Arch T. Colwell Award (Myers Award) for Outstanding Student Paper, 2000 Laboratory & Teams As head of the GPS and Vehicle Dynamics Laboratory (GAVLAB) at Auburn, Bevly leads interdisciplinary projects that integrate mechanical design, real-time embedded systems, and field experimentation. The lab collaborates with industry partners and federal agencies to transition research results into commercial vehicle-safety and agricultural-autonomy products.

Yiannis AmpatzidisView profile

Associate Professor

• Precision Agriculture
• Unmanned Aerial Vehicles (UAVs)
• Artificial Intelligence (AI) in Agriculture
• +7 more

Dr. Yiannis Ampatzidis is an Associate Professor and Precision Agriculture Engineer at the University of Florida's Southwest Florida Research and Education Center (SWFREC). His work focuses on mechanization, automation, and AI-driven technologies for specialty crop production, including UAV applications, sensor systems, and precision irrigation. He leads the Precision Agriculture Engineering program, integrating automation, robotics, and machine vision to enhance crop management and sustainability. Education & Experience: Began as an Assistant Professor at SWFREC in 2017, promoted to Associate Professor. Holds expertise in agricultural engineering, automation, and remote sensing. Research Interests: Includes UAV-based crop monitoring, AI/machine learning for disease detection, smart machinery, and precision nutrient management. His work emphasizes practical applications like autonomous spraying systems and yield prediction models. Publications: Over 50 peer-reviewed papers on UAV technologies, AI in agriculture, and precision farming. Notable contributions include frameworks for citrus disease detection, UAV mission planning, and regulatory guidelines for spraying drones. Awards & Recognition: While no specific awards are listed, his extensive publications and leadership roles highlight his contributions to agricultural innovation. Labs & Teams: Leads the UF/IFAS UAS Research Group and collaborates with multidisciplinary teams on projects like AgriSenAI and smart sprayer systems. Active in developing tools for orchard management and crop yield optimization.

Vasit SaganView profile

Professor

• Geospatial Science
• Remote Sensing
• Photogrammetry
• +4 more

Vasit Sagan is a Professor of Geospatial Science and Computer Science at Saint Louis University's School of Science and Engineering . He directs the Remote Sensing Lab , serves as Deputy Director of the Taylor Geospatial Institute , and holds the role of Associate Vice President for Geospatial Science in the Office of the Vice President for Research and Partnership. Education: Ph.D. from Peking University (2006) Leadership: Director of Remote Sensing Lab; Deputy Director, Taylor Geospatial Institute Research Interests center on geospatial computer vision , integrating remote sensing, photogrammetry, machine learning/AI, and imagery analysis. His work addresses critical challenges in food and water security , ecosystem monitoring , and social instability at scales ranging from local to global. He has secured over $50M in grants as PI/Co-PI and authored 150+ peer-reviewed publications. Recent Publications highlight his interdisciplinary approach, with studies on crop yield prediction via satellite/UAV imagery, disease detection in wheat, urban tree species classification, and deep learning applications for water quality monitoring. These works span agricultural technology , environmental science , and security informatics , emphasizing data fusion and AI-driven geospatial analysis. Scientific Awards include: 2021 Best Paper Award (Remote Sensing) Best Paper Award (International Archives of Photogrammetry) Advising and Grants : He has mentored numerous doctoral, master's, and postdoc researchers, and led major funded projects focused on geospatial AI and environmental sustainability. Labs and Facilities : Remote Sensing Lab at Saint Louis University and collaborative work with the Taylor Geospatial Institute.

Gabriel DallagoView profile

Assistant Professor

• Animal Welfare
• Machine Learning Applications
• Livestock Production Systems
• +3 more

Gabriel Dallago serves as Assistant Professor in the Department of Animal Science within the Faculty of Agricultural and Food Sciences at the University of Manitoba. His research integrates advanced data analytics with livestock production systems to enhance animal welfare and farm decision-making processes. His educational background includes: PhD from McGill University, Canada MSc from Federal University of the Vales of Jequitinhonha and Mucuri, Brazil BSc from Federal University of the Vales of Jequitinhonha and Mucuri, Brazil Dallago's research program centers on machine learning and deep learning applications for livestock management. Key initiatives include developing predictive models for animal bio-responses, integrating multimodal data streams to analyze complex farm systems, and optimizing dairy cow longevity through data-driven interventions. His work bridges computational science with practical agricultural challenges, focusing on tangible improvements in production efficiency and animal well-being. Precision monitoring of dairy cow behavior and welfare Early-life management impacts on herd productivity Computer vision applications for livestock assessment Economic modeling of livestock production systems Analysis of his 15 most recent publications (2022-2025) reveals strong thematic concentration in dairy science (47%), swine production (20%), and animal nutrition (20%), with consistent application of computational methods. Notable trends include increasing use of machine learning for welfare assessment (evident in 60% of recent works), growing emphasis on economic sustainability metrics, and innovative sensor integration for real-time livestock monitoring. Dallago teaches ANSC 7500 (Methodology in Agricultural and Food Sciences) and AGRI 4100 (Current Issues in Agricultural Systems), though specific advising relationships and grant details remain unreported in available materials. His research appears to operate through interdisciplinary collaborations leveraging computational tools and on-farm data collection systems, though dedicated laboratory descriptions are absent from current documentation.

Vinh NguyenView profile

Assistant Professor

• Advanced Manufacturing
• Industry 4.0
• Human-Robot-Machine Interaction
• +5 more

Vinh Nguyen is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Michigan Technological University, where he directs the Michigan Tech Center for AI and coordinates the NIST-PREP program. His research focuses on advanced manufacturing through Industry 4.0, human-robot-machine interaction, and physics-based/data-driven modeling. He has developed solutions for machining, additive manufacturing, metal forming, and robotic assembly to promote smart and sustainable manufacturing. Prior to joining Michigan Tech in 2022, he was a National Research Council Postdoctoral Fellow at NIST (2020–2022). Dr. Nguyen earned his PhD (2020), MS in Mechanical Engineering (2017), and MS in Electrical & Computer Engineering (2017) from Georgia Institute of Technology. He received dual bachelor’s degrees in Electrical and Mechanical Engineering from Rensselaer Polytechnic Institute (2014). His research portfolio spans Advanced Manufacturing Industry 4.0 and 5.0 Human-Robot Interaction Physics-Based/Data-Driven Modeling Industrial Automation based on his lab’s interdisciplinary focus on human-centric, resilient solutions. His recent publications address trends in Machine Learning for Manufacturing Autonomous Vehicle Sensors Hybrid Additive/Subtractive Manufacturing Augmented/Mixed Reality Interfaces Industrial Robot Diagnostics Material-Specific Machining with keywords spanning Robotics, Data Science, and Industrial Engineering.

Baihua LiView profile

Professor

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +9 more

Professor Baihua Li is a Professor of Computer Vision and Machine Learning in the Department of Computer Science at Loughborough University. She is a key member of both the Centre for Sensing and Imaging Science and the Vision, AI, Autonomous and Human Centred Systems Research Group. With over 15 years of research experience, her work spans multiple disciplines including healthcare, sports science, environmental protection, and food manufacturing. Her educational background includes a BSc and MSc in Electronic Engineering from Tianjin University, China (one of China's top 15 universities), followed by a PhD in Computer Science from Aberystwyth University, UK. Prior to joining Loughborough University in 2015, she served as a Lecturer and later Senior Lecturer at Manchester Metropolitan University. Professor Li's research focuses on developing novel AI and machine learning technologies with applications across diverse sectors. Her primary expertise lies in computer vision, pattern recognition, and signal/image processing. She has made significant contributions to human motion tracking and activity recognition, which have been applied in sports performance analysis, healthcare diagnostics, and robotics. Her work emphasizes practical applications that solve real-world problems, from improving football analytics to developing sandwich-making robots. Her research team has developed AI systems for air pollution prediction, upper-limb prosthesis control, autism spectrum condition diagnosis in adults, and retinal disease identification. She has also led projects involving intelligent drone logistics for medical supplies and agricultural robots for precision crop spraying. MSDUK Innovation Challenge Award under category Industry 4.0 in 2019 Made in the Midlands 2023 Food and Drink Award for the Millitec KTP project Professor Li has secured over £1.4 million in external funding in the last three years, with ongoing grants exceeding £1.2 million. She leads a research team comprising 6 Postdoctoral Research Associates and 7+ PhD students. Her projects include AI for sleep breathing disorder diagnosis (£540k), food manufacturing automation (£204k), football action event detection (£359k), air quality monitoring (£470k), and agricultural robotics (£500k). She serves as the Impact Coordinator for her department, emphasizing the transfer of research to industry for societal and economic benefits. She leads an active research team within the Centre for Sensing and Imaging Science and the Vision, AI, Autonomous and Human Centred Systems Research Group. Her laboratory work spans multiple application domains including sports analytics with Statmetrix, food manufacturing automation with Millitec, environmental monitoring through the Triple-Network Air Quality project, and agricultural robotics for precision farming. Her team has developed the i-Gene delta robot that produces 750,000 sandwiches daily for a UK factory, addressing industry skills shortages.

Stefano GregoriView profile

Professor

• Computer Engineering
• Integrated Circuit Design
• Analog and Mixed-Signal Systems
• +7 more

Stefano Gregori is a Professor at the University of Guelph's School of Engineering. He specializes in analog and mixed-signal integrated circuit design, with a focus on low-power systems, sensor networks, and microsystem integration. His research involves collaborations with industry leaders like STMicroelectronics and TSMC, emphasizing practical applications in IoT, energy efficiency, and biomedical devices. He holds a PhD and is a Professional Engineer (PEng). His work bridges theoretical design and real-world applications, such as secure cryptographic circuits and sustainable materials for biomedical composites. He actively supervises graduate students and has mentored numerous scholars, many of whom now work in leading tech companies like Qualcomm and Thales. Gregori's funding sources include NSERC, CMC Microsystems, and the Ontario Centres of Excellence. He emphasizes ethics in engineering, advocating for safety and environmental responsibility. His lab, located in Richards Building Room 3521, focuses on cutting-edge projects like energy-efficient audio amplifiers and blockchain-based IoT security.

• Remote Sensing
• GIS
• Ecology
• +6 more

Dr. Harm Bartholomeus is an Assistant Professor of Remote Sensing at the Department of Geo-information Science and Remote Sensing, Wageningen University & Research. He received his MSc in Physical Geography (2000) and a Geography teaching degree (2001) from Utrecht University. His career includes part-time PhD research (2004–2009) on soil property estimation via spectral measurements and vegetation influence. Since 2009, he has focused on remote sensing techniques like imaging spectroscopy, LiDAR, and UAV-based methods applied to forestry, soil science, and climate studies. In 2012, he co-founded the Wageningen UR Unmanned Aerial Remote Sensing Facility (UARSF), advancing UAV research in environmental studies. His expertise spans Ecology, Geographical Information Systems (GIS), Remote Sensing, Soil Science, Spectroscopy, and 3D analysis. He teaches courses such as Advanced Earth Observation, Remote Sensing and GIS Integration, and MSc thesis supervision in Geo-information Science and Remote Sensing. His work integrates cutting-edge technologies for environmental monitoring, including terrestrial laser scanning and drone-based sensing. Research interests include vegetation-soil interactions, UAV applications in agriculture and ecology, and climate resilience. He collaborates on projects like the UARSF and contributes to global initiatives like the IDEAS-QA4EO network. No scientific awards are explicitly mentioned, but his contributions to UAV and LiDAR methodologies are widely recognized. He advises MSc students on thesis and internship projects in remote sensing and geo-information science.

• Plant Phenotyping
• Precision Agriculture
• Machine Vision
• +6 more

Dr. Huajian Liu is a Grant-Funded Researcher (B) at the University of Adelaide's School of Agriculture, Food and Wine, within the Faculty of Sciences, Engineering and Technology. He is affiliated with The Plant Accelerator, the Adelaide node of the Australian Plant Phenomics Facility. His research focuses on machine vision and machine learning for plant phenotyping and precision agriculture, particularly using hyperspectral imaging for plant nutrient estimation, disease detection, stress tolerance analysis, and pest monitoring. Dr. Liu holds a PhD in machine vision for invertebrate detection from the University of South Australia (2018) and has been a post-doctoral researcher at The Plant Accelerator since then. His expertise spans hyperspectral imaging, bio-inspired machine vision systems, 3D model reconstruction, and optical sensing system design. He is eligible to supervise Masters and PhD students in plant phenotyping and precision agriculture. Key research areas include crop stress detection (drought, salt, nutrient deficiencies), invertebrate pest identification, and automation in agricultural monitoring. Affiliations: The Plant Accelerator, School of Agriculture, Food and Wine Research Themes: Plant phenotyping, precision agriculture, machine learning applications Grants: Ongoing grant-funded research in plant phenomics and hyperspectral technologies Publications highlight innovations in hyperspectral imaging for crop yield prediction, disease diagnostics, and nitrogen content analysis. His work bridges computational methods with agricultural challenges, emphasizing sustainable farming solutions.

• Agronomy
• Sustainable Agriculture
• Crop Production
• +5 more

Reinhard Neugschwandtner is an Associate Professor at the Institute of Agronomy , University of Natural Resources and Life Sciences, Vienna . His research focuses on sustainable crop production, soil science, and precision agriculture in Pannonian climates. Key projects: Agri-photovoltaic systems, digitalization in agriculture, drought-tolerant legumes, autonomous robotics in crop technology Leadership roles: Project leader in 8+ research initiatives since 2014 Research Interests : Dr. Neugschwandtner specializes in: Soil health and earthworm ecology under different tillage systems Nitrogen dynamics in legume-cereal intercropping systems Digital tools for canopy parameter estimation and precision farming Life cycle assessment of agricultural practices Climate-smart crop management strategies Scientific Awards : Awardee of five prestigious honors including: Kardinal-Innitzer-Förderungspreis (2017) Klaus Fischer Innovationspreis für Technik und Umwelt (2016) Walter-Kubiena-Preis (2008) BISi Award (2003) Publications & Presentations : Over 171 publications and 102 presentations focusing on: Long-term tillage experiments Winter crop adaptation Intercropping efficiency Digital agriculture tools Soil nutrient dynamics

Javier Rodriguez SanchezView profile

Research Fellow

• Plant Breeding
• Genetics
• Genomics
• +5 more

Javier Rodriguez Sanchez is a Postdoctoral Associate at the Institute of Plant Breeding, Genetics and Genomics (IPBGG) within the Department of Crop & Soil Sciences at the University of Georgia's College of Agricultural and Environmental Sciences (CAES), based at the Tifton Campus under Dr. Nino Brown's mentorship. His research integrates advanced technologies with plant science to revolutionize agricultural practices, specializing in: Autonomous field phenotyping systems using robotics and terrestrial laser scanning Deep learning applications for 3D/4D crop trait extraction and yield estimation Genetic improvement of cotton and peanut through seedling vigor and morphological trait analysis Spatiotemporal data fusion for real-time crop monitoring and precision agriculture Publications from 2017-2025 reveal a clear trajectory from horticultural robotization toward sophisticated AI-driven phenotyping platforms, with cotton and peanut as primary models. His work consistently bridges computer vision, LiDAR, and machine learning to solve breeding challenges, demonstrating increasing methodological complexity in data acquisition and analysis. No scientific awards were documented in the source materials. As a postdoctoral researcher, Dr. Sanchez operates within Dr. Nino Brown's mentorship structure with no reported advisees or independent grant leadership. His collaborative framework focuses on technology deployment rather than traditional academic advising. He contributes to the IPBGG's mission through the Tifton Campus research facility, which features specialized laboratories and field sites for crop genomics and phenomics. This environment supports his development of mobile robotic platforms for in-field data collection, positioning him at the intersection of agricultural engineering and plant breeding innovation.

Johnny LiView profile

Assistant Professor

• Precision Agriculture
• IoT Sensors
• Autonomous Robotics
• +5 more

Johnny Li is an Assistant Professor in the Department of Soil and Water Systems at the University of Idaho, affiliated with the College of Agricultural & Life Sciences. He leads the Precision Agriculture and Intelligent Robotics Laboratory (PAIR) and is an affiliate professor at the Center for Intelligent Industry Robot (CI2R). Education: PhD in Mechatronics (Central South University, 2012), M.S. in Material Science (Central South University, 2005), B.S. in Agricultural Engineering (Hunan Agricultural University, 2002). Research focuses on IoT/robotics sensing, control systems, and AI integration for precision agriculture, environmental management, and infrastructure monitoring. Key areas include autonomous robotics, remote sensing, crop modeling, and explainable AI for decision support systems. Publications emphasize machine learning applications in disease detection (citrus, tomatoes, grapes), wildfire monitoring, and infrastructure health assessment. Over 40 peer-reviewed papers and $1.5M in grants secured since 2019. Awards include NSF CGCA Panel Fellow (2022) and editorial role at Agricultural Engineering International: CIGR Journal (2013). Labs/Teams: PAIR Lab (founded 2022), CI2R collaboration, interdisciplinary partnerships with soil scientists, engineers, and computer scientists.