Advanced Drone Technology for Agricultural Applications

Sergio Vélez serves as a Postdoctoral Researcher at Wageningen University & Research, where he specializes in developing innovative remote sensing applications for precision agriculture. After earning his PhD with cum laude honors, he has established himself as an expert in using multispectral imagery and UAV technology for woody crop management. His current work within the Flexigrobots project focuses on developing advanced techniques for disease detection and crop monitoring using UAVs and artificial intelligence. His research has produced significant findings in mapping disease risks in vineyards using UAV multispectral imagery and developing novel methods for estimating canopy volumes in tree crops. With over 15 years of experience in the food production sector across multiple countries including Spain, Argentina, France, and South Africa, he brings a global perspective to precision agriculture research. His recent work includes pioneering studies in real-time crop disease detection, object detection and tracking in precision farming, and the development of high-resolution UAV RGB imagery datasets for agricultural applications. Through his research and international collaborations, he continues to advance the field of precision agriculture, particularly focusing on the integration of UAV technology and artificial intelligence for improved crop management and disease detection.

Harm Bartholomeus serves as Assistant Professor of Remote Sensing at Wageningen University's Laboratory of Geo-information Science and Remote Sensing, where he has established himself as a leading expert in environmental remote sensing applications. After completing his MSc in Physical Geography from Utrecht University in 2000, he earned his PhD from Wageningen University in 2009, focusing on spectroscopic estimation of soil properties. His research combines multiple sensing technologies, including imaging spectroscopy, LiDAR, and UAV-based systems, to advance environmental monitoring in forestry, soil science, and climate studies. In 2012, he co-founded the Wageningen UR Unmanned Aerial Remote Sensing Facility (UARSF), which has become a hub for innovation in UAV-based environmental research. His work has garnered over 8,400 citations, with significant contributions to forest inventory methods using terrestrial LiDAR, development of lightweight hyperspectral mapping systems, and evaluation of UAV-LiDAR systems for measuring forest structure. Through his leadership at UARSF and research initiatives, he continues to advance the application of remote sensing technologies in environmental monitoring, particularly focusing on the integration of multiple sensing modalities for comprehensive ecosystem assessment.

Magdalena Smigaj serves as a postdoctoral researcher at Wageningen University's Laboratory of Geo-information Science and Remote Sensing, where she specializes in multi-modal remote sensing applications for environmental monitoring. After completing her PhD at Newcastle University focusing on forest disease detection using various remote sensing technologies, she has established herself as an expert in UAV-based sensing systems. Her research combines thermal imaging, hyperspectral sensing, and LiDAR technology to monitor vegetation health in both agricultural and forested environments. She actively contributes to interdisciplinary research addressing sustainable development goals, including projects tracking river sand mining activity in Vietnam and South Africa, and improving understanding of water resource availability to reduce food insecurity in South Africa and Ethiopia. Her work has garnered significant attention, with her publications on UAV-based thermal imaging for forest health monitoring and canopy temperature analysis being widely cited in the field. At Wageningen, she continues to advance the application of remote sensing techniques, particularly focusing on developing innovative approaches for vegetation monitoring using drone technology while participating in various international collaborative research projects addressing environmental challenges.

Dr. João Valente currently serves as a Tenured Scientist in UAVs, Robotics & AI at the Spanish National Research Council (CSIC) and heads the Social Artificial Intelligent Drones Lab (Social Drones Lab) at Wageningen University. After receiving his PhD in Robotics and Automation from the Polytechnic University of Madrid in 2014 with cum laude, he has established himself as a leading expert in agricultural robotics and drone technology. His career spans over 15 years in UAVs and field robotics, during which he has authored more than 50 publications and developed innovative solutions for agri-food and ecological management challenges. His research focuses on creating intelligent flying machines that can touch, smell, and see, while making drone technology more accessible to farmers and ecologists. He leads several major research initiatives, including the NWO projects MARS4EARTH and FIELDS2COVER, and the H2020 FLEXIGROBOTS project. Under his leadership, the Social Drones Lab has pioneered concepts like the Cyborg Farmer, presented at the 2020 Dutch Design Week, and developed the popular MOOC "Drones for Agriculture." His team includes 2 postdoctoral researchers, 6 PhD students, and numerous MSc and BSc students, and he has supervised over 20 MSc students across multiple disciplines. His exceptional contributions to the field have been recognized with three awards for his PhD thesis in both academic and industrial sectors, and his work continues to bridge the gap between advanced drone technology and practical agricultural applications.

Dr. Lammert Kooistra has recently been appointed as Professor of Remote Sensing at Wageningen University, advancing from his previous role as associate professor at the Laboratory of Geo-Information Science and Remote Sensing. His research focuses on developing integrated sensing approaches to understand and improve sustainable plant production systems, combining UAV and satellite sensing with active canopy measurements and machine learning. His expertise spans drone technology, hyperspectral sensing systems, and geographical information systems, with particular emphasis on agricultural and environmental applications. He leads the development of advanced multi-sensor systems that can retrieve individual plant traits, from basic measurements like height to complex characteristics such as Sun Induced Fluorescence (SIF). His significant publications include groundbreaking work on UAV-based LiDAR for crop height estimation, automated pest detection using deep learning, and diurnal variation of chlorophyll fluorescence in agricultural crops. As course instructor for "Drones for Agriculture: Prepare and Design Your Drone (UAV) Mission," he shares his expertise in drone technology and remote sensing applications with a global audience. His research has garnered over 10,447 citations, reflecting his significant impact on the field of remote sensing and precision agriculture. At Wageningen University, he continues to advance the integration of multiple sensing technologies while leading research initiatives that combine ecological modeling with cutting-edge remote sensing applications for sustainable agriculture and environmental management.