Special Session 11

SPECIAL SESSION #11

Marco Fontanelli

University of Pisa, Italy

• marco.fontanelli@unipi.it

Dario Mengoli

University of Bologna, Italy

• dario.mengoli2@unibo.it

Gabriele Costante

University of Perugia, Italy

• gabriele.costante@unipg.it

Mino Sportelli

University of Pisa, Italy

• mino.sportelli@phd.unipi.it

Manuel Pérez-Ruiz

University of Sevilla, Spain

• manuelperez@us.es

Automation and robotics applications are becoming more and more suitable and popular for agricultural, forest, urban green spaces and landscape scenarios. These new technologies allow farmers, operators, practitioners, advisors to save time and improve the efficiency of both field operations and data collection. Different kind of sensors and automatic machines are now available to perform field tasks and collect data within a perspective of integrated, sustainable, regenerative, low-input farming systems, which match the current directives of the new common agricultural policy 2023-27 and the main objectives of the European Green Deal. Moreover, expert, automated and robotic systems are also increasingly used by landscapers, superintendent, and gree nkeepers for smart management of urban, peri-urban and sport green areas in order to reduce noise, pollution, emissions and preserve the biodiversity. The same technologies are also significantly developing in the forest contexts.

This special session welcomes contributions related to advanced robotics for monitoring applications, autonomous agro-forestry process control and other topics, including calibration, planning, robot control, sampling and robot-sensor integration. Furthermore, real time data interpretation/perception, decision systems and applications of autonomous, robotic and precision technologies for agricultural, forest, landscape and green areas field tasks, and data collection are also encouraged.

Topics include, but are not limited to:

• Robots for soil/crop monitoring, yield estimation and phenotyping;
• Robots for prediction, decision making and planning;
• Robotized approaches for continuous day/night operations;
• Robots for sample collection and analysis;
• Robot-sensors integration and calibration;
• Monitoring and mapping with swarm robots;
• Robot localization and autonomous navigation;
• Special aspects and solutions for real time data interpretation/perception;
• Smart Implements;
• Data transfer and processing on board robots;
• Measurement precision needs for real time agro-forestry in-field robotized actions;
• Human-machine interaction and machine-production system interaction for agro-forestry applications;
• Innovations and technology in orchards and open-field crops;
• Innovations and technology in landscape and urban green areas;
• Innovations and technology in forestry.

Marco Fontanelli. Born on April 30, 1979, in Empoli (FI), Italy. From 03/01/2022 Associate Professor at Department of Agriculture, Food and Environment, University of Pisa (UP), Scientific Topic AGR/09, Farm machinery. Master degree in Agricultural Sciences (2004) and PhD in Agro-Forestry Engineering (2009). Current research topics include: the mechanization of organic and conservation farming systems using innovative approaches for soil, cover crops and weed management, in arable crops, orchards and vegetable crops; the use of autonomous mowers to increase turfgrass quality and control weeds and cover crops in vegetables and vineyards and to reduce energy consumption; the trajectories tracking and measurement of the autonomous mowers using specific systems; the use of professional and adjustable battery powered mowers to enhance the turfgrass cutting quality; non-chemical weed and sucker control in vineyard in order to reduce herbicide applications. His university didactical duties include the teaching of courses on Machines for green urban areas and landscape management and Agricultural machinery and farm mechanization.

Dario Mengoli is currently a postdoctoral researcher at the Department of Electrical, Electronic and Information Engineering of the University of Bologna. He received his master’s degree in computer science engineering from the same university in 2008. He then worked as a freelance consultant until 2019, while he was involved in several research activities on robotics and agricultural task automation. Dario’s main research topics include autonomous navigation, prototypes development, machine learning and automation, with particular focus on mobile ground and aerial robotic platforms and software development. He is currently deeply involved in an innovative orchard project, supported by the Ital ian Ministry of Research funding programme to departments of excellence, with the aim of creating a reliable sprayer and mulcher robot to be integrated into a new concept of cultivar for apple production. Dario has also worked with emerging machine learning techniques, on the application of artificial intelligence and image classification algorithms to solve agricultural needs.

Gabriele Costante received the Master degree in 2012 and the Ph.D. degree in information engineering in 2016 from the Department of Engineering, University of Perugia, Italy. He is currently an Associate Professor with the Intelligent Systems, Automation and Robotics Laboratory (ISARLab) at the Department of Engineering of the University of Perugia. In 2014 he has participated as a visiting researcher in the activities of the Robotics and Perception Research Group of the University of Zurich (Zurich, Switzerland). He has co-authored more than 40 scientific papers in international journals and conferences. He is currently member of the Academic Board of the Ph.D. in Industrial and Informat ion Engineering at the Department of Engineering, University of Perugia and lecturer of the courses “Computer Vision and Robot Perception” and “Machine Learning and Data Analysis” within the master degree in Informatics and Robotics Engineering. He has participated and is currently active, also with management responsibilities, in several national and international projects with universities and companies. He is also involved in different editorial activities and scientific committees and is co-founder of the academic spin-off Red Lynx Robotics s.r.l.. His research interests include artificial intelligence, robotics, computer vision, and machine learning with applications in several contexts including healthcare, agriculture, logistic and industry.

Mino Sportelli is a postdoc fellow at the department of Agriculture, Food and Environmental sciences at the University of Pisa (Italy). His academic journey started at New Mexico State University, where worked as a research assistant and developed a solid foundation in sustainable management of turfgrasses and green areas. During the last years he worked on autonomous agricultural machineries for non-chemical weed control in agriculture and urban areas. He received its PhD on June 2022 with a thesis titled ’Innovative Machines for Weed Control in conservation Agriculture’ and he has co-authored more than 20 scientific papers in international journals and conferences. He is also involved in editorial activities for minor journals. Currently he is mainly working for technology transfer within the Horizon project ‘CODECS’. His research interests include digital agriculture, unmanned terrestrial vehicle (UTV), computer vision and machine learning for several application in agriculture and urban contexts.

Manuel Pérez-Ruiz is full professor and director of the master’s degree in Digital Agriculture and Agri-Food Innovation at University of Sevilla (Spain). For more than 18 years he works continuously on research lines with sensors and instrumentation in agricultural machinery, precision agriculture, variable application techniques, analysis of spectral and thermal information, GNSS/RTK technology and intelligent system for weed control. He has authored 45 scientific papers published in SCI scientific journals as well as included in various book chapters. He is a founder of Agrosap and Agroplanning startups. Both companies are very focus on Agriculture 4.0- ensuring connectivity of agricultural equipment.