El próximo viernes 23 de junio a las 12:30h se llevará a cabo en la sede del CEIGRAM el seminario «Probabilistic olfactory search strategies for the autonomous control of plant diseases», impartido por el Profesor de la ETSIAAB e investigador del Grupo de Sistemas Complejos Carlos Mejia-Monasterio. El seminario se transmitirá también de forma online.
Carlos Mejia-Monasterio completed his studies in Theoretical Physics at the National University of Mexico and obtained his PhD degree 2001. He held several research and postdoctoral positions in Germany, Italy, Switzerland and Finland and since 2010, he is professor of physics at the School of Agricultural, Food and Biosystems Engineering of the Technical University of Madrid, and member of the research group on precision agriculture TAGRALIA. He has written more than 70 scientific publications mainly on nonequilibrium statistical mechanics, transport phenomena, dynamical systems and stochastic dynamics. He has been visiting scientist at the Polytechnic University of Torino, the University of Helsinki, the Sorbonne University and the University of Geneva, and is active member of the National System of Researchers of Mexico, the Royal Spanish Society of Physics, the Italian Society of Statistical Physics and the European Physical Society.
Resumen del seminario:
Plant diseases represent a major economic and environmental problem in agriculture and forestry. Upon infection, a plant develops symptoms that affect different parts of the plant causing a significant agronomic impact. As many such diseases spread in time over the whole crop, a system for early disease detection can aid to mitigate the losses produced by the plant diseases and can further prevent their spread. In recent years, several mathematical algorithms of search have been proposed that could be used as non-invasive, fast, reliable and cost-effective methods to localise in space infectious foci by detecting changes in the profile of volatile organic compounds. Tracking scents and locating odour sources is a major challenge in fundamental and applied physics, on one hand because odour plumes consists of non-uniform intermittent odour patches dispersed by the wind and on the other hand because of the lack of precise and reliable odour sensors. In this seminar I will present some search algorithms that by means of Bayesian inference and reinforcement learning ideas, are able to solve this problem robustly and reliably, and will discuss how these algorithms could be used in agriculture.
Inscripciones a través del siguiente enlace: «Probabilistic olfactory search strategies for the autonomous control of plant diseases»
Antes del 23 de junio se enviará un enlace a quienes se hayan apuntado para seguir el seminario vía online.
