Jacques-André Landry
ResearchProjectsCurriculum vitaeCoursesHome

Brief overview of my research interests:

Jacques-André Landry (Eng, Ph.D.), professor in the Department of Automated Production Engineering at l’Ecole de Technologie Supérieure, is specialized in the application of the artificial intelligence paradigm to the agro environment. His research deals mostly with the development of intelligent control systems as applied to agricultural systems and with the investigation of new techniques from the artificial intelligence research community to the agro environment, such as artificial vision based systems, data mining, evolutionary algorithms and lately environmental modeling.

Since his joining of the Department of Production Engineering, he is particularly interested with the recognition/characterization of irregular objects, with techniques derived from the artificial intelligence field for the characterization and unsupervised classification of variable and irregular objects, such as biological/natural objects. He investigates as well the application of the artificial intelligence paradigms to the agent based modeling of natural ecosystems. His team develops novel approaches for the description of irregular objects or phenomenon.  These new techniques are applied to the classification of biological objects, to the extraction of knowledge from remotely sensed images, and to environmental monitoring and ecosystems studies.

Professor Landry is also interested in the application of hyperspectral imagery to the field of precision agriculture and in the development of algorithmic methods to allow the analysis and data-mining of images obtained by these sensors, most particularly unsupervised blind separation techniques, a field of research in its infantry in precision agriculture.

Recently, he has joined a team of researchers interested in environmental modeling in which he investigates data mining techniques, amongst which approaches based on evolutionary algorithms, to asses ecological integrity and biodiversity of natural ecosystems and to extract knowledge from data to be included in intelligent agents in agent based models.