Dutilleul is one of the first scientists who have used a computed tomography (CT) scanner to study how tree branching affects light interception. "We collect CT scan data, which basically measures of density in 3D, to quantify the complexity of plant branching patterns," he explains. "This will lead to a more complete and accurate model providing a better understanding of why some plants perform better in given light environments. This is important because in the long run, it means less fertilizer application and less greenhouse gas in the atmosphere through enhanced photosynthesis".
Dutilleul and his group are using CT scan data to create 3-D images of plant canopies. After scanning a plant, such as a young cedar, a computer converts the CT scan data into a digital 3-D model. As leaves and branches yield different CT scan data, the leaves can be removed from the digital model. The resulting skeletal images give more detailed and accurate information than the traditional methods of plant characterization. This information can then be used to estimate the amount of light intercepted by the plant.
"Our system and our models will allow us to predict which branching patterns are more efficient at capturing light," said Dutilleul. "This is of obvious importance when choosing which plants to grow in environments with short photoperiod."
Dutilleul has developed links between statistics and life sciences throughout his career. His research in applied statistics at McGill incorporates both temporal (in time) and spatial (in space) components. He applies his statistical methods to agricultural, biological and environmental sciences. As an example, he is curren
Contact: Christine Zeindler