Robust Plant Segmentation from Challenging Background with a Multiband Acquisition and a Supervised Machine Learning Algorithm

摘要

Remote sensing through imaging forms the basis for non-invasive plant phenotyping and has numerous applications in fundamental plant science as well as in agriculture. Plant segmentation is a challenging task especially when the image background reveals difficulties such as the presence of algae and moss or, more generally when the background contains a large colour variability. In this work, we present a method based on the use of multiband images to construct a machine learning model that separates between the plant and its background containing soil and algae/moss. Our experiment shows that we succeed to separate plant parts from the image background, as desired. The method presents improvements as compared to previous methods proposed in the literature especially with data containing a complex background.