The use of computer vision tools for image-based plant phenotyping has increased substantially in recent years, paving the way for numerous applications in plant science. Sugar beet represents a valid crop model to study the relationships between plant growth, canopy development and final yield (Milford et al. 1985a; Joalland et al. 2016). In sugar beet, visible imaging showed promising results in discriminating at an early plant developmental stage, between Beet Cyst Nematode (BCN) infested and non-infested plants in the greenhouse (Joalland et al. 2016; Joalland et al. 2017). Here we present a specific example, the development of a leaf segmentation method to count the number of sugar beet leaves and detect stress caused by a plant-parasitic nematode attack on sugar beet roots grown outdoor.
Samuel Joalland, M.P. (2018). Application of a plant phenotyping algorithm to detect stress caused by nematodes. RHIZOSPHERE, 6, 86-88 [10.1016/j.rhisph.2018.05.001].
Application of a plant phenotyping algorithm to detect stress caused by nematodes
Alessandro Bevilacqua;
2018
Abstract
The use of computer vision tools for image-based plant phenotyping has increased substantially in recent years, paving the way for numerous applications in plant science. Sugar beet represents a valid crop model to study the relationships between plant growth, canopy development and final yield (Milford et al. 1985a; Joalland et al. 2016). In sugar beet, visible imaging showed promising results in discriminating at an early plant developmental stage, between Beet Cyst Nematode (BCN) infested and non-infested plants in the greenhouse (Joalland et al. 2016; Joalland et al. 2017). Here we present a specific example, the development of a leaf segmentation method to count the number of sugar beet leaves and detect stress caused by a plant-parasitic nematode attack on sugar beet roots grown outdoor.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
