High-throughput phenotyping of canopy height in cool-season crops using sensing techniques

Abstract

Plant breeders are interested in plant height data, which is an important agronomic data associated with lodging and mechanical harvest. Manual measurement of plant height with limited samples per plot and data acquisition frequency remains the standard method in breeding programs. To overcome such limitations, this study focuses on plant height estimation in canola (Brassica napus L./winter canola, and B. napus L. and B. rapa L./spring canola), pea (Pisum sativum L.), chickpea (Cicer arietinum L.), and camelina (Camelina sativa L.) breeding trials using sensors. Plant height data were collected using a light detection and ranging (LiDAR) sensor system mounted on a tractor (for pea and chickpea) and an unmanned aerial system (UAS) integrated with a Red–Green–Blue (RGB) camera (for four crops). The LiDAR data and UAS-based images were processed to extract six plant height features. Significant (P