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

Abstract

AbstractPlant 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 < .0001) correlations between LiDAR estimated and manually measured plant height data were observed with correlation coefficient (r) of .74 and .91 in chickpea and pea, respectively. Image‐based plant height estimations were also correlated (P < .0001) with manually measurement in the four crops (r = .57 – .98). This study demonstrated that the plant height of four cool‐season crops can be estimated using either proximal or remote sensing techniques even if the canopy architectures of these crops pose challenges. Such high throughput phenotyping technologies can be applied in plant breeding and crop production to monitor plant height and associated traits such as lodging in an efficient and timely manner.