Location: Peanut and Small Grains Research Unit
Title: High-throughput phenotyping of peanut canopy architecture by ground LiDAR sensing technologyAuthor
YUAN, HONGBO - Hebei University | |
WANG, NING - Oklahoma State University | |
Bennett, Rebecca | |
LUO, BIN - National Engineering Research Center For Information Technology In Agriculture |
Submitted to: ASABE Annual International Meeting
Publication Type: Abstract Only Publication Acceptance Date: 8/1/2018 Publication Date: N/A Citation: N/A Interpretive Summary: Technical Abstract: Canopy height, shape, and density are important phenotypic traits that can be used not only as indicators of normal growth but also as parameters for predicting temperature and humidity within the peanut canopy. Within-canopy microclimate is a major factor contributing to disease incidence and severity. At present, physical characteristics of peanut canopies can only be measured manually, which is laborious and time consuming. A study was conducted using a ground LiDAR sensor (SICK-LMS291) and image analysis to measure and analyze peanut canopy characteristics. Experiments were conducted using three peanut cultivars at the Caddo Research Station in Fort Cobb, OK, and LiDAR data were collected monthly from July to September 2015. A program was developed to pre-process the LiDAR data to obtain canopy height; in addition, image analysis algorithms were developed to analyze canopy contour and density. Differences among cultivars were examined using Euler number, entropy, cluster number, and mean area from the image data, and results show that the canopy characteristics of the three peanut cultivars can be clearly distinguished. These results show that ground LiDAR can be used effectively to measure peanut canopy characteristics. This approach may be useful to plant breeders for measuring canopy traits of other crops. |