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ARS Home » Plains Area » El Reno, Oklahoma » Oklahoma and Central Plains Agricultural Research Center » Peanut and Small Grains Research Unit » Research » Publications at this Location » Publication #357646
Research Project: Genetic Improvement of Peanut for Production in the Southwest United States Region

Location: Peanut and Small Grains Research Unit

Title: Development of a peanut canopy measurement system using a ground-based LiDAR sensor

Author
item YUAN, HONGBO - Hebei University
item Bennett, Rebecca
item WANG, NING - Oklahoma State University
item Chamberlin, Kelly

Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/7/2019
Publication Date: 2/28/2019
Citation: Yuan, H., Bennett, R.S., Wang, N., Chamberlin, K.D. 2019. Development of a peanut canopy measurement system using a ground-based LiDAR sensor. Frontiers in Plant Science. 10:203. https://doi.org/10.3389/fpls.2019.00203.
DOI: https://doi.org/10.3389/fpls.2019.00203

Interpretive Summary: Characteristics of a plant's canopy, such as shape, density, and size, affect its ability to resist diseases and compete with weeds. However, fine-scale traits of plant canopies are difficult to measure manually and describe. In this study, a ground-based LiDAR laser sensor was used to characterize peanut canopies. A mobile cart system, which included an RGB camera and LiDAR sensor, was built to scan peanut rows and acquire digital data. Three peanut cultivars were planted at Oklahoma State University's Caddo Research Station in Fort Cobb, OK in May 2015 and data were collected monthly from July to September. The region of interest was obtained from the raw digital data and was reduced to a 2-D image of the canopy contour. Various statistics were calculated from the images to describe the shape and density of the peanut canopies. The three peanut cultivars had unique canopy phenotypes and could be classified using shape features, density, and height and width statistics. This approach should be useful for evaluating peanut lines for canopy characteristics.

Technical Abstract: Canopy architecture contributes significantly to a plant's in-canopy microclimate, affecting incidence and severity of foliar and soilborne diseases, as well as ability to compete with weeds. However, plant canopy architecture is difficult to measure and describe. In this study, a ground-based LiDAR sensor was used to characterize peanut canopies. A multi-sensor system, which included an RGB camera and LiDAR, was built to acquire canopy shape data by a 3-D point cloud. Three peanut cultivars were planted at Oklahoma State University's Caddo Research Station in Fort Cobb, OK in May 2015 and data were collected monthly from July to September. The region of interest was obtained from the 3-D point cloud and was reduced to a 2-D image of the canopy contour. Entropy, cluster count and Euler number were extracted from the image, and these parameters were used to describe the shape of the peanut canopies. Canopy density was analyzed by calculating number of clusters and mean area within the canopy shapes. The three peanut cultivars had unique canopy phenotypes and could be classified using shape features, density, and height and width statistics. This approach should be useful for phenotyping peanut germplasm for canopy architecture.