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ARS Home » Plains Area » Stillwater, Oklahoma » Wheat, Peanut, and Other Field Crops Research » Research » Publications at this Location » Publication #339737

Title: Preliminary work in measuring peanut canopy architecture with LiDAR

Author
item PRIETO, CARLA - Technologico De Monterrey
item CONTRERAS, MARIO - Technologico De Monterrey
item MA, JUNCHENG - Chinese Academy Of Agricultural Sciences
item Bennett, Rebecca
item Chamberlin, Kelly
item WANG, NING - Oklahoma State University

Submitted to: American Peanut Research and Education Society Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 7/12/2017
Publication Date: 3/1/2018
Citation: Prieto, C., Contreras, M.A., Ma, J., Bennett, R.S., Chamberlin, K.D., Wang, N. 2018. Preliminary work in measuring peanut canopy architecture with LiDAR [abstract]. In: Proceedings of the American Peanut Research and Education Society, July 11-13, 2017, Albuquerque, NM. 49:153.

Interpretive Summary: Peanuts are susceptible to many diseases, and fungicides account for a significant portion of production costs. Temperature and high humidity, especially within the peanut canopy, are major factors contributing to disease occurrence and severity. Physical characteristics of peanut cultivars, such as density, shape, and height, greatly affect canopy microclimates. However, manual approaches to quantify these physical characteristics are laborious and may be subjective. A preliminary study was conducted using a laser sensor to measure peanut canopies. A field data collection system was developed, and data were collected in 2015 using three cultivars (Georgia-04S, Southwest Runner, and McCloud). Differences among the three cultivars were measurable, and the developed model was able to classify the cultivars with an average accuracy of 89%. This information works toward developing an efficient system for characterizing peanut canopy structure which will be useful to peanut breeders.

Technical Abstract: Peanuts are susceptible to many diseases, and fungicides account for a significant portion of production costs. Temperature and high humidity, especially within the peanut canopy, are major factors contributing to disease incidence and severity. Physical characteristics of peanut cultivars, such as density, shape, and height, greatly affect canopy microclimates. However, manual approaches to quantify these physical characteristics are laborious and may be subjective. A preliminary study was conducted using a ground-based LiDAR sensor to measure the profiles (density, shape, and height) of peanut canopies. A field data collection system was developed, and data were collected in 2015 using three cultivars (Georgia-04S, Southwest Runner, and McCloud). Algorithms to process the line-scan data into images and to analyze the image data were developed. The three cultivars had unique canopy architecture parameters, and the developed model was able to classify the cultivars with an average accuracy of 89%. This information works toward developing a high-throughput system for phenotyping peanut canopy structure which will be useful to peanut breeders.