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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 #364120
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 ground-based peanut canopy phenotyping system

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

Submitted to: International Federation of Automatic Control (IFAC) Symposium
Publication Type: Proceedings
Publication Acceptance Date: 8/12/2018
Publication Date: 9/12/2018
Citation: Yuan, H., Wang, N., Bennett, R.S., Burditt, D., Cannon, A., Chamberlin, K.D. 2018. Development of a ground-based peanut canopy phenotyping system. In: Zhang, M. (ed.) Proceedings of the International Federation of Automatic Control (IFAC) Conference on Bio-Robotics. 51(17):162-165. https://doi.org/10.1016/j.ifacol.2018.08.081.
DOI: https://doi.org/10.1016/j.ifacol.2018.08.081

Interpretive Summary:

Technical Abstract: Phenotypic information of peanut canopy, including height, width, shape, and density are important in the selection of the best cultivars of peanuts. However, current methods to acquire these data are mainly by manual measurements or qualitative scorings. These methods are laborious, time-consuming, and subjective. In this study, a ground-based peanut canopy phenotypic system was developed to improve the efficiency and accuracy of the data collection on peanut canopy architecture. The system was on a ground-based, remote controlled cart with a sensor suite of two RGB cameras, a thermal camera, a laser scanner and an RTK GPS. Software programs was developed to control the system and collect, store, and analyze the data. This system was tested in the peanut growth season of 2017. The result showed that the system was able to complete the data collection at least four times faster than previous manual collection. The data collected were with a much higher resolution, thus could be used to acquire detailed features of peanut canopy.