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      Development of a Peanut Canopy Measurement System Using a Ground-Based LiDAR Sensor

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          Abstract

          Plant architecture characteristics contribute significantly to the microclimate within peanut canopies, affecting weed suppression as well as incidence and severity of foliar and soil-borne diseases. However, plant canopy architecture is difficult to measure and describe quantitatively. In this study, a ground-based LiDAR sensor was used to scan rows of peanut plants in the field, and a data processing and analysis algorithm was developed to extract feature indices to describe the peanut canopy architecture. A data acquisition platform was constructed to carry the ground-based LiDAR and an RGB camera during field tests. An experimental field was established with three peanut cultivars at Oklahoma State University's Caddo Research Station in Fort Cobb, OK in May and the data collections were conducted once each month from July to September 2015. The ground-based LiDAR used for this research was a line-scan laser scanner with a scan-angle of 100°, an angle resolution of 0.25°, and a scanning speed of 53 ms. The collected line-scanned data were processed using the developed image processing algorithm. The canopy height, width, and shape/density were evaluated. Euler number, entropy, cluster count, and mean number of connected objects were extracted from the image and used to describe the shape of the peanut canopies. The three peanut cultivars were then classified using the shape features and indices. A high correlation was also observed between the LiDAR and ground-truth measurements for plant height. This approach should be useful for phenotyping peanut germplasm for canopy architecture.

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          Future scenarios for plant phenotyping.

          With increasing demand to support and accelerate progress in breeding for novel traits, the plant research community faces the need to accurately measure increasingly large numbers of plants and plant parameters. The goal is to provide quantitative analyses of plant structure and function relevant for traits that help plants better adapt to low-input agriculture and resource-limited environments. We provide an overview of the inherently multidisciplinary research in plant phenotyping, focusing on traits that will assist in selecting genotypes with increased resource use efficiency. We highlight opportunities and challenges for integrating noninvasive or minimally invasive technologies into screening protocols to characterize plant responses to environmental challenges for both controlled and field experimentation. Although technology evolves rapidly, parallel efforts are still required because large-scale phenotyping demands accurate reporting of at least a minimum set of information concerning experimental protocols, data management schemas, and integration with modeling. The journey toward systematic plant phenotyping has only just begun.
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            Drought tolerance improvement in crop plants: An integrated view from breeding to genomics

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              A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding

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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                28 February 2019
                2019
                : 10
                : 203
                Affiliations
                [1] 1College of Mechanical and Electrical Engineering, Hebei Agricultural University , Baoding, China
                [2] 2Department of Biosystems and Agricultural Engineering, Oklahoma State University , Stillwater, OK, United States
                [3] 3USDA-ARS, Wheat, Peanuts and Other Field Crops Research Unit , Stillwater, OK, United States
                Author notes

                Edited by: Yanbo Huang, United States Department of Agriculture, United States

                Reviewed by: Yufeng Ge, University of Nebraska-Lincoln, United States; Weiwei Sun, Ningbo University, China; Jingcheng Zhang, Hangzhou Dianzi University, China

                *Correspondence: Ning Wang ning.wang@ 123456okstate.edu

                This article was submitted to Technical Advances in Plant Science, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2019.00203
                6403138
                30873193
                5f7d5f62-d92c-46e3-bcd5-9cb311a9a858
                Copyright © 2019 Yuan, Bennett, Wang and Chamberlin.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 01 October 2018
                : 07 February 2019
                Page count
                Figures: 10, Tables: 5, Equations: 6, References: 54, Pages: 13, Words: 7574
                Funding
                Funded by: U.S. Department of Agriculture 10.13039/100000199
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                peanut cultivar,canopy height and density,image processing,classification,region of interest (roi)

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