3D Robotic System Development for High-throughput Crop Phenotyping

被引:9
|
作者
Zhang, Chongyuan [1 ]
Gao, Honghong [1 ,2 ]
Zhou, Jianfeng [1 ]
Cousins, Asaph [3 ]
Pumphrey, Michael O. [4 ]
Sankaran, Sindhuja [1 ]
机构
[1] Washington State Univ, Dept Biol Syst Engn, Pullman, WA 99164 USA
[2] Xian Technol Univ, Xian, Shaanxi, Peoples R China
[3] Washington State Univ, Sch Biol Sci, Pullman, WA 99164 USA
[4] Washington State Univ, Dept Crop & Soil Sci, Pullman, WA 99164 USA
来源
IFAC PAPERSONLINE | 2016年 / 49卷 / 16期
关键词
Automation; Crop monitoring; Data acquisition platform; Plant breeding; WINTER-WHEAT; PLANT; DEFICIENCY;
D O I
10.1016/j.ifacol.2016.10.045
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Plant breeding programs are working towards developing new high-yielding crop varieties to accommodate the increasing demand for food. However, high throughput phenotyping remains to be the bottleneck that is currently limiting the complete breeding potential. In this project, a 3D robotic system was developed to conduct automated high-throughput phenotyping in cereal crops. The 3D robotic phenotyping system consisted of an aluminum framework to support a 3D sliding system (sliders and tracks), which allows a sensor mount travel in X and Y axis in a selected height (Z axis). The system was controlled with a custom designed algorithm based on LabVIEW program. A control box was used to interface the system with a computer. During preliminary evaluation, a thermal camera and a multispectral camera were installed on the sensor mount, and the integrated automated phenotyping system was continuously operated for 48 hours for autonomous data collection. The 3D robotic system had been working precisely based on the design specifications. Results showed that the 3D robotic system had time repeatability with trigger activation within 4 s and positioning error less than 0.78 mm, indicating the potential of system for automated, systematic high-throughput phenotyping. (C) 2016, IFAC (International Federation of Automatic (Control) Hosting by Elsevier Ltd.,All rights reserved.
引用
收藏
页码:242 / 247
页数:6
相关论文
共 50 条
  • [31] Review: High-throughput phenotyping to enhance the use of crop genetic resources
    Rebetzke, G. J.
    Jimenez-Berni, J.
    Fischer, R. A.
    Deery, D. M.
    Smith, D. J.
    PLANT SCIENCE, 2019, 282 : 40 - 48
  • [32] High-throughput Phenotyping and Genomic Selection: The Frontiers of Crop Breeding Converge
    Cabrera-Bosquet, Llorenc
    Crossa, Jose
    von Zitzewitz, Jarislav
    Dolors Serret, Maria
    Luis Araus, Jose
    JOURNAL OF INTEGRATIVE PLANT BIOLOGY, 2012, 54 (05) : 312 - 320
  • [33] High-throughput proximal ground crop phenotyping systems - A comprehensive review
    Rui, Z.
    Zhang, Z.
    Zhang, M.
    Azizi, A.
    Igathinathane, C.
    Cen, H.
    Vougioukas, S.
    Li, H.
    Zhang, J.
    Jiang, Y.
    Jiao, X.
    Wang, M.
    Ampatzidis, Y.
    Oladele, O. I.
    Ghasemi-Varnamkhasti, M.
    Radi, Radi
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2024, 224
  • [34] High-throughput phenotyping: Breaking through the bottleneck in future crop breeding
    Peng Song
    Jinglu Wang
    Xinyu Guo
    Wanneng Yang
    Chunjiang Zhao
    The Crop Journal, 2021, 9 (03) : 633 - 645
  • [35] High-Throughput Phenotyping Enabled Genetic Dissection of Crop Lodging in Wheat
    Singh, Daljit
    Wang, Xu
    Kumar, Uttam
    Gao, Liangliang
    Noor, Muhammad
    Imtiaz, Muhammad
    Singh, Ravi P.
    Poland, Jesse
    FRONTIERS IN PLANT SCIENCE, 2019, 10
  • [36] High-throughput phenotyping: Breaking through the bottleneck in future crop breeding
    Song, Peng
    Wang, Jinglu
    Guo, Xinyu
    Yang, Wanneng
    Zhao, Chunjiang
    CROP JOURNAL, 2021, 9 (03): : 633 - 645
  • [37] Vinobot and Vinoculer: Two Robotic Platforms for High-Throughput Field Phenotyping
    Shafiekhani, Ali
    Kadam, Suhas
    Fritschi, Felix B.
    DeSouza, Guilherme N.
    SENSORS, 2017, 17 (01)
  • [38] DEVELOPMENT OF AN AUTOMATED HIGH-THROUGHPUT PHENOTYPING SYSTEM FOR WHEAT EVALUATION IN A CONTROLLED ENVIRONMENT
    Zhang, C.
    Pumphrey, M. O.
    Zhou, J.
    Zhang, Q.
    Sankaran, S.
    TRANSACTIONS OF THE ASABE, 2019, 62 (01) : 61 - 74
  • [39] Fenomica: A Computer Vision System for High-Throughput Phenotyping
    dos Santos, Marcos Roberto
    Madalozzo, Guilherme Afonso
    Cunha Fernandes, Jose Mauricio
    Rieder, Rafael
    INTERNATIONAL JOURNAL OF AGRICULTURAL AND ENVIRONMENTAL INFORMATION SYSTEMS, 2020, 11 (01) : 1 - 22
  • [40] High-throughput mouse phenotyping
    Gates, Hilary
    Mallon, Ann-Marie
    Brown, Steve D. M.
    METHODS, 2011, 53 (04) : 394 - 404