Literature DB >> 21361628

High-throughput measurement of rice tillers using a conveyor equipped with x-ray computed tomography.

Wanneng Yang1, Xiaochun Xu, Lingfeng Duan, Qingming Luo, Shangbin Chen, Shaoqun Zeng, Qian Liu.   

Abstract

Tillering is one of the most important agronomic traits because the number of shoots per plant determines panicle number, a key component of grain yield. The conventional method of counting tillers is still manual. Under the condition of mass measurement, the accuracy and efficiency could be gradually degraded along with fatigue of experienced staff. Thus, manual measurement, including counting and recording, is not only time consuming but also lack objectivity. To automate this process, we developed a high-throughput facility, dubbed high-throughput system for measuring automatically rice tillers (H-SMART), for measuring rice tillers based on a conventional x-ray computed tomography (CT) system and industrial conveyor. Each pot-grown rice plant was delivered into the CT system for scanning via the conveyor equipment. A filtered back-projection algorithm was used to reconstruct the transverse section image of the rice culms. The number of tillers was then automatically extracted by image segmentation. To evaluate the accuracy of this system, three batches of rice at different growth stages (tillering, heading, or filling) were tested, yielding absolute mean absolute errors of 0.22, 0.36, and 0.36, respectively. Subsequently, the complete machine was used under industry conditions to estimate its efficiency, which was 4320 pots per continuous 24 h workday. Thus, the H-SMART could determine the number of tillers of pot-grown rice plants, providing three advantages over the manual tillering method: absence of human disturbance, automation, and high throughput. This facility expands the application of agricultural photonics in plant phenomics.

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Mesh:

Year:  2011        PMID: 21361628     DOI: 10.1063/1.3531980

Source DB:  PubMed          Journal:  Rev Sci Instrum        ISSN: 0034-6748            Impact factor:   1.523


  14 in total

1.  A novel mesh processing based technique for 3D plant analysis.

Authors:  Anthony Paproki; Xavier Sirault; Scott Berry; Robert Furbank; Jurgen Fripp
Journal:  BMC Plant Biol       Date:  2012-05-03       Impact factor: 4.215

2.  Development of a New Tool for 3D Modeling for Regenerative Medicine.

Authors:  Filippo Mattoli; Roberto Tiribuzi; Francesco D'Angelo; Ilaria di Girolamo; Mattia Quattrocelli; Simona Montesano; Lucia Crispoltoni; Vasileios Oikonomou; Maria Gabriella Cusella De Angelis; Peggy Marconi; Antonio Orlacchio; Maurilio Sampaolesi; Sabata Martino; Aldo Orlacchio
Journal:  Int J Biomed Imaging       Date:  2011-06-13

3.  A novel machine-vision-based facility for the automatic evaluation of yield-related traits in rice.

Authors:  Lingfeng Duan; Wanneng Yang; Chenglong Huang; Qian Liu
Journal:  Plant Methods       Date:  2011-12-12       Impact factor: 4.993

4.  Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice.

Authors:  Wanneng Yang; Zilong Guo; Chenglong Huang; Lingfeng Duan; Guoxing Chen; Ni Jiang; Wei Fang; Hui Feng; Weibo Xie; Xingming Lian; Gongwei Wang; Qingming Luo; Qifa Zhang; Qian Liu; Lizhong Xiong
Journal:  Nat Commun       Date:  2014-10-08       Impact factor: 14.919

Review 5.  A review of imaging techniques for plant phenotyping.

Authors:  Lei Li; Qin Zhang; Danfeng Huang
Journal:  Sensors (Basel)       Date:  2014-10-24       Impact factor: 3.576

Review 6.  Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms.

Authors:  Fernando Perez-Sanz; Pedro J Navarro; Marcos Egea-Cortines
Journal:  Gigascience       Date:  2017-11-01       Impact factor: 6.524

Review 7.  Review: Application of Artificial Intelligence in Phenomics.

Authors:  Shona Nabwire; Hyun-Kwon Suh; Moon S Kim; Insuck Baek; Byoung-Kwan Cho
Journal:  Sensors (Basel)       Date:  2021-06-25       Impact factor: 3.576

8.  Effects of X-Ray Dose On Rhizosphere Studies Using X-Ray Computed Tomography.

Authors:  Susan Zappala; Jonathan R Helliwell; Saoirse R Tracy; Stefan Mairhofer; Craig J Sturrock; Tony Pridmore; Malcolm Bennett; Sacha J Mooney
Journal:  PLoS One       Date:  2013-06-26       Impact factor: 3.240

9.  X-ray computed tomography to study rice (Oryza sativa L.) panicle development.

Authors:  Vibhuti M Jhala; Vrinda S Thaker
Journal:  J Exp Bot       Date:  2015-08-11       Impact factor: 6.992

10.  P-TRAP: a Panicle TRAit Phenotyping tool.

Authors:  Faroq A L-Tam; Helene Adam; António dos Anjos; Mathias Lorieux; Pierre Larmande; Alain Ghesquière; Stefan Jouannic; Hamid Reza Shahbazkia
Journal:  BMC Plant Biol       Date:  2013-08-29       Impact factor: 4.215

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