Literature DB >> 28616808

Crop 3D-a LiDAR based platform for 3D high-throughput crop phenotyping.

Qinghua Guo1, Fangfang Wu2,3, Shuxin Pang2, Xiaoqian Zhao2,3, Linhai Chen2,3, Jin Liu2, Baolin Xue2, Guangcai Xu2, Le Li4, Haichun Jing2, Chengcai Chu5.   

Abstract

With the growing population and the reducing arable land, breeding has been considered as an effective way to solve the food crisis. As an important part in breeding, high-throughput phenotyping can accelerate the breeding process effectively. Light detection and ranging (LiDAR) is an active remote sensing technology that is capable of acquiring three-dimensional (3D) data accurately, and has a great potential in crop phenotyping. Given that crop phenotyping based on LiDAR technology is not common in China, we developed a high-throughput crop phenotyping platform, named Crop 3D, which integrated LiDAR sensor, high-resolution camera, thermal camera and hyperspectral imager. Compared with traditional crop phenotyping techniques, Crop 3D can acquire multi-source phenotypic data in the whole crop growing period and extract plant height, plant width, leaf length, leaf width, leaf area, leaf inclination angle and other parameters for plant biology and genomics analysis. In this paper, we described the designs, functions and testing results of the Crop 3D platform, and briefly discussed the potential applications and future development of the platform in phenotyping. We concluded that platforms integrating LiDAR and traditional remote sensing techniques might be the future trend of crop high-throughput phenotyping.

Keywords:  LiDAR; crop breeding; data fusion; high-throughput; integrated platform; phenotypic traits

Mesh:

Year:  2017        PMID: 28616808     DOI: 10.1007/s11427-017-9056-0

Source DB:  PubMed          Journal:  Sci China Life Sci        ISSN: 1674-7305            Impact factor:   6.038


  14 in total

1.  Estimation of maize plant height and leaf area index dynamics using an unmanned aerial vehicle with oblique and nadir photography.

Authors:  Yingpu Che; Qing Wang; Ziwen Xie; Long Zhou; Shuangwei Li; Fang Hui; Xiqing Wang; Baoguo Li; Yuntao Ma
Journal:  Ann Bot       Date:  2020-09-14       Impact factor: 4.357

2.  PSegNet: Simultaneous Semantic and Instance Segmentation for Point Clouds of Plants.

Authors:  Dawei Li; Jinsheng Li; Shiyu Xiang; Anqi Pan
Journal:  Plant Phenomics       Date:  2022-05-23

3.  Evaluating maize phenotype dynamics under drought stress using terrestrial lidar.

Authors:  Yanjun Su; Fangfang Wu; Zurui Ao; Shichao Jin; Feng Qin; Boxin Liu; Shuxin Pang; Lingli Liu; Qinghua Guo
Journal:  Plant Methods       Date:  2019-02-04       Impact factor: 4.993

4.  An automatic method for counting wheat tiller number in the field with terrestrial LiDAR.

Authors:  Yuan Fang; Xiaolei Qiu; Tai Guo; Yongqing Wang; Tao Cheng; Yan Zhu; Qi Chen; Weixing Cao; Xia Yao; Qingsong Niu; Yongqiang Hu; Lijuan Gui
Journal:  Plant Methods       Date:  2020-09-29       Impact factor: 4.993

5.  Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping.

Authors:  Riccardo Rossi; Claudio Leolini; Sergi Costafreda-Aumedes; Luisa Leolini; Marco Bindi; Alessandro Zaldei; Marco Moriondo
Journal:  Sensors (Basel)       Date:  2020-06-02       Impact factor: 3.576

6.  Deep Learning: Individual Maize Segmentation From Terrestrial Lidar Data Using Faster R-CNN and Regional Growth Algorithms.

Authors:  Shichao Jin; Yanjun Su; Shang Gao; Fangfang Wu; Tianyu Hu; Jin Liu; Wenkai Li; Dingchang Wang; Shaojiang Chen; Yuanxi Jiang; Shuxin Pang; Qinghua Guo
Journal:  Front Plant Sci       Date:  2018-06-22       Impact factor: 5.753

Review 7.  Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective.

Authors:  Keiichi Mochida; Satoru Koda; Komaki Inoue; Takashi Hirayama; Shojiro Tanaka; Ryuei Nishii; Farid Melgani
Journal:  Gigascience       Date:  2019-01-01       Impact factor: 6.524

8.  Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level.

Authors:  Shichao Jin; Yanjun Su; Shilin Song; Kexin Xu; Tianyu Hu; Qiuli Yang; Fangfang Wu; Guangcai Xu; Qin Ma; Hongcan Guan; Shuxin Pang; Yumei Li; Qinghua Guo
Journal:  Plant Methods       Date:  2020-05-13       Impact factor: 4.993

9.  High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation.

Authors:  Leonardo Volpato; Francisco Pinto; Lorena González-Pérez; Iyotirindranath Gilberto Thompson; Aluízio Borém; Matthew Reynolds; Bruno Gérard; Gemma Molero; Francelino Augusto Rodrigues
Journal:  Front Plant Sci       Date:  2021-02-16       Impact factor: 5.753

10.  Three-Dimensional Modeling of Weed Plants Using Low-Cost Photogrammetry.

Authors:  Dionisio Andújar; Mikel Calle; César Fernández-Quintanilla; Ángela Ribeiro; José Dorado
Journal:  Sensors (Basel)       Date:  2018-04-03       Impact factor: 3.576

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