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Harandi N, Vandenberghe B, Vankerschaver J, Depuydt S, Van Messem A. How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques. PLANT METHODS 2023; 19:60. [PMID: 37353846 DOI: 10.1186/s13007-023-01031-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 05/19/2023] [Indexed: 06/25/2023]
Abstract
Computer vision technology is moving more and more towards a three-dimensional approach, and plant phenotyping is following this trend. However, despite its potential, the complexity of the analysis of 3D representations has been the main bottleneck hindering the wider deployment of 3D plant phenotyping. In this review we provide an overview of typical steps for the processing and analysis of 3D representations of plants, to offer potential users of 3D phenotyping a first gateway into its application, and to stimulate its further development. We focus on plant phenotyping applications where the goal is to measure characteristics of single plants or crop canopies on a small scale in research settings, as opposed to large scale crop monitoring in the field.
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Affiliation(s)
- Negin Harandi
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, 119 Songdomunhwa-ro, Yeonsu-gu, Incheon, South Korea
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281, S9, Ghent, Belgium
| | | | - Joris Vankerschaver
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, 119 Songdomunhwa-ro, Yeonsu-gu, Incheon, South Korea
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281, S9, Ghent, Belgium
| | - Stephen Depuydt
- Erasmus Applied University of Sciences and Arts, Campus Kaai, Nijverheidskaai 170, Anderlecht, Belgium
| | - Arnout Van Messem
- Department of Mathematics, Université de Liège, Allée de la Découverte 12, Liège, Belgium.
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Ma Z, Du R, Xie J, Sun D, Fang H, Jiang L, Cen H. Phenotyping of Silique Morphology in Oilseed Rape Using Skeletonization with Hierarchical Segmentation. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0027. [PMID: 36939450 PMCID: PMC10017417 DOI: 10.34133/plantphenomics.0027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 02/03/2023] [Indexed: 06/18/2023]
Abstract
Silique morphology is an important trait that determines the yield output of oilseed rape (Brassica napus L.). Segmenting siliques and quantifying traits are challenging because of the complicated structure of an oilseed rape plant at the reproductive stage. This study aims to develop an accurate method in which a skeletonization algorithm was combined with the hierarchical segmentation (SHS) algorithm to separate siliques from the whole plant using 3-dimensional (3D) point clouds. We combined the L1-median skeleton with the random sample consensus for iteratively extracting skeleton points and optimized the skeleton based on information such as distance, angle, and direction from neighborhood points. Density-based spatial clustering of applications with noise and weighted unidirectional graph were used to achieve hierarchical segmentation of siliques. Using the SHS, we quantified the silique number (SN), silique length (SL), and silique volume (SV) automatically based on the geometric rules. The proposed method was tested with the oilseed rape plants at the mature stage grown in a greenhouse and field. We found that our method showed good performance in silique segmentation and phenotypic extraction with R 2 values of 0.922 and 0.934 for SN and total SL, respectively. Additionally, SN, total SL, and total SV had the statistical significance of correlations with the yield of a plant, with R values of 0.935, 0.916, and 0.897, respectively. Overall, the SHS algorithm is accurate, efficient, and robust for the segmentation of siliques and extraction of silique morphological parameters, which is promising for high-throughput silique phenotyping in oilseed rape breeding.
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Affiliation(s)
- Zhihong Ma
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, P.R. China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, P.R. China
| | - Ruiming Du
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, P.R. China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, P.R. China
| | - Jiayang Xie
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, P.R. China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, P.R. China
| | - Dawei Sun
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, P.R. China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, P.R. China
| | - Hui Fang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, P.R. China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, P.R. China
| | - Lixi Jiang
- Institute of Crop Science and Zhejiang Key Laboratory of Crop Germplasm, Zhejiang University, Hangzhou 310058, P.R. China
| | - Haiyan Cen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, P.R. China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, P.R. China
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Bouvet L, Holdgate S, James L, Thomas J, Mackay IJ, Cockram J. The evolving battle between yellow rust and wheat: implications for global food security. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:741-753. [PMID: 34821981 PMCID: PMC8942934 DOI: 10.1007/s00122-021-03983-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 10/21/2021] [Indexed: 05/04/2023]
Abstract
Wheat (Triticum aestivum L.) is a global commodity, and its production is a key component underpinning worldwide food security. Yellow rust, also known as stripe rust, is a wheat disease caused by the fungus Puccinia striiformis Westend f. sp. tritici (Pst), and results in yield losses in most wheat growing areas. Recently, the rapid global spread of genetically diverse sexually derived Pst races, which have now largely replaced the previous clonally propagated slowly evolving endemic populations, has resulted in further challenges for the protection of global wheat yields. However, advances in the application of genomics approaches, in both the host and pathogen, combined with classical genetic approaches, pathogen and disease monitoring, provide resources to help increase the rate of genetic gain for yellow rust resistance via wheat breeding while reducing the carbon footprint of the crop. Here we review key elements in the evolving battle between the pathogen and host, with a focus on solutions to help protect future wheat production from this globally important disease.
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Affiliation(s)
- Laura Bouvet
- John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, CB2 3EA, UK
| | - Sarah Holdgate
- John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Lucy James
- John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Jane Thomas
- John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Ian J Mackay
- John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
- Scotland's Rural College (SRUC), The King's Buildings, West Mains Road, Edinburgh, EH9 3JG, UK
| | - James Cockram
- John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK.
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Okura F. 3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping. BREEDING SCIENCE 2022; 72:31-47. [PMID: 36045890 PMCID: PMC8987840 DOI: 10.1270/jsbbs.21074] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 11/26/2021] [Indexed: 06/15/2023]
Abstract
This paper reviews the past and current trends of three-dimensional (3D) modeling and reconstruction of plants and trees. These topics have been studied in multiple research fields, including computer vision, graphics, plant phenotyping, and forestry. This paper, therefore, provides a cross-cutting review. Representations of plant shape and structure are first summarized, where every method for plant modeling and reconstruction is based on a shape/structure representation. The methods were then categorized into 1) creating non-existent plants (modeling) and 2) creating models from real-world plants (reconstruction). This paper also discusses the limitations of current methods and possible future directions.
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Affiliation(s)
- Fumio Okura
- Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan
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Ghahremani M, Williams K, Corke FMK, Tiddeman B, Liu Y, Doonan JH. Deep Segmentation of Point Clouds of Wheat. FRONTIERS IN PLANT SCIENCE 2021; 12:608732. [PMID: 33841454 PMCID: PMC8025700 DOI: 10.3389/fpls.2021.608732] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 02/24/2021] [Indexed: 05/31/2023]
Abstract
The 3D analysis of plants has become increasingly effective in modeling the relative structure of organs and other traits of interest. In this paper, we introduce a novel pattern-based deep neural network, Pattern-Net, for segmentation of point clouds of wheat. This study is the first to segment the point clouds of wheat into defined organs and to analyse their traits directly in 3D space. Point clouds have no regular grid and thus their segmentation is challenging. Pattern-Net creates a dynamic link among neighbors to seek stable patterns from a 3D point set across several levels of abstraction using the K-nearest neighbor algorithm. To this end, different layers are connected to each other to create complex patterns from the simple ones, strengthen dynamic link propagation, alleviate the vanishing-gradient problem, encourage link reuse and substantially reduce the number of parameters. The proposed deep network is capable of analysing and decomposing unstructured complex point clouds into semantically meaningful parts. Experiments on a wheat dataset verify the effectiveness of our approach for segmentation of wheat in 3D space.
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Affiliation(s)
- Morteza Ghahremani
- National Plant Phenomics Centre, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
- Department of Computer Science, Aberystwyth University, Aberystwyth, United Kingdom
| | - Kevin Williams
- National Plant Phenomics Centre, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Fiona M. K. Corke
- National Plant Phenomics Centre, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Bernard Tiddeman
- Department of Computer Science, Aberystwyth University, Aberystwyth, United Kingdom
| | - Yonghuai Liu
- Department of Computer Science, Edge Hill University, Ormskirk, United Kingdom
| | - John H. Doonan
- National Plant Phenomics Centre, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
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