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Zhang S, Song Y, Ou R, Liu Y, Li S, Lu X, Xu S, Su Y, Jiang D, Ding Y, Xia H, Guo Q, Wu J, Zhang J, Wang J, Jin S. SCAG: A Stratified, Clustered, and Growing-Based Algorithm for Soybean Branch Angle Extraction and Ideal Plant Architecture Evaluation. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0190. [PMID: 39045573 PMCID: PMC11265809 DOI: 10.34133/plantphenomics.0190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Accepted: 04/28/2024] [Indexed: 07/25/2024]
Abstract
Three-dimensional (3D) phenotyping is important for studying plant structure and function. Light detection and ranging (LiDAR) has gained prominence in 3D plant phenotyping due to its ability to collect 3D point clouds. However, organ-level branch detection remains challenging due to small targets, sparse points, and low signal-to-noise ratios. In addition, extracting biologically relevant angle traits is difficult. In this study, we developed a stratified, clustered, and growing-based algorithm (SCAG) for soybean branch detection and branch angle calculation from LiDAR data, which is heuristic, open-source, and expandable. SCAG achieved high branch detection accuracy (F-score = 0.77) and branch angle calculation accuracy (r = 0.84) when evaluated on 152 diverse soybean varieties. Meanwhile, the SCAG outperformed 2 other classic algorithms, the support vector machine (F-score = 0.53) and density-based methods (F-score = 0.55). Moreover, after applying the SCAG to 405 soybean varieties over 2 consecutive years, we quantified various 3D traits, including canopy width, height, stem length, and average angle. After data filtering, we identified novel heritable and repeatable traits for evaluating soybean density tolerance potential, such as the ratio of average angle to height and the ratio of average angle to stem length, which showed greater potential than the well-known ratio of canopy width to height trait. Our work demonstrates remarkable advances in 3D phenotyping and plant architecture screening. The algorithm can be applied to other crops, such as maize and tomato. Our dataset, scripts, and software are public, which can further benefit the plant science community by enhancing plant architecture characterization and ideal variety selection.
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Affiliation(s)
- Songyin Zhang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production cosponsored by Province and Ministry, State Key Laboratory of Crop Genetics and Germplasm Enhancement,
Nanjing Agricultural University, Nanjing 210095, China
| | - Yinmeng Song
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production cosponsored by Province and Ministry, State Key Laboratory of Crop Genetics and Germplasm Enhancement,
Nanjing Agricultural University, Nanjing 210095, China
- National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), College of Agriculture,
Nanjing Agricultural University, Nanjing 210095, China
| | - Ran Ou
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production cosponsored by Province and Ministry, State Key Laboratory of Crop Genetics and Germplasm Enhancement,
Nanjing Agricultural University, Nanjing 210095, China
- National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), College of Agriculture,
Nanjing Agricultural University, Nanjing 210095, China
| | - Yiqiang Liu
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production cosponsored by Province and Ministry, State Key Laboratory of Crop Genetics and Germplasm Enhancement,
Nanjing Agricultural University, Nanjing 210095, China
- Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Shaochen Li
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production cosponsored by Province and Ministry, State Key Laboratory of Crop Genetics and Germplasm Enhancement,
Nanjing Agricultural University, Nanjing 210095, China
| | - Xinlan Lu
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production cosponsored by Province and Ministry, State Key Laboratory of Crop Genetics and Germplasm Enhancement,
Nanjing Agricultural University, Nanjing 210095, China
| | - Shan Xu
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production cosponsored by Province and Ministry, State Key Laboratory of Crop Genetics and Germplasm Enhancement,
Nanjing Agricultural University, Nanjing 210095, China
| | - Yanjun Su
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany,
Chinese Academy of Sciences, Beijing 100093, China
| | - Dong Jiang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production cosponsored by Province and Ministry, State Key Laboratory of Crop Genetics and Germplasm Enhancement,
Nanjing Agricultural University, Nanjing 210095, China
- National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), College of Agriculture,
Nanjing Agricultural University, Nanjing 210095, China
- Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Yanfeng Ding
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production cosponsored by Province and Ministry, State Key Laboratory of Crop Genetics and Germplasm Enhancement,
Nanjing Agricultural University, Nanjing 210095, China
- National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), College of Agriculture,
Nanjing Agricultural University, Nanjing 210095, China
- Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Haifeng Xia
- School of Automation,
Southeast University, Nanjing 210096, China
| | - Qinghua Guo
- Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences,
Peking University, Beijing 100871, China
| | - Jin Wu
- Division for Ecology and Biodiversity, School of Biological Sciences,
The University of Hong Kong, Hong Kong, China
| | - Jiaoping Zhang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production cosponsored by Province and Ministry, State Key Laboratory of Crop Genetics and Germplasm Enhancement,
Nanjing Agricultural University, Nanjing 210095, China
- National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), College of Agriculture,
Nanjing Agricultural University, Nanjing 210095, China
| | - Jiao Wang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production cosponsored by Province and Ministry, State Key Laboratory of Crop Genetics and Germplasm Enhancement,
Nanjing Agricultural University, Nanjing 210095, China
- National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), College of Agriculture,
Nanjing Agricultural University, Nanjing 210095, China
| | - Shichao Jin
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production cosponsored by Province and Ministry, State Key Laboratory of Crop Genetics and Germplasm Enhancement,
Nanjing Agricultural University, Nanjing 210095, China
- Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
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2
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Wu T, Shen P, Dai J, Ma Y, Feng Y. A Pathway to Assess Genetic Variation of Wheat Germplasm by Multidimensional Traits with Digital Images. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0119. [PMID: 38026469 PMCID: PMC10665127 DOI: 10.34133/plantphenomics.0119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 11/04/2023] [Indexed: 12/01/2023]
Abstract
In this paper, a new pathway was proposed to assess the germplasm genetic variation by multidimensional traits of wheat seeds generated from digital images. A machine vision platform was first established to reconstruct wheat germplasm 3D model from omnidirectional image sequences of wheat seeds. Then, multidimensional traits were conducted from the wheat germplasm 3D model, including seed length, width, thickness, surface area, volume, maximum projection area, roundness, and 2 new defined traits called cardioid-derived area and the index of adjustment (J index). To assess genetic variation of wheat germplasm, phenotypic coefficients of variation (PCVs), analysis of variance (ANOVA), clustering, and the defined genetic variation factor (GVF) were calculated using the extracted morphological traits of 15 wheat accessions comprising 13 offspring and 2 parents. The measurement accuracy of 3D reconstruction model is demonstrated by the correlation coefficient (R) and root mean square errors (RMSEs). Results of PCVs among all the traits show importance of multidimensional traits, as seed volume (22.4%), cardioid-derived area (16.97%), and maximum projection area (14.67%). ANOVA shows a highly significance difference among all accessions. The results of GVF innovatively reflect the connection between genotypic variance and phenotypic traits from parents to offspring. Our results confirmed that extracting multidimensional traits from digital images is a promising high-throughput and cost-efficient pathway that can be included as a valuable approach in genetic variation assessment, and it can provide useful information for genetic improvement, preservation, and evaluation of wheat germplasm.
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Affiliation(s)
- Tingting Wu
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
- Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China
| | - Peng Shen
- College of Information Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Jianlong Dai
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Yuntao Ma
- College of Land Science and Technology,
China Agricultural University, Beijing 100091, China
| | - Yi Feng
- College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China
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Wada KC, Hayashi A, Lee U, Tanabata T, Isobe S, Itoh H, Maeda H, Fujisako S, Kochi N. A Novel Method for Quantifying Plant Morphological Characteristics Using Normal Vectors and Local Curvature Data via 3D Modelling-A Case Study in Leaf Lettuce. SENSORS (BASEL, SWITZERLAND) 2023; 23:6825. [PMID: 37571608 PMCID: PMC10422436 DOI: 10.3390/s23156825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 07/24/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023]
Abstract
Three-dimensional measurement is a high-throughput method that can record a large amount of information. Three-dimensional modelling of plants has the possibility to not only automate dimensional measurement, but to also enable visual assessment to be quantified, eliminating ambiguity in human judgment. In this study, we have developed new methods that could be used for the morphological analysis of plants from the information contained in 3D data. Specifically, we investigated characteristics that can be measured by scale (dimension) and/or visual assessment by humans. The latter is particularly novel in this paper. The characteristics that can be measured on a scale-related dimension were tested based on the bounding box, convex hull, column solid, and voxel. Furthermore, for characteristics that can be evaluated by visual assessment, we propose a new method using normal vectors and local curvature (LC) data. For these examinations, we used our highly accurate all-around 3D plant modelling system. The coefficient of determination between manual measurements and the scale-related methods were all above 0.9. Furthermore, the differences in LC calculated from the normal vector data allowed us to visualise and quantify the concavity and convexity of leaves. This technique revealed that there were differences in the time point at which leaf blistering began to develop among the varieties. The precise 3D model made it possible to perform quantitative measurements of lettuce size and morphological characteristics. In addition, the newly proposed LC-based analysis method made it possible to quantify the characteristics that rely on visual assessment. This research paper was able to demonstrate the following possibilities as outcomes: (1) the automation of conventional manual measurements, and (2) the elimination of variability caused by human subjectivity, thereby rendering evaluations by skilled experts unnecessary.
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Affiliation(s)
- Kaede C Wada
- Breeding Big Data Management and Utilization Group, Division of Smart Breeding Research, Institute of Crop Science, National Agriculture and Food Research Organization (NARO), Tsukuba 305-0856, Japan
| | - Atsushi Hayashi
- Research Center for Agricultural Robotics, Core Technology Research Headquarters, NARO, Tsukuba 305-0856, Japan
| | - Unseok Lee
- Research Center for Agricultural Robotics, Core Technology Research Headquarters, NARO, Tsukuba 305-0856, Japan
| | - Takanari Tanabata
- Department of Frontier Research Plant Genomics and Genetics, Kazusa DNA Research Institute, Kisarazu 292-0818, Japan
| | - Sachiko Isobe
- Department of Frontier Research Plant Genomics and Genetics, Kazusa DNA Research Institute, Kisarazu 292-0818, Japan
| | - Hironori Itoh
- Breeding Big Data Management and Utilization Group, Division of Smart Breeding Research, Institute of Crop Science, National Agriculture and Food Research Organization (NARO), Tsukuba 305-0856, Japan
| | - Hideki Maeda
- Center for Seeds and Seedlings, Nishinihon Station (NARO), Kasaoka 714-0054, Japan
| | - Satoshi Fujisako
- Center for Seeds and Seedlings, Nishinihon Station (NARO), Kasaoka 714-0054, Japan
| | - Nobuo Kochi
- Research Center for Agricultural Robotics, Core Technology Research Headquarters, NARO, Tsukuba 305-0856, Japan
- Department of Frontier Research Plant Genomics and Genetics, Kazusa DNA Research Institute, Kisarazu 292-0818, Japan
- R&D Initiative, Chuo University, Kasuga, Tokyo 112-8551, Japan
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Song Q, Liu F, Bu H, Zhu XG. Quantifying Contributions of Different Factors to Canopy Photosynthesis in 2 Maize Varieties: Development of a Novel 3D Canopy Modeling Pipeline. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0075. [PMID: 37502446 PMCID: PMC10371248 DOI: 10.34133/plantphenomics.0075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 07/01/2023] [Indexed: 07/29/2023]
Abstract
Crop yield potential is intrinsically related to canopy photosynthesis; therefore, improving canopy photosynthetic efficiency is a major focus of current efforts to enhance crop yield. Canopy photosynthesis rate (Ac) is influenced by several factors, including plant architecture, leaf chlorophyll content, and leaf photosynthetic properties, which interact with each other. Identifying factors that restrict canopy photosynthesis and target adjustments to improve canopy photosynthesis in a specific crop cultivar pose an important challenge for the breeding community. To address this challenge, we developed a novel pipeline that utilizes factorial analysis, canopy photosynthesis modeling, and phenomics data collected using a 64-camera multi-view stereo system, enabling the dissection of the contributions of different factors to differences in canopy photosynthesis between maize cultivars. We applied this method to 2 maize varieties, W64A and A619, and found that leaf photosynthetic efficiency is the primary determinant (17.5% to 29.2%) of the difference in Ac between 2 maize varieties at all stages, and plant architecture at early stages also contribute to the difference in Ac (5.3% to 6.7%). Additionally, the contributions of each leaf photosynthetic parameter and plant architectural trait were dissected. We also found that the leaf photosynthetic parameters were linearly correlated with Ac and plant architecture traits were non-linearly related to Ac. This study developed a novel pipeline that provides a method for dissecting the relationship among individual phenotypes controlling the complex trait of canopy photosynthesis.
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Affiliation(s)
- Qingfeng Song
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
| | - Fusang Liu
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
| | - Hongyi Bu
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
| | - Xin-Guang Zhu
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
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5
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Zhou H, Zhou Y, Long W, Wang B, Zhou Z, Chen Y. A fast phenotype approach of 3D point clouds of Pinus massoniana seedlings. FRONTIERS IN PLANT SCIENCE 2023; 14:1146490. [PMID: 37434607 PMCID: PMC10332475 DOI: 10.3389/fpls.2023.1146490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 06/05/2023] [Indexed: 07/13/2023]
Abstract
The phenotyping of Pinus massoniana seedlings is essential for breeding, vegetation protection, resource investigation, and so on. Few reports regarding estimating phenotypic parameters accurately in the seeding stage of Pinus massoniana plants using 3D point clouds exist. In this study, seedlings with heights of approximately 15-30 cm were taken as the research object, and an improved approach was proposed to automatically calculate five key parameters. The key procedure of our proposed method includes point cloud preprocessing, stem and leaf segmentation, and morphological trait extraction steps. In the skeletonization step, the cloud points were sliced in vertical and horizontal directions, gray value clustering was performed, the centroid of the slice was regarded as the skeleton point, and the alternative skeleton point of the main stem was determined by the DAG single source shortest path algorithm. Then, the skeleton points of the canopy in the alternative skeleton point were removed, and the skeleton point of the main stem was obtained. Last, the main stem skeleton point after linear interpolation was restored, while stem and leaf segmentation was achieved. Because of the leaf morphological characteristics of Pinus massoniana, its leaves are large and dense. Even using a high-precision industrial digital readout, it is impossible to obtain a 3D model of Pinus massoniana leaves. In this study, an improved algorithm based on density and projection is proposed to estimate the relevant parameters of Pinus massoniana leaves. Finally, five important phenotypic parameters, namely plant height, stem diameter, main stem length, regional leaf length, and total leaf number, are obtained from the skeleton and the point cloud after separation and reconstruction. The experimental results showed that there was a high correlation between the actual value from manual measurement and the predicted value from the algorithm output. The accuracies of the main stem diameter, main stem length, and leaf length were 93.5%, 95.7%, and 83.8%, respectively, which meet the requirements of real applications.
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Affiliation(s)
- Honghao Zhou
- College of Electronic and Information Engineering, Zhejiang University of Science and Technology, HangZhou, ZheJiang, China
- College of Biological and Chemical Engineering, Zhejiang University of Science and Technology, HangZhou, ZheJiang, China
| | - Yang Zhou
- College of Electronic and Information Engineering, Zhejiang University of Science and Technology, HangZhou, ZheJiang, China
- College of Biological and Chemical Engineering, Zhejiang University of Science and Technology, HangZhou, ZheJiang, China
| | - Wei Long
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, HangZhou, ZheJiang, China
| | - Bin Wang
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, HangZhou, ZheJiang, China
| | - Zhichun Zhou
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, HangZhou, ZheJiang, China
| | - Yue Chen
- Horticulture Institute, Zhejiang Academy of Agricultural Sciences, HangZhou, ZheJiang, China
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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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Kochi N, Hayashi A, Shinohara Y, Tanabata T, Kodama K, Isobe S. All-around 3D plant modeling system using multiple images and its composition. BREEDING SCIENCE 2022; 72:75-84. [PMID: 36045893 PMCID: PMC8987847 DOI: 10.1270/jsbbs.21068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 12/02/2021] [Indexed: 06/15/2023]
Abstract
In this study, we developed an all-around 3D plant modeling system that operates using images and is capable of measuring plants non-destructively without any contact. During the fabrication of this device, we selected a method capable of performing 3D model reconstruction from multiple images. We then developed an improved SfM-MVS (Structure from Motion / Multi-View-Stereo) method that enables 3D reconstruction by simply capturing images with a camera. The resulting image-based method offers a high degree of freedom because the hardware and software can comprise commercially available products, and it permits the use of one or more cameras according to the shape and size of the plant. The advantages of the image-based method are that 3D reconstruction can be conducted at any time as long as the images are already taken, and that the desired locations can be observed, measured, and analyzed from 2D images and a 3D point cloud. The device we developed is capable of 3D measurements and modeling of plants from a few millimeters to 2.4 m of height using this method. This article explains this device, the principles of its composition, and the accuracy of the models obtained from it.
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Affiliation(s)
- Nobuo Kochi
- National Agriculture and Food Research Organization, Tokyo 105-0003, Japan
| | - Atsushi Hayashi
- National Agriculture and Food Research Organization, Tokyo 105-0003, Japan
| | - Yota Shinohara
- National Agriculture and Food Research Organization, Tokyo 105-0003, Japan
| | | | - Kunihiro Kodama
- Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan
| | - Sachiko Isobe
- Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan
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Noshita K, Murata H, Kirie S. Model-based plant phenomics on morphological traits using morphometric descriptors. BREEDING SCIENCE 2022; 72:19-30. [PMID: 36045892 PMCID: PMC8987841 DOI: 10.1270/jsbbs.21078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 12/20/2021] [Indexed: 06/15/2023]
Abstract
The morphological traits of plants contribute to many important functional features such as radiation interception, lodging tolerance, gas exchange efficiency, spatial competition between individuals and/or species, and disease resistance. Although the importance of plant phenotyping techniques is increasing with advances in molecular breeding strategies, there are barriers to its advancement, including the gap between measured data and phenotypic values, low quantitativity, and low throughput caused by the lack of models for representing morphological traits. In this review, we introduce morphological descriptors that can be used for phenotyping plant morphological traits. Geometric morphometric approaches pave the way to a general-purpose method applicable to single units. Hierarchical structures composed of an indefinite number of multiple elements, which is often observed in plants, can be quantified in terms of their multi-scale topological characteristics using topological data analysis. Theoretical morphological models capture specific anatomical structures, if recognized. These morphological descriptors provide us with the advantages of model-based plant phenotyping, including robust quantification of limited datasets. Moreover, we discuss the future possibilities that a system of model-based measurement and model refinement would solve the lack of morphological models and the difficulties in scaling out the phenotyping processes.
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Affiliation(s)
- Koji Noshita
- Department of Biology, Kyushu University, Fukuoka, Fukuoka 819-0395, Japan
- Plant Frontier Research Center, Kyushu University, Fukuoka, Fukuoka 819-0395, Japan
| | - Hidekazu Murata
- Department of Biology, Kyushu University, Fukuoka, Fukuoka 819-0395, Japan
| | - Shiryu Kirie
- metaPhorest (Bioaesthetics Platform), Department of Electrical Engineering and Bioscience, Waseda University, TWIns, Tokyo 162-8480, Japan
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9
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Wu S, Wen W, Gou W, Lu X, Zhang W, Zheng C, Xiang Z, Chen L, Guo X. A miniaturized phenotyping platform for individual plants using multi-view stereo 3D reconstruction. FRONTIERS IN PLANT SCIENCE 2022; 13:897746. [PMID: 36003825 PMCID: PMC9393617 DOI: 10.3389/fpls.2022.897746] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 07/08/2022] [Indexed: 05/14/2023]
Abstract
Plant phenotyping is essential in plant breeding and management. High-throughput data acquisition and automatic phenotypes extraction are common concerns in plant phenotyping. Despite the development of phenotyping platforms and the realization of high-throughput three-dimensional (3D) data acquisition in tall plants, such as maize, handling small-size plants with complex structural features remains a challenge. This study developed a miniaturized shoot phenotyping platform MVS-Pheno V2 focusing on low plant shoots. The platform is an improvement of MVS-Pheno V1 and was developed based on multi-view stereo 3D reconstruction. It has the following four components: Hardware, wireless communication and control, data acquisition system, and data processing system. The hardware sets the rotation on top of the platform, separating plants to be static while rotating. A novel local network was established to realize wireless communication and control; thus, preventing cable twining. The data processing system was developed to calibrate point clouds and extract phenotypes, including plant height, leaf area, projected area, shoot volume, and compactness. This study used three cultivars of wheat shoots at four growth stages to test the performance of the platform. The mean absolute percentage error of point cloud calibration was 0.585%. The squared correlation coefficient R 2 was 0.9991, 0.9949, and 0.9693 for plant height, leaf length, and leaf width, respectively. The root mean squared error (RMSE) was 0.6996, 0.4531, and 0.1174 cm for plant height, leaf length, and leaf width. The MVS-Pheno V2 platform provides an alternative solution for high-throughput phenotyping of low individual plants and is especially suitable for shoot architecture-related plant breeding and management studies.
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Affiliation(s)
- Sheng Wu
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Weiliang Wen
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Wenbo Gou
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Xianju Lu
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Wenqi Zhang
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Chenxi Zheng
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Zhiwei Xiang
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Liping Chen
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- *Correspondence: Liping Chen
| | - Xinyu Guo
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- College of Agricultural Engineering, Jiangsu University, Zhenjiang, China
- Xinyu Guo
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10
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Burgess AJ, Durand M, Gibbs JA, Retkute R, Robson TM, Murchie EH. The effect of canopy architecture on the patterning of "windflecks" within a wheat canopy. PLANT, CELL & ENVIRONMENT 2021; 44:3524-3537. [PMID: 34418115 DOI: 10.1111/pce.14168] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 08/14/2021] [Accepted: 08/16/2021] [Indexed: 06/13/2023]
Abstract
Under field conditions, plants are subject to wind-induced movement which creates fluctuations of light intensity and spectral quality reaching the leaves, defined here as windflecks. Within this study, irradiance within two contrasting wheat (Triticum aestivum) canopies during full sun conditions was measured using a spectroradiometer to determine the frequency, duration and magnitude of low- to high-light events plus the spectral composition during wind-induced movement. Similarly, a static canopy was modelled using three-dimensional reconstruction and ray tracing to determine fleck characteristics without the presence of wind. Corresponding architectural traits were measured manually and in silico including plant height, leaf area and angle plus biomechanical properties. Light intensity can differ up to 40% during a windfleck, with changes occurring on a sub-second scale compared to ~5 min in canopies not subject to wind. Features such as a shorter height, more erect leaf stature and having an open structure led to an increased frequency and reduced time interval of light flecks in the CMH79A canopy compared to Paragon. This finding illustrates the potential for architectural traits to be selected to improve the canopy light environment and provides the foundation to further explore the links between plant form and function in crop canopies.
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Affiliation(s)
- Alexandra J Burgess
- Division of Agriculture and Environmental Sciences, School of Biosciences, University of Nottingham Sutton Bonington Campus, Leicestershire, UK
| | - Maxime Durand
- Organismal and Evolutionary Biology (OEB), Faculty of Biological and Environmental Sciences, Viikki Plant Science Centre (ViPS), University of Helsinki, Helsinki, Finland
| | - Jonathon A Gibbs
- Computer Vision Lab, School of Computer Science, University of Nottingham Jubilee Campus, Nottingham, UK
| | - Renata Retkute
- Department of Plant Sciences, University of Cambridge, Cambridge, UK
| | - T Matthew Robson
- Organismal and Evolutionary Biology (OEB), Faculty of Biological and Environmental Sciences, Viikki Plant Science Centre (ViPS), University of Helsinki, Helsinki, Finland
| | - Erik H Murchie
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham Sutton Bonington Campus, Leicestershire, UK
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11
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Liu F, Song Q, Zhao J, Mao L, Bu H, Hu Y, Zhu XG. Canopy occupation volume as an indicator of canopy photosynthetic capacity. THE NEW PHYTOLOGIST 2021; 232:941-956. [PMID: 34245568 DOI: 10.1111/nph.17611] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 07/03/2021] [Indexed: 06/13/2023]
Abstract
Leaf angle and leaf area index together influence canopy light interception and canopy photosynthesis. However, so far, there is no effective method to identify the optimal combination of these two parameters for canopy photosynthesis. In this study, first a robust high-throughput method for accurate segmentation of maize organs based on 3D point clouds data was developed, then the segmented plant organs were used to generate new 3D point clouds for the canopy of altered architectures. With this, we simulated the synergistic effect of leaf area and leaf angle on canopy photosynthesis. The results show that, compared to the traditional parameters describing the canopy photosynthesis including leaf area index, facet angle and canopy coverage, a new parameter - the canopy occupation volume (COV) - can better explain the variations of canopy photosynthetic capacity. Specifically, COV can explain > 79% variations of canopy photosynthesis generated by changing leaf angle and > 84% variations of canopy photosynthesis generated by changing leaf area. As COV can be calculated in a high-throughput manner based on the canopy point clouds, it can be used to evaluate canopy architecture in breeding and agronomic research.
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Affiliation(s)
- Fusang Liu
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Qingfeng Song
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jinke Zhao
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Linxiong Mao
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hongyi Bu
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
| | - Yong Hu
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
| | - Xin-Guang Zhu
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200031, China
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12
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Salter WT, Shrestha A, Barbour MM. Open source 3D phenotyping of chickpea plant architecture across plant development. PLANT METHODS 2021; 17:95. [PMID: 34530876 PMCID: PMC8444385 DOI: 10.1186/s13007-021-00795-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 08/30/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Being able to accurately assess the 3D architecture of plant canopies can allow us to better estimate plant productivity and improve our understanding of underlying plant processes. This is especially true if we can monitor these traits across plant development. Photogrammetry techniques, such as structure from motion, have been shown to provide accurate 3D reconstructions of monocot crop species such as wheat and rice, yet there has been little success reconstructing crop species with smaller leaves and more complex branching architectures, such as chickpea. RESULTS In this work, we developed a low-cost 3D scanner and used an open-source data processing pipeline to assess the 3D structure of individual chickpea plants. The imaging system we developed consists of a user programmable turntable and three cameras that automatically captures 120 images of each plant and offloads these to a computer for processing. The capture process takes 5-10 min for each plant and the majority of the reconstruction process on a Windows PC is automated. Plant height and total plant surface area were validated against "ground truth" measurements, producing R2 > 0.99 and a mean absolute percentage error < 10%. We demonstrate the ability to assess several important architectural traits, including canopy volume and projected area, and estimate relative growth rate in commercial chickpea cultivars and lines from local and international breeding collections. Detailed analysis of individual reconstructions also allowed us to investigate partitioning of plant surface area, and by proxy plant biomass. CONCLUSIONS Our results show that it is possible to use low-cost photogrammetry techniques to accurately reconstruct individual chickpea plants, a crop with a complex architecture consisting of many small leaves and a highly branching structure. We hope that our use of open-source software and low-cost hardware will encourage others to use this promising technique for more architecturally complex species.
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Affiliation(s)
- William T. Salter
- School of Life and Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney, NSW 2570 Brownlow Hill, Australia
| | - Arjina Shrestha
- School of Life and Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney, NSW 2570 Brownlow Hill, Australia
| | - Margaret M. Barbour
- School of Life and Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney, NSW 2570 Brownlow Hill, Australia
- School of Science, University of Waikato, Hillcrest, Hamilton, 3216 New Zealand
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13
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Ando R, Ozasa Y, Guo W. Robust Surface Reconstruction of Plant Leaves from 3D Point Clouds. PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:3184185. [PMID: 33860276 PMCID: PMC8038853 DOI: 10.34133/2021/3184185] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 02/15/2021] [Indexed: 05/19/2023]
Abstract
The automation of plant phenotyping using 3D imaging techniques is indispensable. However, conventional methods for reconstructing the leaf surface from 3D point clouds have a trade-off between the accuracy of leaf surface reconstruction and the method's robustness against noise and missing points. To mitigate this trade-off, we developed a leaf surface reconstruction method that reduces the effects of noise and missing points while maintaining surface reconstruction accuracy by capturing two components of the leaf (the shape and distortion of that shape) separately using leaf-specific properties. This separation simplifies leaf surface reconstruction compared with conventional methods while increasing the robustness against noise and missing points. To evaluate the proposed method, we reconstructed the leaf surfaces from 3D point clouds of leaves acquired from two crop species (soybean and sugar beet) and compared the results with those of conventional methods. The result showed that the proposed method robustly reconstructed the leaf surfaces, despite the noise and missing points for two different leaf shapes. To evaluate the stability of the leaf surface reconstructions, we also calculated the leaf surface areas for 14 consecutive days of the target leaves. The result derived from the proposed method showed less variation of values and fewer outliers compared with the conventional methods.
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Affiliation(s)
- Ryuhei Ando
- Graduate School of Science and Technology, Keio University, Japan
| | - Yuko Ozasa
- School of System Design and Technology, Tokyo Denki University, Japan
| | - Wei Guo
- International Field Phenomics Research Laboratory, Institute for Sustainable Agro-ecosystem Services, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
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14
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Foo CC, Burgess AJ, Retkute R, Tree-Intong P, Ruban AV, Murchie EH. Photoprotective energy dissipation is greater in the lower, not the upper, regions of a rice canopy: a 3D analysis. JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:7382-7392. [PMID: 32905587 PMCID: PMC7906788 DOI: 10.1093/jxb/eraa411] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 09/07/2020] [Indexed: 05/22/2023]
Abstract
High light intensities raise photosynthetic and plant growth rates but can cause damage to the photosynthetic machinery. The likelihood and severity of deleterious effects are minimised by a set of photoprotective mechanisms, one key process being the controlled dissipation of energy from chlorophyll within PSII known as non-photochemical quenching (NPQ). Although ubiquitous, the role of NPQ in plant productivity is important because it momentarily reduces the quantum efficiency of photosynthesis. Rice plants overexpressing and deficient in the gene encoding a central regulator of NPQ, the protein PsbS, were used to assess the effect of protective effectiveness of NPQ (pNPQ) at the canopy scale. Using a combination of three-dimensional reconstruction, modelling, chlorophyll fluorescence, and gas exchange, the influence of altered NPQ capacity on the distribution of pNPQ was explored. A higher phototolerance in the lower layers of a canopy was found, regardless of genotype, suggesting a mechanism for increased protection for leaves that experience relatively low light intensities interspersed with brief periods of high light. Relative to wild-type plants, psbS overexpressors have a reduced risk of photoinactivation and early growth advantage, demonstrating that manipulating photoprotective mechanisms can impact both subcellular mechanisms and whole-canopy function.
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Affiliation(s)
- Chuan Ching Foo
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington, UK
| | - Alexandra J Burgess
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington, UK
| | - Renata Retkute
- Department of Plant Sciences, University of Cambridge, Cambridge, UK
| | - Pracha Tree-Intong
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington, UK
| | - Alexander V Ruban
- School of Biological and Chemical Sciences, Queen Mary University of London, London, UK
| | - Erik H Murchie
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington, UK
- Correspondence:
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15
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Gibbs JA, Pound MP, French AP, Wells DM, Murchie EH, Pridmore TP. Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1907-1917. [PMID: 31027044 DOI: 10.1109/tcbb.2019.2896908] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Plant phenotyping is the quantitative description of a plant's physiological, biochemical, and anatomical status which can be used in trait selection and helps to provide mechanisms to link underlying genetics with yield. Here, an active vision- based pipeline is presented which aims to contribute to reducing the bottleneck associated with phenotyping of architectural traits. The pipeline provides a fully automated response to photometric data acquisition and the recovery of three-dimensional (3D) models of plants without the dependency of botanical expertise, whilst ensuring a non-intrusive and non-destructive approach. Access to complete and accurate 3D models of plants supports computation of a wide variety of structural measurements. An Active Vision Cell (AVC) consisting of a camera-mounted robot arm plus combined software interface and a novel surface reconstruction algorithm is proposed. This pipeline provides a robust, flexible, and accurate method for automating the 3D reconstruction of plants. The reconstruction algorithm can reduce noise and provides a promising and extendable framework for high throughput phenotyping, improving current state-of-the-art methods. Furthermore, the pipeline can be applied to any plant species or form due to the application of an active vision framework combined with the automatic selection of key parameters for surface reconstruction.
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16
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Zhu B, Liu F, Xie Z, Guo Y, Li B, Ma Y. Quantification of light interception within image-based 3-D reconstruction of sole and intercropped canopies over the entire growth season. ANNALS OF BOTANY 2020; 126:701-712. [PMID: 32179920 PMCID: PMC7489074 DOI: 10.1093/aob/mcaa046] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Accepted: 03/12/2020] [Indexed: 05/27/2023]
Abstract
BACKGROUND AND AIMS Light interception is closely related to canopy architecture. Few studies based on multi-view photography have been conducted in a field environment, particularly studies that link 3-D plant architecture with a radiation model to quantify the dynamic canopy light interception. In this study, we combined realistic 3-D plant architecture with a radiation model to quantify and evaluate the effect of differences in planting patterns and row orientations on canopy light interception. METHODS The 3-D architectures of maize and soybean plants were reconstructed for sole crops and intercrops based on multi-view images obtained at five growth dates in the field. We evaluated the accuracy of the calculated leaf length, maximum leaf width, plant height and leaf area according to the measured data. The light distribution within the 3-D plant canopy was calculated with a 3-D radiation model. Finally, we evaluated canopy light interception in different row orientations. KEY RESULTS There was good agreement between the measured and calculated phenotypic traits, with an R2 >0.97. The light distribution was more uniform for intercropped maize and more concentrated for sole maize. At the maize silking stage, 85 % of radiation was intercepted by approx. 55 % of the upper canopy region for maize and by approx. 33 % of the upper canopy region for soybean. There was no significant difference in daily light interception between the different row orientations for the entire intercropping and sole systems. However, for intercropped maize, near east-west orientations showed approx. 19 % higher daily light interception than near south-north orientations. For intercropped soybean, daily light interception showed the opposite trend. It was approx. 49 % higher for near south-north orientations than for near east-west orientations. CONCLUSIONS The accurate reconstruction of 3-D plants grown in the field based on multi-view images provides the possibility for high-throughput 3-D phenotyping in the field and allows a better understanding of the relationship between canopy architecture and the light environment.
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Affiliation(s)
- Binglin Zhu
- Key Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Fusang Liu
- Key Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Ziwen Xie
- Key Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Yan Guo
- Key Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Baoguo Li
- Key Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Yuntao Ma
- Key Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, College of Land Science and Technology, China Agricultural University, Beijing, China
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17
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Song Q, Srinivasan V, Long SP, Zhu XG. Decomposition analysis on soybean productivity increase under elevated CO2 using 3-D canopy model reveals synergestic effects of CO2 and light in photosynthesis. ANNALS OF BOTANY 2020; 126:601-614. [PMID: 31638642 PMCID: PMC7489077 DOI: 10.1093/aob/mcz163] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 03/29/2019] [Accepted: 10/17/2019] [Indexed: 05/19/2023]
Abstract
BACKGROUND AND AIMS Understanding how climate change influences crop productivity helps in identifying new options to increase crop productivity. Soybean is the most important dicotyledonous seed crop in terms of planting area. Although the impacts of elevated atmospheric [CO2] on soybean physiology, growth and biomass accumulation have been studied extensively, the contribution of different factors to changes in season-long whole crop photosynthetic CO2 uptake [gross primary productivity (GPP)] under elevated [CO2] have not been fully quantified. METHODS A 3-D canopy model combining canopy 3-D architecture, ray tracing and leaf photosynthesis was built to: (1) study the impacts of elevated [CO2] on soybean GPP across a whole growing season; (2) dissect the contribution of different factors to changes in GPP; and (3) determine the extent, if any, of synergism between [CO2] and light on changes in GPP. The model was parameterized from measurements of leaf physiology and canopy architectural parameters at the soybean Free Air CO2 Enrichment (SoyFACE) facility in Champaign, Illinois. KEY RESULTS Using this model, we showed that both a CO2 fertilization effect and changes in canopy architecture contributed to the large increase in GPP while acclimation in photosynthetic physiological parameters to elevated [CO2] and altered leaf temperature played only a minor role in the changes in GPP. Furthermore, at early developmental stages, elevated [CO2] increased leaf area index which led to increased canopy light absorption and canopy photosynthesis. At later developmental stages, on days with high ambient light levels, the proportion of leaves in a canopy limited by Rubisco carboxylation increased from 12.2 % to 35.6 %, which led to a greater enhancement of elevated [CO2] to GPP. CONCLUSIONS This study develops a new method to dissect the contribution of different factors to responses of crops under climate change. We showed that there is a synergestic effect of CO2 and light on crop growth under elevated CO2 conditions.
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Affiliation(s)
- Qingfeng Song
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China
| | - Venkatraman Srinivasan
- Departments of Crop Sciences and of Plant Biology, Carl R. Woese Institute of Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India
| | - Steve P Long
- Departments of Crop Sciences and of Plant Biology, Carl R. Woese Institute of Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Lancaster Environment Center, Lancaster University, Lancaster, UK
| | - Xin-Guang Zhu
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China
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18
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Abstract
Wheat was one of the first grain crops domesticated by humans and remains among the major contributors to the global calorie and protein budget. The rapidly expanding world population demands further enhancement of yield and performance of wheat. Phenotypic information has historically been instrumental in wheat breeding for improved traits. In the last two decades, a steadily growing collection of tools and imaging software have given us the ability to quantify shoot, root, and seed traits with progressively increasing accuracy and throughput. This review discusses challenges and advancements in image analysis platforms for wheat phenotyping at the organ level. Perspectives on how these collective phenotypes can inform basic research on understanding wheat physiology and breeding for wheat improvement are also provided.
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19
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Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping. SENSORS 2020; 20:s20113150. [PMID: 32498361 PMCID: PMC7308841 DOI: 10.3390/s20113150] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 05/28/2020] [Accepted: 05/30/2020] [Indexed: 12/28/2022]
Abstract
This study aims to test the performances of a low-cost and automatic phenotyping platform, consisting of a Red-Green-Blue (RGB) commercial camera scanning objects on rotating plates and the reconstruction of main plant phenotypic traits via the structure for motion approach (SfM). The precision of this platform was tested in relation to three-dimensional (3D) models generated from images of potted maize, tomato and olive tree, acquired at a different frequency (steps of 4°, 8° and 12°) and quality (4.88, 6.52 and 9.77 µm/pixel). Plant and organs heights, angles and areas were extracted from the 3D models generated for each combination of these factors. Coefficient of determination (R2), relative Root Mean Square Error (rRMSE) and Akaike Information Criterion (AIC) were used as goodness-of-fit indexes to compare the simulated to the observed data. The results indicated that while the best performances in reproducing plant traits were obtained using 90 images at 4.88 µm/pixel (R2 = 0.81, rRMSE = 9.49% and AIC = 35.78), this corresponded to an unviable processing time (from 2.46 h to 28.25 h for herbaceous plants and olive trees, respectively). Conversely, 30 images at 4.88 µm/pixel resulted in a good compromise between a reliable reconstruction of considered traits (R2 = 0.72, rRMSE = 11.92% and AIC = 42.59) and processing time (from 0.50 h to 2.05 h for herbaceous plants and olive trees, respectively). In any case, the results pointed out that this input combination may vary based on the trait under analysis, which can be more or less demanding in terms of input images and time according to the complexity of its shape (R2 = 0.83, rRSME = 10.15% and AIC = 38.78). These findings highlight the reliability of the developed low-cost platform for plant phenotyping, further indicating the best combination of factors to speed up the acquisition and elaboration process, at the same time minimizing the bias between observed and simulated data.
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20
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Murchie EH, Ruban AV. Dynamic non-photochemical quenching in plants: from molecular mechanism to productivity. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 101:885-896. [PMID: 31686424 DOI: 10.1111/tpj.14601] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 10/18/2019] [Accepted: 10/28/2019] [Indexed: 05/02/2023]
Abstract
Photoprotection refers to a set of well defined plant processes that help to prevent the deleterious effects of high and excess light on plant cells, especially within the chloroplast. Molecular components of chloroplast photoprotection are closely aligned with those of photosynthesis and together they influence productivity. Proof of principle now exists that major photoprotective processes such as non-photochemical quenching (NPQ) directly determine whole canopy photosynthesis, biomass and yield via prevention of photoinhibition and a momentary downregulation of photosynthetic quantum yield. However, this phenomenon has neither been quantified nor well characterized across different environments. Here we address this problem by assessing the existing literature with a different approach to that taken previously, beginning with our understanding of the molecular mechanism of NPQ and its regulation within dynamic environments. We then move to the leaf and the plant level, building an understanding of the circumstances (when and where) NPQ limits photosynthesis and linking to our understanding of how this might take place on a molecular and metabolic level. We argue that such approaches are needed to fine tune the relevant features necessary for improving dynamic NPQ in important crop species.
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Affiliation(s)
- Erik H Murchie
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Sutton Bonington, Leicestershire, LE12 5RD, UK
| | - Alexander V Ruban
- School of Biological and Chemical Sciences, Queen Mary University of London, London, UK
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21
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Ward B, Brien C, Oakey H, Pearson A, Negrão S, Schilling RK, Taylor J, Jarvis D, Timmins A, Roy SJ, Tester M, Berger B, van den Hengel A. High-throughput 3D modelling to dissect the genetic control of leaf elongation in barley (Hordeum vulgare). THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 98:555-570. [PMID: 30604470 PMCID: PMC6850118 DOI: 10.1111/tpj.14225] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2018] [Revised: 12/17/2018] [Accepted: 12/19/2018] [Indexed: 05/11/2023]
Abstract
To optimize shoot growth and structure of cereals, we need to understand the genetic components controlling initiation and elongation. While measuring total shoot growth at high throughput using 2D imaging has progressed, recovering the 3D shoot structure of small grain cereals at a large scale is still challenging. Here, we present a method for measuring defined individual leaves of cereals, such as wheat and barley, using few images. Plant shoot modelling over time was used to measure the initiation and elongation of leaves in a bi-parental barley mapping population under low and high soil salinity. We detected quantitative trait loci (QTL) related to shoot growth per se, using both simple 2D total shoot measurements and our approach of measuring individual leaves. In addition, we detected QTL specific to leaf elongation and not to total shoot size. Of particular importance was the detection of a QTL on chromosome 3H specific to the early responses of leaf elongation to salt stress, a locus that could not be detected without the computer vision tools developed in this study.
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Affiliation(s)
- Ben Ward
- Australian Center for Visual TechnologiesUniversity of AdelaideAdelaideSA5005Australia
| | - Chris Brien
- Australian Plant Phenomics FacilityThe Plant AcceleratorSchool of Agriculture Food & WineUniversity of AdelaideUrrbraeSA5064Australia
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
- Phenomics and Bioinformatics Research CentreSchool of Information Technology and Mathematical SciencesUniversity of South AustraliaAdelaide5001Australia
| | - Helena Oakey
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
| | - Allison Pearson
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
- ARC Centre of Excellence in Plant Energy BiologyThe University of AdelaidePMB 1, Glen OsmondAdelaideSouth Australia5064Australia
- Australian Centre for Plant Functional GenomicsPMB 1, Glen OsmondAdelaideSouth Australia5064Australia
| | - Sónia Negrão
- Division of Biological and Environmental Sciences and Engineering (BESE)King Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Saudi Arabia
| | - Rhiannon K. Schilling
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
- Australian Centre for Plant Functional GenomicsPMB 1, Glen OsmondAdelaideSouth Australia5064Australia
| | - Julian Taylor
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
| | - David Jarvis
- Division of Biological and Environmental Sciences and Engineering (BESE)King Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Saudi Arabia
| | - Andy Timmins
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
- Australian Centre for Plant Functional GenomicsPMB 1, Glen OsmondAdelaideSouth Australia5064Australia
| | - Stuart J. Roy
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
- Australian Centre for Plant Functional GenomicsPMB 1, Glen OsmondAdelaideSouth Australia5064Australia
| | - Mark Tester
- Division of Biological and Environmental Sciences and Engineering (BESE)King Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Saudi Arabia
| | - Bettina Berger
- Australian Plant Phenomics FacilityThe Plant AcceleratorSchool of Agriculture Food & WineUniversity of AdelaideUrrbraeSA5064Australia
- School of Agriculture Food & Wine and Waite Research InstituteUniversity of AdelaideUrrbraeSA5064Australia
| | - Anton van den Hengel
- Australian Center for Visual TechnologiesUniversity of AdelaideAdelaideSA5005Australia
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Hassan MA, Yang M, Rasheed A, Yang G, Reynolds M, Xia X, Xiao Y, He Z. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2019; 282:95-103. [PMID: 31003615 DOI: 10.1016/j.plantsci.2018.10.022] [Citation(s) in RCA: 82] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 10/23/2018] [Accepted: 10/24/2018] [Indexed: 05/18/2023]
Abstract
Wheat improvement programs require rapid assessment of large numbers of individual plots across multiple environments. Vegetation indices (VIs) that are mainly associated with yield and yield-related physiological traits, and rapid evaluation of canopy normalized difference vegetation index (NDVI) can assist in-season selection. Multi-spectral imagery using unmanned aerial vehicles (UAV) can readily assess the VIs traits at various crop growth stages. Thirty-two wheat cultivars and breeding lines grown in limited irrigation and full irrigation treatments were investigated to monitor NDVI across the growth cycle using a Sequoia sensor mounted on a UAV. Significant correlations ranging from R2 = 0.38 to 0.90 were observed between NDVI detected from UAV and Greenseeker (GS) during stem elongation (SE) to late grain gilling (LGF) across the treatments. UAV-NDVI also had high heritabilities at SE (h2 = 0.91), flowering (F)(h2 = 0.95), EGF (h2 = 0.79) and mid grain filling (MGF) (h2 = 0.71) under the full irrigation treatment, and at booting (B) (h2 = 0.89), EGF (h2 = 0.75) in the limited irrigation treatment. UAV-NDVI explained significant variation in grain yield (GY) at EGF (R2 = 0.86), MGF (R2 = 0.83) and LGF (R2 = 0.89) stages, and results were consistent with GS-NDVI. Higher correlations between UAV-NDVI and GY were observed under full irrigation at three different grain-filling stages (R2 = 0.40, 0.49 and 0.45) than the limited irrigation treatment (R2 = 0.08, 0.12 and 0.14) and GY was calculated to be 24.4% lower under limited irrigation conditions. Pearson correlations between UAV-NDVI and GY were also low ranging from r = 0.29 to 0.37 during grain-filling under limited irrigation but higher than GS-NDVI data. A similar pattern was observed for normalized difference red-edge (NDRE) and normalized green red difference index (NGRDI) when correlated with GY. Fresh biomass estimated at late flowering stage had significant correlations of r = 0.30 to 0.51 with UAV-NDVI at EGF. Some genotypes Nongda 211, Nongda 5181, Zhongmai 175 and Zhongmai 12 were identified as high yielding genotypes using NDVI during grain-filling. In conclusion, a multispectral sensor mounted on a UAV is a reliable high-throughput platform for NDVI measurement to predict biomass and GY and grain-filling stage seems the best period for selection.
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Affiliation(s)
- Muhammad Adeel Hassan
- Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China
| | - Mengjiao Yang
- Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China; College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, China
| | - Awais Rasheed
- Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China; International Maize and Wheat Improvement Centre (CIMMYT) China Office, c/o CAAS, Beijing 100081, China
| | - Guijun Yang
- Beijing Research Centre for Information Technology in Agriculture, Beijing Academy of Agricultural and Forestry Sciences, China
| | - Matthew Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT), Apdo. Postal 6-641, 06600 Mexico DF, Mexico
| | - Xianchun Xia
- Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China
| | - Yonggui Xiao
- Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China.
| | - Zhonghu He
- Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China; International Maize and Wheat Improvement Centre (CIMMYT) China Office, c/o CAAS, Beijing 100081, China.
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Bernotas G, Scorza LCT, Hansen MF, Hales IJ, Halliday KJ, Smith LN, Smith ML, McCormick AJ. A photometric stereo-based 3D imaging system using computer vision and deep learning for tracking plant growth. Gigascience 2019; 8:giz056. [PMID: 31127811 PMCID: PMC6534809 DOI: 10.1093/gigascience/giz056] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2018] [Revised: 03/25/2019] [Accepted: 04/21/2019] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Tracking and predicting the growth performance of plants in different environments is critical for predicting the impact of global climate change. Automated approaches for image capture and analysis have allowed for substantial increases in the throughput of quantitative growth trait measurements compared with manual assessments. Recent work has focused on adopting computer vision and machine learning approaches to improve the accuracy of automated plant phenotyping. Here we present PS-Plant, a low-cost and portable 3D plant phenotyping platform based on an imaging technique novel to plant phenotyping called photometric stereo (PS). RESULTS We calibrated PS-Plant to track the model plant Arabidopsis thaliana throughout the day-night (diel) cycle and investigated growth architecture under a variety of conditions to illustrate the dramatic effect of the environment on plant phenotype. We developed bespoke computer vision algorithms and assessed available deep neural network architectures to automate the segmentation of rosettes and individual leaves, and extract basic and more advanced traits from PS-derived data, including the tracking of 3D plant growth and diel leaf hyponastic movement. Furthermore, we have produced the first PS training data set, which includes 221 manually annotated Arabidopsis rosettes that were used for training and data analysis (1,768 images in total). A full protocol is provided, including all software components and an additional test data set. CONCLUSIONS PS-Plant is a powerful new phenotyping tool for plant research that provides robust data at high temporal and spatial resolutions. The system is well-suited for small- and large-scale research and will help to accelerate bridging of the phenotype-to-genotype gap.
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Affiliation(s)
- Gytis Bernotas
- Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, T block, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, UK
| | - Livia C T Scorza
- SynthSys & Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, The King's Buildings, Edinburgh EH9 3BF, UK
| | - Mark F Hansen
- Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, T block, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, UK
| | - Ian J Hales
- Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, T block, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, UK
| | - Karen J Halliday
- SynthSys & Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, The King's Buildings, Edinburgh EH9 3BF, UK
| | - Lyndon N Smith
- Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, T block, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, UK
| | - Melvyn L Smith
- Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, T block, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, UK
| | - Alistair J McCormick
- SynthSys & Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, The King's Buildings, Edinburgh EH9 3BF, UK
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24
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Chen TW, Cabrera-Bosquet L, Alvarez Prado S, Perez R, Artzet S, Pradal C, Coupel-Ledru A, Fournier C, Tardieu F. Genetic and environmental dissection of biomass accumulation in multi-genotype maize canopies. JOURNAL OF EXPERIMENTAL BOTANY 2019; 70:2523-2534. [PMID: 30137451 PMCID: PMC6487589 DOI: 10.1093/jxb/ery309] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 08/14/2018] [Indexed: 05/22/2023]
Abstract
Multi-genotype canopies are frequent in phenotyping experiments and are of increasing interest in agriculture. Radiation interception efficiency (RIE) and radiation use efficiency (RUE) have low heritabilities in such canopies. We propose a revised Monteith equation that identifies environmental and genetic components of RIE and RUE. An environmental term, a component of RIE, characterizes the effect of the presence or absence of neighbours on light interception. The ability of a given plant to compete with its neighbours is then identified, which accounts for the genetic variability of RIE of plants having similar leaf areas. This method was used in three experiments in a phenotyping platform with 765 plants of 255 maize hybrids. As expected, the heritability of the environmental term was near zero, whereas that of the competitiveness term increased with phenological stage, resulting in the identification of quantitative trait loci. In the same way, RUE was dissected as an effect of intercepted light and a genetic term. This approach was used for predicting the behaviour of individual genotypes in virtual multi-genotype canopies. A large effect of competitiveness was observed in multi-genotype but not in single-genotype canopies, resulting in a bias for genotype comparisons in breeding fields.
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Affiliation(s)
- Tsu-Wei Chen
- Université de Montpellier, INRA, LEPSE, Montpellier, France
| | | | | | - Raphaël Perez
- Université de Montpellier, INRA, LEPSE, Montpellier, France
| | - Simon Artzet
- Université de Montpellier, INRA, LEPSE, Montpellier, France
| | | | - Aude Coupel-Ledru
- Université de Montpellier, INRA, LEPSE, Montpellier, France
- CIRAD, UMR AGAP, Montpellier, France
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25
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Burgess AJ, Gibbs JA, Murchie EH. A canopy conundrum: can wind-induced movement help to increase crop productivity by relieving photosynthetic limitations? JOURNAL OF EXPERIMENTAL BOTANY 2019; 70:2371-2380. [PMID: 30481324 DOI: 10.1093/jxb/ery424] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 11/19/2018] [Indexed: 05/12/2023]
Abstract
Wind-induced movement is a ubiquitous occurrence for all plants grown in natural or agricultural settings, and in the context of high, damaging wind speeds it has been well studied. However, the impact of lower wind speeds (which do not cause any damage) on mode of movement, light transmission, and photosynthetic properties has, surprisingly, not been fully explored. This impact is likely to be influenced by biomechanical properties and architectural features of the plant and canopy. A limited number of eco-physiological studies have indicated that movement in wind has the potential to alter light distribution within canopies, improving canopy productivity by relieving photosynthetic limitations. Given the current interest in canopy photosynthesis, it is timely to consider such movement in terms of crop yield progress. This opinion article sets out the background to wind-induced crop movement and argues that plant biomechanical properties may have a role in the optimization of whole-canopy photosynthesis via established physiological processes. We discuss how this could be achieved using canopy models.
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Affiliation(s)
- Alexandra J Burgess
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington Campus, UK
| | - Jonathon A Gibbs
- School of Computer Science, University of Nottingham, Jubilee Campus, UK
| | - Erik H Murchie
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington Campus, UK
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26
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Wu S, Wen W, Xiao B, Guo X, Du J, Wang C, Wang Y. An Accurate Skeleton Extraction Approach From 3D Point Clouds of Maize Plants. FRONTIERS IN PLANT SCIENCE 2019; 10:248. [PMID: 30899271 PMCID: PMC6416182 DOI: 10.3389/fpls.2019.00248] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 02/14/2019] [Indexed: 05/27/2023]
Abstract
Accurate and high-throughput determination of plant morphological traits is essential for phenotyping studies. Nowadays, there are many approaches to acquire high-quality three-dimensional (3D) point clouds of plants. However, it is difficult to estimate phenotyping parameters accurately of the whole growth stages of maize plants using these 3D point clouds. In this paper, an accurate skeleton extraction approach was proposed to bridge the gap between 3D point cloud and phenotyping traits estimation of maize plants. The algorithm first uses point cloud clustering and color difference denoising to reduce the noise of the input point clouds. Next, the Laplacian contraction algorithm is applied to shrink the points. Then the key points representing the skeleton of the plant are selected through adaptive sampling, and neighboring points are connected to form a plant skeleton composed of semantic organs. Finally, deviation skeleton points to the input point cloud are calibrated by building a step forward local coordinate along the tangent direction of the original points. The proposed approach successfully generates accurately extracted skeleton from 3D point cloud and helps to estimate phenotyping parameters with high precision of maize plants. Experimental verification of the skeleton extraction process, tested using three cultivars and different growth stages maize, demonstrates that the extracted matches the input point cloud well. Compared with 3D digitizing data-derived morphological parameters, the NRMSE of leaf length, leaf inclination angle, leaf top length, leaf azimuthal angle, leaf growth height, and plant height, estimated using the extracted plant skeleton, are 5.27, 8.37, 5.12, 4.42, 1.53, and 0.83%, respectively, which could meet the needs of phenotyping analysis. The time required to process a single maize plant is below 100 s. The proposed approach may play an important role in further maize research and applications, such as genotype-to-phenotype study, geometric reconstruction, functional structural maize modeling, and dynamic growth animation.
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Affiliation(s)
- Sheng Wu
- Beijing Research Center for Information Technology in Agriculture, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Weiliang Wen
- Beijing Research Center for Information Technology in Agriculture, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Boxiang Xiao
- Beijing Research Center for Information Technology in Agriculture, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Xinyu Guo
- Beijing Research Center for Information Technology in Agriculture, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Jianjun Du
- Beijing Research Center for Information Technology in Agriculture, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Chuanyu Wang
- Beijing Research Center for Information Technology in Agriculture, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Yongjian Wang
- Beijing Research Center for Information Technology in Agriculture, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
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27
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Bolger AM, Poorter H, Dumschott K, Bolger ME, Arend D, Osorio S, Gundlach H, Mayer KFX, Lange M, Scholz U, Usadel B. Computational aspects underlying genome to phenome analysis in plants. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 97:182-198. [PMID: 30500991 PMCID: PMC6849790 DOI: 10.1111/tpj.14179] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 11/06/2018] [Accepted: 11/16/2018] [Indexed: 05/18/2023]
Abstract
Recent advances in genomics technologies have greatly accelerated the progress in both fundamental plant science and applied breeding research. Concurrently, high-throughput plant phenotyping is becoming widely adopted in the plant community, promising to alleviate the phenotypic bottleneck. While these technological breakthroughs are significantly accelerating quantitative trait locus (QTL) and causal gene identification, challenges to enable even more sophisticated analyses remain. In particular, care needs to be taken to standardize, describe and conduct experiments robustly while relying on plant physiology expertise. In this article, we review the state of the art regarding genome assembly and the future potential of pangenomics in plant research. We also describe the necessity of standardizing and describing phenotypic studies using the Minimum Information About a Plant Phenotyping Experiment (MIAPPE) standard to enable the reuse and integration of phenotypic data. In addition, we show how deep phenotypic data might yield novel trait-trait correlations and review how to link phenotypic data to genomic data. Finally, we provide perspectives on the golden future of machine learning and their potential in linking phenotypes to genomic features.
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Affiliation(s)
- Anthony M. Bolger
- Institute for Biology I, BioSCRWTH Aachen UniversityWorringer Weg 352074AachenGermany
| | - Hendrik Poorter
- Forschungszentrum Jülich (FZJ) Institute of Bio‐ and Geosciences (IBG‐2) Plant SciencesWilhelm‐Johnen‐Straße52428JülichGermany
- Department of Biological SciencesMacquarie UniversityNorth RydeNSW2109Australia
| | - Kathryn Dumschott
- Institute for Biology I, BioSCRWTH Aachen UniversityWorringer Weg 352074AachenGermany
| | - Marie E. Bolger
- Forschungszentrum Jülich (FZJ) Institute of Bio‐ and Geosciences (IBG‐2) Plant SciencesWilhelm‐Johnen‐Straße52428JülichGermany
| | - Daniel Arend
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) GaterslebenCorrensstraße 306466SeelandGermany
| | - Sonia Osorio
- Department of Molecular Biology and BiochemistryInstituto de Hortofruticultura Subtropical y Mediterránea “La Mayora”Universidad de Málaga‐Consejo Superior de Investigaciones CientíficasCampus de Teatinos29071MálagaSpain
| | - Heidrun Gundlach
- Plant Genome and Systems Biology (PGSB)Helmholtz Zentrum München (HMGU)Ingolstädter Landstraße 185764NeuherbergGermany
| | - Klaus F. X. Mayer
- Plant Genome and Systems Biology (PGSB)Helmholtz Zentrum München (HMGU)Ingolstädter Landstraße 185764NeuherbergGermany
| | - Matthias Lange
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) GaterslebenCorrensstraße 306466SeelandGermany
| | - Uwe Scholz
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) GaterslebenCorrensstraße 306466SeelandGermany
| | - Björn Usadel
- Institute for Biology I, BioSCRWTH Aachen UniversityWorringer Weg 352074AachenGermany
- Forschungszentrum Jülich (FZJ) Institute of Bio‐ and Geosciences (IBG‐2) Plant SciencesWilhelm‐Johnen‐Straße52428JülichGermany
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28
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Multi-scale 3D Data Acquisition of Maize. COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE XI 2019. [DOI: 10.1007/978-3-030-06137-1_11] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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29
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Zhang Y, Du J, Wang J, Ma L, Lu X, Pan X, Guo X, Zhao C. High-throughput micro-phenotyping measurements applied to assess stalk lodging in maize (Zea mays L.). Biol Res 2018; 51:40. [PMID: 30368254 PMCID: PMC6203980 DOI: 10.1186/s40659-018-0190-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Accepted: 10/09/2018] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND The biomechanical properties of maize stalks largely determine their lodging resistance, which affects crop yield per unit area. However, the quantitative and qualitative relationship between micro-phenotypes and the biomechanics of maize stalks is still under examined. In particular, the roles of the number, geometry, and distribution of vascular bundles of stalks in maize lodging resistance remain unclear. Research on these biomechanical properties will benefit from high-resolution micro-phenotypic image acquisition capabilities, which have been improved by modern X-ray imaging devices such as micro-CT and the development of micro-phenotyping analysis software. Hence, high-throughput image analysis and accurate quantification of anatomical phenotypes of stalks are necessary. RESULTS We have updated VesselParser version 1.0 to version 2.0 and have improved its performance, accuracy, and computation strategies. Anatomical characteristics of the second and third stalk internodes of the cultivars 'Jingke968' and 'Jingdan38' were analyzed using VesselParser 2.0. The relationships between lodging resistance and anatomical phenotypes of stalks between the two different maize varieties were investigated. The total area of vascular bundles in the peripheral layer, auxiliary axis diameter, and total area of vascular bundles were revealed to have the highest correlation with mechanical properties, and anatomical phenotypes of maize stalk were better predictors of mechanical properties than macro features observed optically from direct measurement, such as diameter and perimeter. CONCLUSIONS This study demonstrates the utility of VesselParser 2.0 in assessing stalk mechanical properties. The combination of anatomical phenotypes and mechanical behavior research provides unique insights into the problem of stalk lodging, showing that micro phenotypes of vascular bundles are good predictors of maize stalk mechanical properties that may be important indices for the evaluation and identification of the biomechanical properties to improve lodging resistance of future maize varieties.
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Affiliation(s)
- Ying Zhang
- Beijing Key Lab of Digital Plant, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Shuguang Huayuan Middle Road, Haidian District, No. 11, Beijing, 100097 People’s Republic of China
| | - Jianjun Du
- Beijing Key Lab of Digital Plant, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Shuguang Huayuan Middle Road, Haidian District, No. 11, Beijing, 100097 People’s Republic of China
| | - Jinglu Wang
- Beijing Key Lab of Digital Plant, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Shuguang Huayuan Middle Road, Haidian District, No. 11, Beijing, 100097 People’s Republic of China
| | - Liming Ma
- Beijing Key Lab of Digital Plant, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Shuguang Huayuan Middle Road, Haidian District, No. 11, Beijing, 100097 People’s Republic of China
| | - Xianju Lu
- Beijing Key Lab of Digital Plant, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Shuguang Huayuan Middle Road, Haidian District, No. 11, Beijing, 100097 People’s Republic of China
| | - Xiaodi Pan
- Beijing Key Lab of Digital Plant, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Shuguang Huayuan Middle Road, Haidian District, No. 11, Beijing, 100097 People’s Republic of China
| | - Xinyu Guo
- Beijing Key Lab of Digital Plant, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Shuguang Huayuan Middle Road, Haidian District, No. 11, Beijing, 100097 People’s Republic of China
| | - Chunjiang Zhao
- Beijing Key Lab of Digital Plant, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Shuguang Huayuan Middle Road, Haidian District, No. 11, Beijing, 100097 People’s Republic of China
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30
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Gibbs JA, Pound M, French AP, Wells DM, Murchie E, Pridmore T. Plant Phenotyping: An Active Vision Cell for Three-Dimensional Plant Shoot Reconstruction. PLANT PHYSIOLOGY 2018; 178:524-534. [PMID: 30097468 PMCID: PMC6181042 DOI: 10.1104/pp.18.00664] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Accepted: 07/27/2018] [Indexed: 05/18/2023]
Abstract
Three-dimensional (3D) computer-generated models of plants are urgently needed to support both phenotyping and simulation-based studies such as photosynthesis modeling. However, the construction of accurate 3D plant models is challenging, as plants are complex objects with an intricate leaf structure, often consisting of thin and highly reflective surfaces that vary in shape and size, forming dense, complex, crowded scenes. We address these issues within an image-based method by taking an active vision approach, one that investigates the scene to intelligently capture images, to image acquisition. Rather than use the same camera positions for all plants, our technique is to acquire the images needed to reconstruct the target plant, tuning camera placement to match the plant's individual structure. Our method also combines volumetric- and surface-based reconstruction methods and determines the necessary images based on the analysis of voxel clusters. We describe a fully automatic plant modeling/phenotyping cell (or module) comprising a six-axis robot and a high-precision turntable. By using a standard color camera, we overcome the difficulties associated with laser-based plant reconstruction methods. The 3D models produced are compared with those obtained from fixed cameras and evaluated by comparison with data obtained by x-ray microcomputed tomography across different plant structures. Our results show that our method is successful in improving the accuracy and quality of data obtained from a variety of plant types.
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Affiliation(s)
- Jonathon A Gibbs
- School of Computer Science, University of Nottingham, Jubilee Campus, Nottingham NG8 1BB, United Kingdom
| | - Michael Pound
- School of Computer Science, University of Nottingham, Jubilee Campus, Nottingham NG8 1BB, United Kingdom
| | - Andrew P French
- School of Computer Science, University of Nottingham, Jubilee Campus, Nottingham NG8 1BB, United Kingdom
| | - Darren M Wells
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Sutton Bonington, Leicestershire LE12 5RD, United Kingdom
| | - Erik Murchie
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Sutton Bonington, Leicestershire LE12 5RD, United Kingdom
| | - Tony Pridmore
- School of Computer Science, University of Nottingham, Jubilee Campus, Nottingham NG8 1BB, United Kingdom
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31
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Three-Dimensional Reconstruction of Soybean Canopies Using Multisource Imaging for Phenotyping Analysis. REMOTE SENSING 2018. [DOI: 10.3390/rs10081206] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Geometric three-dimensional (3D) reconstruction has emerged as a powerful tool for plant phenotyping and plant breeding. Although laser scanning is one of the most intensely used sensing techniques for 3D reconstruction projects, it still has many limitations, such as the high investment cost. To overcome such limitations, in the present study, a low-cost, novel, and efficient imaging system consisting of a red-green-blue (RGB) camera and a photonic mixer detector (PMD) was developed, and its usability for plant phenotyping was demonstrated via a 3D reconstruction of a soybean plant that contains color information. To reconstruct soybean canopies, a density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to extract canopy information from the raw 3D point cloud. Principal component analysis (PCA) and iterative closest point (ICP) algorithms were then used to register the multisource images for the 3D reconstruction of a soybean plant from both the side and top views. We then assessed phenotypic traits such as plant height and the greenness index based on the deviations of test samples. The results showed that compared with manual measurements, the side view-based assessments yielded a determination coefficient (R2) of 0.9890 for the estimation of soybean height and a R2 of 0.6059 for the estimation of soybean canopy greenness index; the top view-based assessment yielded a R2 of 0.9936 for the estimation of soybean height and a R2 of 0.8864 for the estimation of soybean canopy greenness. Together, the results indicated that an assembled 3D imaging device applying the algorithms developed in this study could be used as a reliable and robust platform for plant phenotyping, and potentially for automated and high-throughput applications under both natural light and indoor conditions.
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32
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Retkute R, Townsend AJ, Murchie EH, Jensen OE, Preston SP. Three-dimensional plant architecture and sunlit-shaded patterns: a stochastic model of light dynamics in canopies. ANNALS OF BOTANY 2018; 122:291-302. [PMID: 29846520 PMCID: PMC6070062 DOI: 10.1093/aob/mcy067] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 04/17/2018] [Indexed: 05/06/2023]
Abstract
Background and Aims Diurnal changes in solar position and intensity combined with the structural complexity of plant architecture result in highly variable and dynamic light patterns within the plant canopy. This affects productivity through the complex ways that photosynthesis responds to changes in light intensity. Current methods to characterize light dynamics, such as ray-tracing, are able to produce data with excellent spatio-temporal resolution but are computationally intensive and the resulting data are complex and high-dimensional. This necessitates development of more economical models for summarizing the data and for simulating realistic light patterns over the course of a day. Methods High-resolution reconstructions of field-grown plants are assembled in various configurations to form canopies, and a forward ray-tracing algorithm is applied to the canopies to compute light dynamics at high (1 min) temporal resolution. From the ray-tracer output, the sunlit or shaded state for each patch on the plants is determined, and these data are used to develop a novel stochastic model for the sunlit-shaded patterns. The model is designed to be straightforward to fit to data using maximum likelihood estimation, and fast to simulate from. Key Results For a wide range of contrasting 3-D canopies, the stochastic model is able to summarize, and replicate in simulations, key features of the light dynamics. When light patterns simulated from the stochastic model are used as input to a model of photoinhibition, the predicted reduction in carbon gain is similar to that from calculations based on the (extremely costly) ray-tracer data. Conclusions The model provides a way to summarize highly complex data in a small number of parameters, and a cost-effective way to simulate realistic light patterns. Simulations from the model will be particularly useful for feeding into larger-scale photosynthesis models for calculating how light dynamics affects the photosynthetic productivity of canopies.
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Affiliation(s)
- Renata Retkute
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington campus, Loughborough, UK
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences, University of Warwick, Coventry, UK
| | - Alexandra J Townsend
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington campus, Loughborough, UK
| | - Erik H Murchie
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington campus, Loughborough, UK
| | - Oliver E Jensen
- School of Mathematics, University of Manchester, Oxford Road, Manchester, UK
| | - Simon P Preston
- School of Mathematical Sciences, University of Nottingham, Nottingham, UK
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Murchie EH, Kefauver S, Araus JL, Muller O, Rascher U, Flood PJ, Lawson T. Measuring the dynamic photosynthome. ANNALS OF BOTANY 2018; 122:207-220. [PMID: 29873681 PMCID: PMC6070037 DOI: 10.1093/aob/mcy087] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Accepted: 05/02/2018] [Indexed: 05/18/2023]
Abstract
Background Photosynthesis underpins plant productivity and yet is notoriously sensitive to small changes in environmental conditions, meaning that quantitation in nature across different time scales is not straightforward. The 'dynamic' changes in photosynthesis (i.e. the kinetics of the various reactions of photosynthesis in response to environmental shifts) are now known to be important in driving crop yield. Scope It is known that photosynthesis does not respond in a timely manner, and even a small temporal 'mismatch' between a change in the environment and the appropriate response of photosynthesis toward optimality can result in a fall in productivity. Yet the most commonly measured parameters are still made at steady state or a temporary steady state (including those for crop breeding purposes), meaning that new photosynthetic traits remain undiscovered. Conclusions There is a great need to understand photosynthesis dynamics from a mechanistic and biological viewpoint especially when applied to the field of 'phenomics' which typically uses large genetically diverse populations of plants. Despite huge advances in measurement technology in recent years, it is still unclear whether we possess the capability of capturing and describing the physiologically relevant dynamic features of field photosynthesis in sufficient detail. Such traits are highly complex, hence we dub this the 'photosynthome'. This review sets out the state of play and describes some approaches that could be made to address this challenge with reference to the relevant biological processes involved.
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Affiliation(s)
- Erik H Murchie
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington, UK
| | - Shawn Kefauver
- Section of Plant Physiology, Faculty of Biology, University of Barcelona, Barcelona, Spain
| | - Jose Luis Araus
- Section of Plant Physiology, Faculty of Biology, University of Barcelona, Barcelona, Spain
| | - Onno Muller
- Institute of Bio-and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Uwe Rascher
- Institute of Bio-and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Pádraic J Flood
- Max Planck Institute for Plant Breeding Research, Carl-Von-Linne-Weg, Köln, Germany
| | - Tracy Lawson
- School of Biological Sciences, University of Essex, Colchester, UK
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Tardieu F, Cabrera-Bosquet L, Pridmore T, Bennett M. Plant Phenomics, From Sensors to Knowledge. Curr Biol 2018; 27:R770-R783. [PMID: 28787611 DOI: 10.1016/j.cub.2017.05.055] [Citation(s) in RCA: 226] [Impact Index Per Article: 37.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Major improvements in crop yield are needed to keep pace with population growth and climate change. While plant breeding efforts have greatly benefited from advances in genomics, profiling the crop phenome (i.e., the structure and function of plants) associated with allelic variants and environments remains a major technical bottleneck. Here, we review the conceptual and technical challenges facing plant phenomics. We first discuss how, given plants' high levels of morphological plasticity, crop phenomics presents distinct challenges compared with studies in animals. Next, we present strategies for multi-scale phenomics, and describe how major improvements in imaging, sensor technologies and data analysis are now making high-throughput root, shoot, whole-plant and canopy phenomic studies possible. We then suggest that research in this area is entering a new stage of development, in which phenomic pipelines can help researchers transform large numbers of images and sensor data into knowledge, necessitating novel methods of data handling and modelling. Collectively, these innovations are helping accelerate the selection of the next generation of crops more sustainable and resilient to climate change, and whose benefits promise to scale from physiology to breeding and to deliver real world impact for ongoing global food security efforts.
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Affiliation(s)
- François Tardieu
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, F34060, Montpellier, France.
| | - Llorenç Cabrera-Bosquet
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, F34060, Montpellier, France
| | - Tony Pridmore
- School of Computer Science, University of Nottingham, NG8 1BB, UK
| | - Malcolm Bennett
- Plant & Crop Sciences, School of Biosciences, University of Nottingham, LE12 3RD, UK.
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Wen W, Li B, Li BJ, Guo X. A Leaf Modeling and Multi-Scale Remeshing Method for Visual Computation via Hierarchical Parametric Vein and Margin Representation. FRONTIERS IN PLANT SCIENCE 2018; 9:783. [PMID: 29997632 PMCID: PMC6029520 DOI: 10.3389/fpls.2018.00783] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Accepted: 05/23/2018] [Indexed: 05/29/2023]
Abstract
This paper introduces a novel hierarchical structured representation for leaf modeling and proposes a corresponding multi-resolution remeshing method for large-scale visual computation. Leaf modeling is a very difficult and challenging problem due to the wide variations in the shape and structures among different species of plants. Firstly, we introduce a Hierarchical Parametric Veins and Margin (HPVM) representation approach, which describes the leaf biological structures and exact geometry via interpolation of parametric curves from the extracted vein features from non-manifold data. Secondly, a parametric surface model is constructed using HPVM with geometric and structured constraints. Finally, for a given size, we adapt a multi-step discrete point resampling strategy and a CDT-based (Constrained Delaunay Triangulation) meshing method to generate a mesh model. Our representation consists of three coupled data structures, a core hierarchical parametric data structure of veins and margin for the leaf skeleton, the corresponding parametric surface model, and a set of unstructured triangular meshes with user-specified density for the leaf membrane. Numerical experiments show that our method can obtain high quality meshes from the scanned non-manifold mesh data with well-preserved biological structures and geometry. This novel approach is suitable for effective leaf simulation, rendering, texture mapping, and simulation of light distribution in crop canopies.
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Affiliation(s)
- Weiliang Wen
- Beijing Research Center for Information Technology in Agriculture, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Baojun Li
- Faculty of Vehicle Engineering and Mechanics, School of Automotive Engineering, Dalian University of Technology, Dalian, China
| | - Bao-jun Li
- Faculty of Vehicle Engineering and Mechanics, School of Automotive Engineering, Dalian University of Technology, Dalian, China
| | - Xinyu Guo
- Beijing Research Center for Information Technology in Agriculture, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
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Chopin J, Kumar P, Miklavcic SJ. Land-based crop phenotyping by image analysis: consistent canopy characterization from inconsistent field illumination. PLANT METHODS 2018; 14:39. [PMID: 29849745 PMCID: PMC5970541 DOI: 10.1186/s13007-018-0308-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Accepted: 05/18/2018] [Indexed: 05/08/2023]
Abstract
BACKGROUND One of the main challenges associated with image-based field phenotyping is the variability of illumination. During a single day's imaging session, or between different sessions on different days, the sun moves in and out of cloud cover and has varying intensity. How is one to know from consecutive images alone if a plant has become darker over time, or if the weather conditions have simply changed from clear to overcast? This is a significant problem to address as colour is an important phenotypic trait that can be measured automatically from images. RESULTS In this work we use an industry standard colour checker to balance the colour in images within and across every day of a field trial conducted over four months in 2016. By ensuring that the colour checker is present in every image we are afforded a 'ground truth' to correct for varying illumination conditions across images. We employ a least squares approach to fit a quadratic model for correcting RGB values of an image in such a way that the observed values of the colour checker tiles align with their true values after the transformation. CONCLUSIONS The proposed method is successful in reducing the error between observed and reference colour chart values in all images. Furthermore, the standard deviation of mean canopy colour across multiple days is reduced significantly after colour correction is applied. Finally, we use a number of examples to demonstrate the usefulness of accurate colour measurements in recording phenotypic traits and analysing variation among varieties and treatments.
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Affiliation(s)
- Joshua Chopin
- Phenomics and Bioinformatics Research Centre, University of South Australia, Mawson Lakes, 5095 Australia
| | - Pankaj Kumar
- Phenomics and Bioinformatics Research Centre, University of South Australia, Mawson Lakes, 5095 Australia
| | - Stanley J. Miklavcic
- Phenomics and Bioinformatics Research Centre, University of South Australia, Mawson Lakes, 5095 Australia
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Das Choudhury S, Bashyam S, Qiu Y, Samal A, Awada T. Holistic and component plant phenotyping using temporal image sequence. PLANT METHODS 2018; 14:35. [PMID: 29760766 PMCID: PMC5944015 DOI: 10.1186/s13007-018-0303-x] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 04/26/2018] [Indexed: 05/24/2023]
Abstract
BACKGROUND Image-based plant phenotyping facilitates the extraction of traits noninvasively by analyzing large number of plants in a relatively short period of time. It has the potential to compute advanced phenotypes by considering the whole plant as a single object (holistic phenotypes) or as individual components, i.e., leaves and the stem (component phenotypes), to investigate the biophysical characteristics of the plants. The emergence timing, total number of leaves present at any point of time and the growth of individual leaves during vegetative stage life cycle of the maize plants are significant phenotypic expressions that best contribute to assess the plant vigor. However, image-based automated solution to this novel problem is yet to be explored. RESULTS A set of new holistic and component phenotypes are introduced in this paper. To compute the component phenotypes, it is essential to detect the individual leaves and the stem. Thus, the paper introduces a novel method to reliably detect the leaves and the stem of the maize plants by analyzing 2-dimensional visible light image sequences captured from the side using a graph based approach. The total number of leaves are counted and the length of each leaf is measured for all images in the sequence to monitor leaf growth. To evaluate the performance of the proposed algorithm, we introduce University of Nebraska-Lincoln Component Plant Phenotyping Dataset (UNL-CPPD) and provide ground truth to facilitate new algorithm development and uniform comparison. The temporal variation of the component phenotypes regulated by genotypes and environment (i.e., greenhouse) are experimentally demonstrated for the maize plants on UNL-CPPD. Statistical models are applied to analyze the greenhouse environment impact and demonstrate the genetic regulation of the temporal variation of the holistic phenotypes on the public dataset called Panicoid Phenomap-1. CONCLUSION The central contribution of the paper is a novel computer vision based algorithm for automated detection of individual leaves and the stem to compute new component phenotypes along with a public release of a benchmark dataset, i.e., UNL-CPPD. Detailed experimental analyses are performed to demonstrate the temporal variation of the holistic and component phenotypes in maize regulated by environment and genetic variation with a discussion on their significance in the context of plant science.
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Affiliation(s)
- Sruti Das Choudhury
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE USA
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE USA
| | - Srinidhi Bashyam
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE USA
| | - Yumou Qiu
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE USA
| | - Ashok Samal
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE USA
| | - Tala Awada
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE USA
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Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges. AGRONOMY-BASEL 2018. [DOI: 10.3390/agronomy8040057] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Hui F, Zhu J, Hu P, Meng L, Zhu B, Guo Y, Li B, Ma Y. Image-based dynamic quantification and high-accuracy 3D evaluation of canopy structure of plant populations. ANNALS OF BOTANY 2018; 121:1079-1088. [PMID: 29509841 PMCID: PMC5906925 DOI: 10.1093/aob/mcy016] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2017] [Accepted: 01/24/2018] [Indexed: 05/24/2023]
Abstract
BACKGROUND AND AIMS Global agriculture is facing the challenge of a phenotyping bottleneck due to large-scale screening/breeding experiments with improved breeds. Phenotypic analysis with high-throughput, high-accuracy and low-cost technologies has therefore become urgent. Recent advances in image-based 3D reconstruction offer the opportunity of high-throughput phenotyping. The main aim of this study was to quantify and evaluate the canopy structure of plant populations in two and three dimensions based on the multi-view stereo (MVS) approach, and to monitor plant growth and development from seedling stage to fruiting stage. METHODS Multi-view images of flat-leaf cucumber, small-leaf pepper and curly-leaf eggplant were obtained by moving a camera around the plant canopy. Three-dimensional point clouds were reconstructed from images based on the MVS approach and were then converted into surfaces with triangular facets. Phenotypic parameters, including leaf length, leaf width, leaf area, plant height and maximum canopy width, were calculated from reconstructed surfaces. Accurate evaluation in 2D and 3D for individual leaves was performed by comparing reconstructed phenotypic parameters with referenced values and by calculating the Hausdorff distance, i.e. the mean distance between two surfaces. KEY RESULTS Our analysis demonstrates that there were good agreements in leaf parameters between referenced and estimated values. A high level of overlap was also found between surfaces of image-based reconstructions and laser scanning. Accuracy of 3D reconstruction of curly-leaf plants was relatively lower than that of flat-leaf plants. Plant height of three plants and maximum canopy width of cucumber and pepper showed an increasing trend during the 70 d after transplanting. Maximum canopy width of eggplants reached its peak at the 40th day after transplanting. The larger leaf phenotypic parameters of cucumber were mostly found at the middle-upper leaf position. CONCLUSIONS High-accuracy 3D evaluation of reconstruction quality indicated that dynamic capture of the 3D canopy based on the MVS approach can be potentially used in 3D phenotyping for applications in breeding and field management.
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Affiliation(s)
- Fang Hui
- Key Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
| | - Jinyu Zhu
- Institute of Vegetables and Flowers, Chinese Academy of Agricultural Science, Beijing, China
| | - Pengcheng Hu
- Key Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
| | - Lei Meng
- Department of Geography and Institute of the Environment and Sustainability, Western Michigan University, Kalamazoo, MI, USA
| | - Binglin Zhu
- Key Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
| | - Yan Guo
- Key Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
| | - Baoguo Li
- Key Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
| | - Yuntao Ma
- Key Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
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Thapa S, Zhu F, Walia H, Yu H, Ge Y. A Novel LiDAR-Based Instrument for High-Throughput, 3D Measurement of Morphological Traits in Maize and Sorghum. SENSORS 2018; 18:s18041187. [PMID: 29652788 PMCID: PMC5948551 DOI: 10.3390/s18041187] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 04/09/2018] [Accepted: 04/10/2018] [Indexed: 11/17/2022]
Abstract
Recently, imaged-based approaches have developed rapidly for high-throughput plant phenotyping (HTPP). Imaging reduces a 3D plant into 2D images, which makes the retrieval of plant morphological traits challenging. We developed a novel LiDAR-based phenotyping instrument to generate 3D point clouds of single plants. The instrument combined a LiDAR scanner with a precision rotation stage on which an individual plant was placed. A LabVIEW program was developed to control the scanning and rotation motion, synchronize the measurements from both devices, and capture a 360° view point cloud. A data processing pipeline was developed for noise removal, voxelization, triangulation, and plant leaf surface reconstruction. Once the leaf digital surfaces were reconstructed, plant morphological traits, including individual and total leaf area, leaf inclination angle, and leaf angular distribution, were derived. The system was tested with maize and sorghum plants. The results showed that leaf area measurements by the instrument were highly correlated with the reference methods (R2 > 0.91 for individual leaf area; R2 > 0.95 for total leaf area of each plant). Leaf angular distributions of the two species were also derived. This instrument could fill a critical technological gap for indoor HTPP of plant morphological traits in 3D.
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Affiliation(s)
- Suresh Thapa
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583, USA.
| | - Feiyu Zhu
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.
| | - Harkamal Walia
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA.
| | - Hongfeng Yu
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.
| | - Yufeng Ge
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583, USA.
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Hubbart S, Smillie IRA, Heatley M, Swarup R, Foo CC, Zhao L, Murchie EH. Enhanced thylakoid photoprotection can increase yield and canopy radiation use efficiency in rice. Commun Biol 2018; 1:22. [PMID: 30271909 PMCID: PMC6123638 DOI: 10.1038/s42003-018-0026-6] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Accepted: 03/01/2018] [Indexed: 11/09/2022] Open
Abstract
High sunlight can raise plant growth rates but can potentially cause cellular damage. The likelihood of deleterious effects is lowered by a sophisticated set of photoprotective mechanisms, one of the most important being the controlled dissipation of energy from chlorophyll within photosystem II (PSII) measured as non-photochemical quenching (NPQ). Although ubiquitous, the role of NPQ in plant productivity remains uncertain because it momentarily reduces the quantum efficiency of photosynthesis. Here we used plants overexpressing the gene encoding a central regulator of NPQ, the protein PsbS, within a major crop species (rice) to assess the effect of photoprotection at the whole canopy scale. We accounted for canopy light interception, to our knowledge for the first time in this context. We show that in comparison to wild-type plants, psbS overexpressors increased canopy radiation use efficiency and grain yield in fluctuating light, demonstrating that photoprotective mechanisms should be altered to improve rice crop productivity.
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Affiliation(s)
- Stella Hubbart
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington, LE12 5RD, UK
| | - Ian R A Smillie
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington, LE12 5RD, UK
| | - Matthew Heatley
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington, LE12 5RD, UK
| | - Ranjan Swarup
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington, LE12 5RD, UK
| | - Chuan Ching Foo
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington, LE12 5RD, UK
| | - Liang Zhao
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington, LE12 5RD, UK
| | - Erik H Murchie
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington, LE12 5RD, UK.
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Townsend AJ, Retkute R, Chinnathambi K, Randall JWP, Foulkes J, Carmo-Silva E, Murchie EH. Suboptimal Acclimation of Photosynthesis to Light in Wheat Canopies. PLANT PHYSIOLOGY 2018; 176:1233-1246. [PMID: 29217593 PMCID: PMC5813572 DOI: 10.1104/pp.17.01213] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 12/04/2017] [Indexed: 05/18/2023]
Abstract
Photosynthetic acclimation (photoacclimation) is the process whereby leaves alter their morphology and/or biochemistry to optimize photosynthetic efficiency and productivity according to long-term changes in the light environment. The three-dimensional architecture of plant canopies imposes complex light dynamics, but the drivers for photoacclimation in such fluctuating environments are poorly understood. A technique for high-resolution three-dimensional reconstruction was combined with ray tracing to simulate a daily time course of radiation profiles for architecturally contrasting field-grown wheat (Triticum aestivum) canopies. An empirical model of photoacclimation was adapted to predict the optimal distribution of photosynthesis according to the fluctuating light patterns throughout the canopies. While the photoacclimation model output showed good correlation with field-measured gas-exchange data at the top of the canopy, it predicted a lower optimal light-saturated rate of photosynthesis at the base. Leaf Rubisco and protein contents were consistent with the measured optimal light-saturated rate of photosynthesis. We conclude that, although the photosynthetic capacity of leaves is high enough to exploit brief periods of high light within the canopy (particularly toward the base), the frequency and duration of such sunflecks are too small to make acclimation a viable strategy in terms of carbon gain. This suboptimal acclimation renders a large portion of residual photosynthetic capacity unused and reduces photosynthetic nitrogen use efficiency at the canopy level, with further implications for photosynthetic productivity. It is argued that (1) this represents an untapped source of photosynthetic potential and (2) canopy nitrogen could be lowered with no detriment to carbon gain or grain protein content.
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Affiliation(s)
- Alexandra J Townsend
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, United Kingdom
- Crops for the Future, Jalan Broga, 43500 Semenyih Selangor Darul Ehsan, Malaysia
| | - Renata Retkute
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, United Kingdom
- School of Life Sciences, Gibbet Hill Campus, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Kannan Chinnathambi
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, United Kingdom
| | - Jamie W P Randall
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, United Kingdom
| | - John Foulkes
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, United Kingdom
| | - Elizabete Carmo-Silva
- Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom
| | - Erik H Murchie
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, United Kingdom
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Jud W, Winkler JB, Niederbacher B, Niederbacher S, Schnitzler JP. Volatilomics: a non-invasive technique for screening plant phenotypic traits. PLANT METHODS 2018; 14:109. [PMID: 30568721 PMCID: PMC6297985 DOI: 10.1186/s13007-018-0378-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 12/03/2018] [Indexed: 05/15/2023]
Abstract
BACKGROUND Climate change represents a grand challenge for agricultural productivity. Understanding complex plant traits such as stress tolerance, disease resistance or crop yield is thus essential for breeding and the development of sustainable agriculture strategies. When screening for the most robust plant phenotypes, fast, high-throughput phenotyping represents the means of choice. RESULTS We have developed a plant phenotyping platform to measure the emission of volatile organic compounds (VOCs), photosynthetic gas exchange and transpiration under ambient, or abiotic and biotic stress conditions. These parameters are highly suitable markers to non-invasively and dynamically study plant growth and plant stress status, making them perfect test variables for long-term, online plant monitoring. Here we introduce the new phenotyping platform, termed VOC-SCREEN, and present results of a first case study with three barley cultivars, demonstrating that the plant's volatilome can be successfully applied to discriminate different barley varieties. CONCLUSION Volatilomics is a promising technique to non-invasively screen for plant phenotypic traits.
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Affiliation(s)
- Werner Jud
- Research Unit Environmental Simulation (EUS), Institute for Biochemical Plant Pathology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - J. Barbro Winkler
- Research Unit Environmental Simulation (EUS), Institute for Biochemical Plant Pathology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Bishu Niederbacher
- Research Unit Environmental Simulation (EUS), Institute for Biochemical Plant Pathology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
- Present Address: Ionicon Analytic GmbH, Eduard-Bodem-Gasse 3, 6020 Innsbruck, Austria
| | - Simon Niederbacher
- Research Unit Environmental Simulation (EUS), Institute for Biochemical Plant Pathology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
- Present Address: Ionicon Analytic GmbH, Eduard-Bodem-Gasse 3, 6020 Innsbruck, Austria
| | - Jörg-Peter Schnitzler
- Research Unit Environmental Simulation (EUS), Institute for Biochemical Plant Pathology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
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Pound MP, Atkinson JA, Townsend AJ, Wilson MH, Griffiths M, Jackson AS, Bulat A, Tzimiropoulos G, Wells DM, Murchie EH, Pridmore TP, French AP. Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. Gigascience 2017; 6:1-10. [PMID: 29020747 PMCID: PMC5632296 DOI: 10.1093/gigascience/gix083] [Citation(s) in RCA: 122] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Revised: 07/27/2017] [Accepted: 08/16/2017] [Indexed: 11/12/2022] Open
Abstract
In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning-based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets.
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Affiliation(s)
- Michael P. Pound
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK
| | - Jonathan A. Atkinson
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Nr Loughborough, LE12 5RD, UK
| | - Alexandra J. Townsend
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Nr Loughborough, LE12 5RD, UK
| | - Michael H. Wilson
- Centre for Plant Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK
| | - Marcus Griffiths
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Nr Loughborough, LE12 5RD, UK
| | - Aaron S. Jackson
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK
| | - Adrian Bulat
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK
| | - Georgios Tzimiropoulos
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK
| | - Darren M. Wells
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Nr Loughborough, LE12 5RD, UK
| | - Erik H. Murchie
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Nr Loughborough, LE12 5RD, UK
| | - Tony P. Pridmore
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK
| | - Andrew P. French
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Nr Loughborough, LE12 5RD, UK
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Srivastava S, Bhugra S, Lall B, Chaudhury S. Drought Stress Classification Using 3D Plant Models. 2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW) 2017. [DOI: 10.1109/iccvw.2017.240] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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Lu H, Tang L, Whitham SA, Mei Y. A Robotic Platform for Corn Seedling Morphological Traits Characterization. SENSORS 2017; 17:s17092082. [PMID: 28895892 PMCID: PMC5621065 DOI: 10.3390/s17092082] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2017] [Revised: 09/03/2017] [Accepted: 09/06/2017] [Indexed: 12/03/2022]
Abstract
Crop breeding plays an important role in modern agriculture, improving plant performance, and increasing yield. Identifying the genes that are responsible for beneficial traits greatly facilitates plant breeding efforts for increasing crop production. However, associating genes and their functions with agronomic traits requires researchers to observe, measure, record, and analyze phenotypes of large numbers of plants, a repetitive and error-prone job if performed manually. An automated seedling phenotyping system aimed at replacing manual measurement, reducing sampling time, and increasing the allowable work time is thus highly valuable. Toward this goal, we developed an automated corn seedling phenotyping platform based on a time-of-flight of light (ToF) camera and an industrial robot arm. A ToF camera is mounted on the end effector of the robot arm. The arm positions the ToF camera at different viewpoints for acquiring 3D point cloud data. A camera-to-arm transformation matrix was calculated using a hand-eye calibration procedure and applied to transfer different viewpoints into an arm-based coordinate frame. Point cloud data filters were developed to remove the noise in the background and in the merged seedling point clouds. A 3D-to-2D projection and an x-axis pixel density distribution method were used to segment the stem and leaves. Finally, separated leaves were fitted with 3D curves for morphological traits characterization. This platform was tested on a sample of 60 corn plants at their early growth stages with between two to five leaves. The error ratios of the stem height and leave length measurements are 13.7% and 13.1%, respectively, demonstrating the feasibility of this robotic system for automated corn seedling phenotyping.
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Affiliation(s)
- Hang Lu
- Department of Agricultural and Biosystems Engineering, Iowa State University, 2346 Elings Hall, Ames, IA 50011, USA.
| | - Lie Tang
- Department of Agricultural and Biosystems Engineering, Iowa State University, 2346 Elings Hall, Ames, IA 50011, USA.
| | - Steven A Whitham
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA 50011, USA.
| | - Yu Mei
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA 50011, USA.
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Lobet G. Image Analysis in Plant Sciences: Publish Then Perish. TRENDS IN PLANT SCIENCE 2017; 22:559-566. [PMID: 28571940 DOI: 10.1016/j.tplants.2017.05.002] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Revised: 04/07/2017] [Accepted: 05/03/2017] [Indexed: 05/21/2023]
Abstract
Image analysis has become a powerful technique for most plant scientists. In recent years dozens of image analysis tools have been published in plant science journals. These tools cover the full spectrum of plant scales, from single cells to organs and canopies. However, the field of plant image analysis remains in its infancy. It still has to overcome important challenges, such as the lack of robust validation practices or the absence of long-term support. In this Opinion article, I: (i) present the current state of the field, based on data from the plant-image-analysis.org database; (ii) identify the challenges faced by its community; and (iii) propose workable ways of improvement.
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Affiliation(s)
- Guillaume Lobet
- Agrosphäre (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany; Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium.
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Burgess AJ, Retkute R, Herman T, Murchie EH. Exploring Relationships between Canopy Architecture, Light Distribution, and Photosynthesis in Contrasting Rice Genotypes Using 3D Canopy Reconstruction. FRONTIERS IN PLANT SCIENCE 2017; 8:734. [PMID: 28567045 PMCID: PMC5434157 DOI: 10.3389/fpls.2017.00734] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Accepted: 04/20/2017] [Indexed: 05/19/2023]
Abstract
The arrangement of leaf material is critical in determining the light environment, and subsequently the photosynthetic productivity of complex crop canopies. However, links between specific canopy architectural traits and photosynthetic productivity across a wide genetic background are poorly understood for field grown crops. The architecture of five genetically diverse rice varieties-four parental founders of a multi-parent advanced generation intercross (MAGIC) population plus a high yielding Philippine variety (IR64)-was captured at two different growth stages using a method for digital plant reconstruction based on stereocameras. Ray tracing was employed to explore the effects of canopy architecture on the resulting light environment in high-resolution, whilst gas exchange measurements were combined with an empirical model of photosynthesis to calculate an estimated carbon gain and total light interception. To further test the impact of different dynamic light patterns on photosynthetic properties, an empirical model of photosynthetic acclimation was employed to predict the optimal light-saturated photosynthesis rate (Pmax ) throughout canopy depth, hypothesizing that light is the sole determinant of productivity in these conditions. First, we show that a plant type with steeper leaf angles allows more efficient penetration of light into lower canopy layers and this, in turn, leads to a greater photosynthetic potential. Second the predicted optimal Pmax responds in a manner that is consistent with fractional interception and leaf area index across this germplasm. However, measured Pmax , especially in lower layers, was consistently higher than the optimal Pmax indicating factors other than light determine photosynthesis profiles. Lastly, varieties with more upright architecture exhibit higher maximum quantum yield of photosynthesis indicating a canopy-level impact on photosynthetic efficiency.
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Affiliation(s)
- Alexandra J. Burgess
- Division of Plant and Crop Sciences, School of Biosciences, University of NottinghamLoughborough, UK
- Crops For the FutureSemenyih, Malaysia
| | - Renata Retkute
- School of Life Sciences, The University of WarwickCoventry, UK
| | - Tiara Herman
- School of Biosciences, University of Nottingham Malaysia CampusSemenyih, Malaysia
| | - Erik H. Murchie
- Division of Plant and Crop Sciences, School of Biosciences, University of NottinghamLoughborough, UK
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Burgess AJ, Retkute R, Pound MP, Mayes S, Murchie EH. Image-based 3D canopy reconstruction to determine potential productivity in complex multi-species crop systems. ANNALS OF BOTANY 2017; 119:517-532. [PMID: 28065926 PMCID: PMC5458713 DOI: 10.1093/aob/mcw242] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Accepted: 09/27/2016] [Indexed: 05/22/2023]
Abstract
Background and Aims Intercropping systems contain two or more species simultaneously in close proximity. Due to contrasting features of the component crops, quantification of the light environment and photosynthetic productivity is extremely difficult. However it is an essential component of productivity. Here, a low-tech but high-resolution method is presented that can be applied to single- and multi-species cropping systems to facilitate characterization of the light environment. Different row layouts of an intercrop consisting of Bambara groundnut ( Vigna subterranea ) and proso millet ( Panicum miliaceum ) have been used as an example and the new opportunities presented by this approach have been analysed. Methods Three-dimensional plant reconstruction, based on stereo cameras, combined with ray tracing was implemented to explore the light environment within the Bambara groundnut-proso millet intercropping system and associated monocrops. Gas exchange data were used to predict the total carbon gain of each component crop. Key Results The shading influence of the tall proso millet on the shorter Bambara groundnut results in a reduction in total canopy light interception and carbon gain. However, the increased leaf area index (LAI) of proso millet, higher photosynthetic potential due to the C4 pathway and sub-optimal photosynthetic acclimation of Bambara groundnut to shade means that increasing the number of rows of millet will lead to greater light interception and carbon gain per unit ground area, despite Bambara groundnut intercepting more light per unit leaf area. Conclusions Three-dimensional reconstruction combined with ray tracing provides a novel, accurate method of exploring the light environment within an intercrop that does not require difficult measurements of light interception and data-intensive manual reconstruction, especially for such systems with inherently high spatial possibilities. It provides new opportunities for calculating potential productivity within multi-species cropping systems, enables the quantification of dynamic physiological differences between crops grown as monoculture and those within intercrops, and enables the prediction of new productive combinations of previously untested crops.
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Affiliation(s)
- Alexandra J. Burgess
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, UK
- Crops For the Future, Jalan Broga, 43500 Semenyih Selangor Darul Ehsan, Malaysia
| | - Renata Retkute
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, UK
- School of Life Sciences, Gibbet Hill Campus, The University of Warwick, Coventry CV4 7AL, UK
| | - Michael P. Pound
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK
| | - Sean Mayes
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, UK
- Crops For the Future, Jalan Broga, 43500 Semenyih Selangor Darul Ehsan, Malaysia
| | - Erik H. Murchie
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, UK
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