1
|
Daviet B, Fournier C, Cabrera-Bosquet L, Simonneau T, Cafier M, Romieu C. Ripening dynamics revisited: an automated method to track the development of asynchronous berries on time-lapse images. PLANT METHODS 2023; 19:146. [PMID: 38098093 PMCID: PMC10720176 DOI: 10.1186/s13007-023-01125-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 12/08/2023] [Indexed: 12/18/2023]
Abstract
BACKGROUND Grapevine berries undergo asynchronous growth and ripening dynamics within the same bunch. Due to the lack of efficient methods to perform sequential non-destructive measurements on a representative number of individual berries, the genetic and environmental origins of this heterogeneity, remain nearly unknown. To address these limitations, we propose a method to track the growth and coloration kinetics of individual berries on time-lapse images of grapevine bunches. RESULTS First, a deep-learning approach is used to detect berries with at least 50 ± 10% of visible contours, and infer the shape they would have in the absence of occlusions. Second, a tracking algorithm was developed to assign a common label to shapes representing the same berry along the time-series. Training and validation of the methods were performed on challenging image datasets acquired in a robotised high-throughput phenotyping platform. Berries were detected on various genotypes with a F1-score of 91.8%, and segmented with a mean absolute error of 4.1% on their area. Tracking allowed to label and retrieve the temporal identity of more than half of the segmented berries, with an accuracy of 98.1%. This method was used to extract individual growth and colour kinetics of various berries from the same bunch, allowing us to propose the first statistically relevant analysis of berry ripening kinetics, with a time resolution lower than one day. CONCLUSIONS We successfully developed a fully-automated open-source method to detect, segment and track overlapping berries in time-series of grapevine bunch images acquired in laboratory conditions. This makes it possible to quantify fine aspects of individual berry development, and to characterise the asynchrony within the bunch. The interest of such analysis was illustrated here for one cultivar, but the method has the potential to be applied in a high throughput phenotyping context. This opens the way for revisiting the genetic and environmental variations of the ripening dynamics. Such variations could be considered both from the point of view of fruit development and the phenological structure of the population, which would constitute a paradigm shift.
Collapse
Affiliation(s)
- Benoit Daviet
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | | | | | | | - Maxence Cafier
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Charles Romieu
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France.
| |
Collapse
|
2
|
Bouidghaghen J, Moreau L, Beauchêne K, Chapuis R, Mangel N, Cabrera-Bosquet L, Welcker C, Bogard M, Tardieu F. Robotized indoor phenotyping allows genomic prediction of adaptive traits in the field. Nat Commun 2023; 14:6603. [PMID: 37857601 PMCID: PMC10587076 DOI: 10.1038/s41467-023-42298-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 10/06/2023] [Indexed: 10/21/2023] Open
Abstract
Breeding for resilience to climate change requires considering adaptive traits such as plant architecture, stomatal conductance and growth, beyond the current selection for yield. Robotized indoor phenotyping allows measuring such traits at high throughput for speed breeding, but is often considered as non-relevant for field conditions. Here, we show that maize adaptive traits can be inferred in different fields, based on genotypic values obtained indoor and on environmental conditions in each considered field. The modelling of environmental effects allows translation from indoor to fields, but also from one field to another field. Furthermore, genotypic values of considered traits match between indoor and field conditions. Genomic prediction results in adequate ranking of genotypes for the tested traits, although with lesser precision for elite varieties presenting reduced phenotypic variability. Hence, it distinguishes genotypes with high or low values for adaptive traits, conferring either spender or conservative strategies for water use under future climates.
Collapse
Affiliation(s)
- Jugurta Bouidghaghen
- LEPSE, Univ Montpellier, INRAE, Montpellier, France
- ARVALIS, Chemin de la côte vieille, Baziège, France
| | - Laurence Moreau
- GQE-Le Moulon, INRAE, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Katia Beauchêne
- ARVALIS, 45 Voie Romaine, Ouzouer-Le-Marché, Beauce La Romaine, France
| | | | - Nathalie Mangel
- ARVALIS, Station de recherche et d'expérimentation, Boigneville, France
| | | | | | | | | |
Collapse
|
3
|
Fu H, Yang Y. How Plants Tolerate Salt Stress. Curr Issues Mol Biol 2023; 45:5914-5934. [PMID: 37504290 PMCID: PMC10378706 DOI: 10.3390/cimb45070374] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 07/13/2023] [Accepted: 07/13/2023] [Indexed: 07/29/2023] Open
Abstract
Soil salinization inhibits plant growth and seriously restricts food security and agricultural development. Excessive salt can cause ionic stress, osmotic stress, and ultimately oxidative stress in plants. Plants exclude excess salt from their cells to help maintain ionic homeostasis and stimulate phytohormone signaling pathways, thereby balancing growth and stress tolerance to enhance their survival. Continuous innovations in scientific research techniques have allowed great strides in understanding how plants actively resist salt stress. Here, we briefly summarize recent achievements in elucidating ionic homeostasis, osmotic stress regulation, oxidative stress regulation, and plant hormonal responses under salt stress. Such achievements lay the foundation for a comprehensive understanding of plant salt-tolerance mechanisms.
Collapse
Affiliation(s)
- Haiqi Fu
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
- Tianjin Key Laboratory of Crop Genetics and Breeding, Institute of Crop Sciences, Tianjin Academy of Agricultural Sciences, Tianjin 300380, China
| | - Yongqing Yang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| |
Collapse
|
4
|
Großkinsky DK, Faure JD, Gibon Y, Haslam RP, Usadel B, Zanetti F, Jonak C. The potential of integrative phenomics to harness underutilized crops for improving stress resilience. FRONTIERS IN PLANT SCIENCE 2023; 14:1216337. [PMID: 37409292 PMCID: PMC10318926 DOI: 10.3389/fpls.2023.1216337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 06/08/2023] [Indexed: 07/07/2023]
Affiliation(s)
- Dominik K. Großkinsky
- AIT Austrian Institute of Technology, Center for Health and Bioresources, Bioresources Unit, Tulln a. d. Donau, Austria
| | - Jean-Denis Faure
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin, Versailles, France
| | - Yves Gibon
- INRAE, Univ. Bordeaux, UMR BFP, Villenave d’Ornon, France
- Bordeaux Metabolome, INRAE, Univ. Bordeaux, Villenave d’Ornon, France
| | | | - Björn Usadel
- IBG-4 Bioinformatics, CEPLAS, Forschungszentrum, Jülich, Germany
- Biological Data Science, Heinrich Heine University, Universitätsstrasse 1, Düsseldorf, Germany
| | - Federica Zanetti
- Department of Agricultural and Food Sciences (DISTAL), Alma Mater Studiorum - Università di Bologna, Bologna, Italy
| | - Claudia Jonak
- AIT Austrian Institute of Technology, Center for Health and Bioresources, Bioresources Unit, Tulln a. d. Donau, Austria
| |
Collapse
|
5
|
Solimani F, Cardellicchio A, Nitti M, Lako A, Dimauro G, Renò V. A Systematic Review of Effective Hardware and Software Factors Affecting High-Throughput Plant Phenotyping. INFORMATION 2023. [DOI: 10.3390/info14040214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023] Open
Abstract
Plant phenotyping studies the complex characteristics of plants, with the aim of evaluating and assessing their condition and finding better exemplars. Recently, a new branch emerged in the phenotyping field, namely, high-throughput phenotyping (HTP). Specifically, HTP exploits modern data sampling techniques to gather a high amount of data that can be used to improve the effectiveness of phenotyping. Hence, HTP combines the knowledge derived from the phenotyping domain with computer science, engineering, and data analysis techniques. In this scenario, machine learning (ML) and deep learning (DL) algorithms have been successfully integrated with noninvasive imaging techniques, playing a key role in automation, standardization, and quantitative data analysis. This study aims to systematically review two main areas of interest for HTP: hardware and software. For each of these areas, two influential factors were identified: for hardware, platforms and sensing equipment were analyzed; for software, the focus was on algorithms and new trends. The study was conducted following the PRISMA protocol, which allowed the refinement of the research on a wide selection of papers by extracting a meaningful dataset of 32 articles of interest. The analysis highlighted the diffusion of ground platforms, which were used in about 47% of reviewed methods, and RGB sensors, mainly due to their competitive costs, high compatibility, and versatility. Furthermore, DL-based algorithms accounted for the larger share (about 69%) of reviewed approaches, mainly due to their effectiveness and the focus posed by the scientific community over the last few years. Future research will focus on improving DL models to better handle hardware-generated data. The final aim is to create integrated, user-friendly, and scalable tools that can be directly deployed and used on the field to improve the overall crop yield.
Collapse
Affiliation(s)
- Firozeh Solimani
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Via Amendola 122 D/O, 70126 Bari, Italy
| | - Angelo Cardellicchio
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Via Amendola 122 D/O, 70126 Bari, Italy
| | - Massimiliano Nitti
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Via Amendola 122 D/O, 70126 Bari, Italy
| | - Alfred Lako
- Faculty of Civil Engineering, Polytechnic University of Tirana, Bulevardi Dëshmorët e Kombit Nr. 4, 1000 Tiranë, Albania
| | - Giovanni Dimauro
- Department of Computer Science, University of Bari, Via E. Orabona, 4, 70125 Bari, Italy
| | - Vito Renò
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Via Amendola 122 D/O, 70126 Bari, Italy
| |
Collapse
|
6
|
Daviet B, Fernandez R, Cabrera-Bosquet L, Pradal C, Fournier C. PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time. PLANT METHODS 2022; 18:130. [PMID: 36482291 PMCID: PMC9730636 DOI: 10.1186/s13007-022-00961-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND High-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs. RESULTS We propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. The method tracks the development of each organ from a time-series of plants whose organs have already been segmented in 3D using existing methods, such as Phenomenal [Artzet et al. in BioRxiv 1:805739, 2019] which was chosen in this study. First, a novel stem detection method based on deep-learning is used to locate precisely the point of separation between ligulated and growing leaves. Second, a new and original multiple sequence alignment algorithm has been developed to perform the temporal tracking of ligulated leaves, which have a consistent geometry over time and an unambiguous topological position. Finally, growing leaves are back-tracked with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE < 2.1 cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants × 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10-355 plants. CONCLUSIONS We developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise the development of maize architecture at organ level, automatically and at a high-throughput. It has been validated on hundreds of plants during the entire development cycle, showing its applicability on GxE analyses of large maize datasets.
Collapse
Affiliation(s)
- Benoit Daviet
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | - Romain Fernandez
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- CIRAD, INRAE, UMR AGAP Institut, Univ Montpellier, Institut Agro, 34398, Montpellier, France
| | | | - Christophe Pradal
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France.
- CIRAD, INRAE, UMR AGAP Institut, Univ Montpellier, Institut Agro, 34398, Montpellier, France.
- Inria & LIRMM, CNRS, Univ Montpellier, Montpellier, France.
| | | |
Collapse
|
7
|
Wild Wheat Rhizosphere-Associated Plant Growth-Promoting Bacteria Exudates: Effect on Root Development in Modern Wheat and Composition. Int J Mol Sci 2022; 23:ijms232315248. [PMID: 36499572 PMCID: PMC9740669 DOI: 10.3390/ijms232315248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/15/2022] [Accepted: 11/28/2022] [Indexed: 12/12/2022] Open
Abstract
Diazotrophic bacteria isolated from the rhizosphere of a wild wheat ancestor, grown from its refuge area in the Fertile Crescent, were found to be efficient Plant Growth-Promoting Rhizobacteria (PGPR), upon interaction with an elite wheat cultivar. In nitrogen-starved plants, they increased the amount of nitrogen in the seed crop (per plant) by about twofold. A bacterial growth medium was developed to investigate the effects of bacterial exudates on root development in the elite cultivar, and to analyze the exo-metabolomes and exo-proteomes. Altered root development was observed, with distinct responses depending on the strain, for instance, with respect to root hair development. A first conclusion from these results is that the ability of wheat to establish effective beneficial interactions with PGPRs does not appear to have undergone systematic deep reprogramming during domestication. Exo-metabolome analysis revealed a complex set of secondary metabolites, including nutrient ion chelators, cyclopeptides that could act as phytohormone mimetics, and quorum sensing molecules having inter-kingdom signaling properties. The exo-proteome-comprised strain-specific enzymes, and structural proteins belonging to outer-membrane vesicles, are likely to sequester metabolites in their lumen. Thus, the methodological processes we have developed to collect and analyze bacterial exudates have revealed that PGPRs constitutively exude a highly complex set of metabolites; this is likely to allow numerous mechanisms to simultaneously contribute to plant growth promotion, and thereby to also broaden the spectra of plant genotypes (species and accessions/cultivars) with which beneficial interactions can occur.
Collapse
|
8
|
Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding. Int J Mol Sci 2022; 23:ijms231911156. [PMID: 36232455 PMCID: PMC9570104 DOI: 10.3390/ijms231911156] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/18/2022] [Accepted: 09/19/2022] [Indexed: 11/21/2022] Open
Abstract
Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other “omics” approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These “omics” approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with “omics” tools can allow rapid gene identification and eventually accelerate crop-improvement programs.
Collapse
|
9
|
Affortit P, Effa-Effa B, Ndoye MS, Moukouanga D, Luchaire N, Cabrera-Bosquet L, Perálvarez M, Pilloni R, Welcker C, Champion A, Gantet P, Diedhiou AG, Manneh B, Aroca R, Vadez V, Laplaze L, Cubry P, Grondin A. Physiological and genetic control of transpiration efficiency in African rice, Oryza glaberrima Steud. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:5279-5293. [PMID: 35429274 DOI: 10.1093/jxb/erac156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 04/13/2022] [Indexed: 06/14/2023]
Abstract
Improving crop water use efficiency, the amount of carbon assimilated as biomass per unit of water used by a plant, is of major importance as water for agriculture becomes scarcer. In rice, the genetic bases of transpiration efficiency, the derivation of water use efficiency at the whole-plant scale, and its putative component trait transpiration restriction under high evaporative demand remain unknown. These traits were measured in 2019 in a panel of 147 African rice (Oryza glaberrima) genotypes known to be potential sources of tolerance genes to biotic and abiotic stresses. Our results reveal that higher transpiration efficiency is associated with transpiration restriction in African rice. Detailed measurements in a subset of highly contrasted genotypes in terms of biomass accumulation and transpiration confirmed these associations and suggested that root to shoot ratio played an important role in transpiration restriction. Genome wide association studies identified marker-trait associations for transpiration response to evaporative demand, transpiration efficiency, and its residuals, with links to genes involved in water transport and cell wall patterning. Our data suggest that root-shoot partitioning is an important component of transpiration restriction that has a positive effect on transpiration efficiency in African rice. Both traits are heritable and define targets for breeding rice with improved water use strategies.
Collapse
Affiliation(s)
- Pablo Affortit
- DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France
| | - Branly Effa-Effa
- DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France
- CENAREST, Libreville, Gabon
| | - Mame Sokhatil Ndoye
- DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France
- CERAAS, Thiès, Senegal
| | | | - Nathalie Luchaire
- LEPSE, Université de Montpellier, INRAE, Institut Agro, Montpellier, France
| | | | | | - Raphaël Pilloni
- DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France
| | - Claude Welcker
- LEPSE, Université de Montpellier, INRAE, Institut Agro, Montpellier, France
| | - Antony Champion
- DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France
| | - Pascal Gantet
- DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France
| | | | | | | | - Vincent Vadez
- DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France
- CERAAS, Thiès, Senegal
- LMI LAPSE, Dakar, Senegal
- ICRISAT, Patancheru, India
| | - Laurent Laplaze
- DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France
- LMI LAPSE, Dakar, Senegal
| | - Philippe Cubry
- DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France
| | - Alexandre Grondin
- DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France
- CERAAS, Thiès, Senegal
- LMI LAPSE, Dakar, Senegal
| |
Collapse
|
10
|
Forero MG, Murcia HF, Méndez D, Betancourt-Lozano J. LiDAR Platform for Acquisition of 3D Plant Phenotyping Database. PLANTS 2022; 11:plants11172199. [PMID: 36079580 PMCID: PMC9459957 DOI: 10.3390/plants11172199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 07/26/2022] [Accepted: 08/10/2022] [Indexed: 11/16/2022]
Abstract
Currently, there are no free databases of 3D point clouds and images for seedling phenotyping. Therefore, this paper describes a platform for seedling scanning using 3D Lidar with which a database was acquired for use in plant phenotyping research. In total, 362 maize seedlings were recorded using an RGB camera and a SICK LMS4121R-13000 laser scanner with angular resolutions of 45° and 0.5° respectively. The scanned plants are diverse, with seedling captures ranging from less than 10 cm to 40 cm, and ranging from 7 to 24 days after planting in different light conditions in an indoor setting. The point clouds were processed to remove noise and imperfections with a mean absolute precision error of 0.03 cm, synchronized with the images, and time-stamped. The database includes the raw and processed data and manually assigned stem and leaf labels. As an example of a database application, a Random Forest classifier was employed to identify seedling parts based on morphological descriptors, with an accuracy of 89.41%.
Collapse
|
11
|
Huang C, Li W, Zhang Z, Hua X, Yang J, Ye J, Duan L, Liang X, Yang W. An Intelligent Rice Yield Trait Evaluation System Based on Threshed Panicle Compensation. FRONTIERS IN PLANT SCIENCE 2022; 13:900408. [PMID: 35937323 PMCID: PMC9354939 DOI: 10.3389/fpls.2022.900408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
High-throughput phenotyping of yield-related traits is meaningful and necessary for rice breeding and genetic study. The conventional method for rice yield-related trait evaluation faces the problems of rice threshing difficulties, measurement process complexity, and low efficiency. To solve these problems, a novel intelligent system, which includes an integrated threshing unit, grain conveyor-imaging units, threshed panicle conveyor-imaging unit, and specialized image analysis software has been proposed to achieve rice yield trait evaluation with high throughput and high accuracy. To improve the threshed panicle detection accuracy, the Region of Interest Align, Convolution Batch normalization activation with Leaky Relu module, Squeeze-and-Excitation unit, and optimal anchor size have been adopted to optimize the Faster-RCNN architecture, termed 'TPanicle-RCNN,' and the new model achieved F1 score 0.929 with an increase of 0.044, which was robust to indica and japonica varieties. Additionally, AI cloud computing was adopted, which dramatically reduced the system cost and improved flexibility. To evaluate the system accuracy and efficiency, 504 panicle samples were tested, and the total spikelet measurement error decreased from 11.44 to 2.99% with threshed panicle compensation. The average measuring efficiency was approximately 40 s per sample, which was approximately twenty times more efficient than manual measurement. In this study, an automatic and intelligent system for rice yield-related trait evaluation was developed, which would provide an efficient and reliable tool for rice breeding and genetic research.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), College of Engineering, Huazhong Agricultural University, Wuhan, China
| |
Collapse
|
12
|
Gill T, Gill SK, Saini DK, Chopra Y, de Koff JP, Sandhu KS. A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:156-183. [PMID: 36939773 PMCID: PMC9590503 DOI: 10.1007/s43657-022-00048-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/29/2022] [Accepted: 02/11/2022] [Indexed: 02/04/2023]
Abstract
During the last decade, there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant. High throughput phenotyping (HTP) involves the application of these tools to phenotype the plants and can vary from ground-based imaging to aerial phenotyping to remote sensing. Adoption of these HTP tools has tried to reduce the phenotyping bottleneck in breeding programs and help to increase the pace of genetic gain. More specifically, several root phenotyping tools are discussed to study the plant's hidden half and an area long neglected. However, the use of these HTP technologies produces big data sets that impede the inference from those datasets. Machine learning and deep learning provide an alternative opportunity for the extraction of useful information for making conclusions. These are interdisciplinary approaches for data analysis using probability, statistics, classification, regression, decision theory, data visualization, and neural networks to relate information extracted with the phenotypes obtained. These techniques use feature extraction, identification, classification, and prediction criteria to identify pertinent data for use in plant breeding and pathology activities. This review focuses on the recent findings where machine learning and deep learning approaches have been used for plant stress phenotyping with data being collected using various HTP platforms. We have provided a comprehensive overview of different machine learning and deep learning tools available with their potential advantages and pitfalls. Overall, this review provides an avenue for studying various HTP platforms with particular emphasis on using the machine learning and deep learning tools for drawing legitimate conclusions. Finally, we propose the conceptual challenges being faced and provide insights on future perspectives for managing those issues.
Collapse
Affiliation(s)
- Taqdeer Gill
- grid.280741.80000 0001 2284 9820Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Simranveer K. Gill
- grid.412577.20000 0001 2176 2352College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Dinesh K. Saini
- grid.412577.20000 0001 2176 2352Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Yuvraj Chopra
- grid.412577.20000 0001 2176 2352College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Jason P. de Koff
- grid.280741.80000 0001 2284 9820Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Karansher S. Sandhu
- grid.30064.310000 0001 2157 6568Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163 USA
| |
Collapse
|
13
|
Colbach N, Felten E, Gée C, Klein A, Lannuzel L, Lecomte C, Maillot T, Strbik F, Villerd J, Moreau D. Tracking Ideal Varieties and Cropping Techniques for Agroecological Weed Management: A Simulation-Based Study on Pea. FRONTIERS IN PLANT SCIENCE 2022; 13:809056. [PMID: 35444680 PMCID: PMC9014269 DOI: 10.3389/fpls.2022.809056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 01/26/2022] [Indexed: 06/14/2023]
Abstract
Pea or Pisum sativum L. is a key diversification crop, but current varieties are not very competitive against weeds. The objective was to identify, depending on the type of cropping system and weed flora, (1) the key pea parameters that drive crop production, weed control and weed contribution to biodiversity, (2) optimal combinations of pea-parameter values and crop-management techniques to maximize these goals. For this, virtual experiments were run, using FLORSYS, a mechanistic simulation model. This individual-based 3D model simulates daily crop-weed seed and plant dynamics over the years, from the cropping system and pedoclimate. Here, this model was parameterized for seven pea varieties, from experiments and literature. Moreover, ten virtual varieties were created by randomly combining variety-parameter values according to a Latin Hypercube Sampling (LHS) plan, respecting parameter ranges and correlations observed in the actual varieties. A global sensitivity analysis was run, using another LHS plan to combine pea varieties, crop rotations and management techniques in nine contrasting situations (e.g., conventional vs. organic, no-till, type of weed flora). Simulated data were analyzed with classification and regression trees (CART). We highlighted (1) Parameters that drive potential yield and competitivity against weeds (notably the ability to increase plant height and leaf area in shaded situations), depending on variety type (spring vs. winter) and cropping system. These are pointers for breeding varieties to regulate weeds by biological interactions; (2) Rules to guide farmers to choose the best pea variety, depending on the production goal and the cropping system; (3) The trade-off between increasing yield potential and minimizing yield losses due to weeds when choosing pea variety and management, especially in winter peas. The main pea-variety rules were the same for all performance goals, management strategies, and analyses scales, but further rules were useful for individual goals, strategies, and scales. Some variety features only fitted to particular systems (e.g., delayed pea emergence is only beneficial in case of herbicide-spraying and disastrous in unsprayed systems). Fewer variety rules should be compensated by more management rules. If one of the two main weed-control levers, herbicide or tillage, was eliminated, further pea-variety and/or management rules were needed.
Collapse
|
14
|
Pérez-Valencia DM, Rodríguez-Álvarez MX, Boer MP, Kronenberg L, Hund A, Cabrera-Bosquet L, Millet EJ, Eeuwijk FAV. A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data. Sci Rep 2022; 12:3177. [PMID: 35210494 PMCID: PMC8873425 DOI: 10.1038/s41598-022-06935-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 01/20/2022] [Indexed: 12/19/2022] Open
Abstract
High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. Such HTP data require challenging statistical analyses in which longitudinal genetic signals need to be estimated against a background of spatio-temporal noise processes. We propose a two-stage approach for the analysis of such longitudinal HTP data. In a first stage, we correct for design features and spatial trends per time point. In a second stage, we focus on the longitudinal modelling of the spatially corrected data, thereby taking advantage of shared longitudinal features between genotypes and plants within genotypes. We propose a flexible hierarchical three-level P-spline growth curve model, with plants/plots nested in genotypes, and genotypes nested in populations. For selection of genotypes in a plant breeding context, we show how to extract new phenotypes, like growth rates, from the estimated genotypic growth curves and their first-order derivatives. We illustrate our approach on HTP data from the PhenoArch greenhouse platform at INRAE Montpellier and the outdoor Field Phenotyping platform at ETH Zürich.
Collapse
Affiliation(s)
- Diana M Pérez-Valencia
- BCAM-Basque Center for Applied Mathematics, Mazarredo 14, 48009, Bilbao, Spain.
- Departamento de Matemáticas, Universidad del País Vasco UPV/EHU, 48940, Leioa, Spain.
| | - María Xosé Rodríguez-Álvarez
- BCAM-Basque Center for Applied Mathematics, Mazarredo 14, 48009, Bilbao, Spain
- IKERBASQUE, Basque Foundation for Science, 48009, Bilbao, Spain
- Department of Statistics and Operations Research, Universidade de Vigo, 36310, Vigo, Spain
| | - Martin P Boer
- Biometris, Wageningen University & Research, 6708 PB, Wageningen, The Netherlands
| | - Lukas Kronenberg
- Crop Science, Institute of Agricultural Sciences, ETH Zürich, 8092, Zürich, Switzerland
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zürich, 8092, Zürich, Switzerland
| | - Andreas Hund
- Crop Science, Institute of Agricultural Sciences, ETH Zürich, 8092, Zürich, Switzerland
| | | | - Emilie J Millet
- Biometris, Wageningen University & Research, 6708 PB, Wageningen, The Netherlands
- LEPSE, Univ Montpellier, INRAE, Institut Agro, 34060, Montpellier, France
| | - Fred A van Eeuwijk
- Biometris, Wageningen University & Research, 6708 PB, Wageningen, The Netherlands
| |
Collapse
|
15
|
Sun D, Robbins K, Morales N, Shu Q, Cen H. Advances in optical phenotyping of cereal crops. TRENDS IN PLANT SCIENCE 2022; 27:191-208. [PMID: 34417079 DOI: 10.1016/j.tplants.2021.07.015] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 07/22/2021] [Accepted: 07/24/2021] [Indexed: 06/13/2023]
Abstract
Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established.
Collapse
Affiliation(s)
- Dawei Sun
- College of Biosystems Engineering and Food Science, and State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, PR China
| | - Kelly Robbins
- Section of Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Nicolas Morales
- Section of Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Qingyao Shu
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Zhejiang University, Hangzhou, PR China; State Key Laboratory of Rice Biology, Zhejiang University, Hangzhou 310058, PR China
| | - Haiyan Cen
- College of Biosystems Engineering and Food Science, and State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, PR China.
| |
Collapse
|
16
|
Kuromori T, Fujita M, Takahashi F, Yamaguchi‐Shinozaki K, Shinozaki K. Inter-tissue and inter-organ signaling in drought stress response and phenotyping of drought tolerance. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 109:342-358. [PMID: 34863007 PMCID: PMC9300012 DOI: 10.1111/tpj.15619] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 11/26/2021] [Accepted: 11/29/2021] [Indexed: 05/10/2023]
Abstract
Plant response to drought stress includes systems for intracellular regulation of gene expression and signaling, as well as inter-tissue and inter-organ signaling, which helps entire plants acquire stress resistance. Plants sense water-deficit conditions both via the stomata of leaves and roots, and transfer water-deficit signals from roots to shoots via inter-organ signaling. Abscisic acid is an important phytohormone involved in the drought stress response and adaptation, and is synthesized mainly in vascular tissues and guard cells of leaves. In leaves, stress-induced abscisic acid is distributed to various tissues by transporters, which activates stomatal closure and expression of stress-related genes to acquire drought stress resistance. Moreover, the stepwise stress response at the whole-plant level is important for proper understanding of the physiological response to drought conditions. Drought stress is sensed by multiple types of sensors as molecular patterns of abiotic stress signals, which are transmitted via separate parallel signaling networks to induce downstream responses, including stomatal closure and synthesis of stress-related proteins and metabolites. Peptide molecules play important roles in the inter-organ signaling of dehydration from roots to shoots, as well as signaling of osmotic changes and reactive oxygen species/Ca2+ . In this review, we have summarized recent advances in research on complex plant drought stress responses, focusing on inter-tissue signaling in leaves and inter-organ signaling from roots to shoots. We have discussed the mechanisms via which drought stress adaptations and resistance are acquired at the whole-plant level, and have proposed the importance of quantitative phenotyping for measuring plant growth under drought conditions.
Collapse
Affiliation(s)
- Takashi Kuromori
- Gene Discovery Research GroupRIKEN Center for Sustainable Resource Science2‐1 HirosawaWakoSaitama351‐0198Japan
| | - Miki Fujita
- Gene Discovery Research GroupRIKEN Center for Sustainable Resource Science3‐1‐1 KoyadaiTsukubaIbaraki305‐0074Japan
| | - Fuminori Takahashi
- Gene Discovery Research GroupRIKEN Center for Sustainable Resource Science3‐1‐1 KoyadaiTsukubaIbaraki305‐0074Japan
- Department of Biological Science and TechnologyGraduate School of Advanced EngineeringTokyo University of Science6‐3‐1 Niijyuku, Katsushika‐kuTokyo125‐8585Japan
| | - Kazuko Yamaguchi‐Shinozaki
- Laboratory of Plant Molecular PhysiologyGraduate School of Agricultural and Life SciencesThe University of Tokyo1‐1‐1 Yayoi, Bunkyo‐kuTokyo113‐8657Japan
- Research Institute for Agricultural and Life SciencesTokyo University of Agriculture1‐1‐1 Sakuragaoka, Setagaya‐kuTokyo156‐8502Japan
| | - Kazuo Shinozaki
- Gene Discovery Research GroupRIKEN Center for Sustainable Resource Science2‐1 HirosawaWakoSaitama351‐0198Japan
- Gene Discovery Research GroupRIKEN Center for Sustainable Resource Science3‐1‐1 KoyadaiTsukubaIbaraki305‐0074Japan
- Biotechonology CenterNational Chung Hsing University (NCHU)Taichung402Taiwan
| |
Collapse
|
17
|
Hilty J, Muller B, Pantin F, Leuzinger S. Plant growth: the What, the How, and the Why. THE NEW PHYTOLOGIST 2021; 232:25-41. [PMID: 34245021 DOI: 10.1111/nph.17610] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 06/19/2021] [Indexed: 05/28/2023]
Abstract
Growth is a widely used term in plant science and ecology, but it can have different meanings depending on the context and the spatiotemporal scale of analysis. At the meristem level, growth is associated with the production of cells and initiation of new organs. At the organ or plant scale and over short time periods, growth is often used synonymously with tissue expansion, while over longer time periods the increase in biomass is a common metric. At even larger temporal and spatial scales, growth is mostly described as net primary production. Here, we first address the question 'what is growth?'. We propose a general framework to distinguish between the different facets of growth, and the corresponding physiological processes, environmental drivers and mathematical formalisms. Based on these different definitions, we then review how plant growth can be measured and analysed at different organisational, spatial and temporal scales. We conclude by discussing why gaining a better understanding of the different facets of plant growth is essential to disentangle genetic and environmental effects on the phenotype, and to uncover the causalities around source or sink limitations of plant growth.
Collapse
Affiliation(s)
- Jonas Hilty
- School of Science, Auckland University of Technology, 46 Wakefield Street, Auckland, 1142, New Zealand
| | - Bertrand Muller
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, 34000, France
| | - Florent Pantin
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, 34000, France
| | - Sebastian Leuzinger
- School of Science, Auckland University of Technology, 46 Wakefield Street, Auckland, 1142, New Zealand
| |
Collapse
|
18
|
Kim M, Lee C, Hong S, Kim SL, Baek JH, Kim KH. High-Throughput Phenotyping Methods for Breeding Drought-Tolerant Crops. Int J Mol Sci 2021; 22:ijms22158266. [PMID: 34361030 PMCID: PMC8347144 DOI: 10.3390/ijms22158266] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/28/2021] [Accepted: 07/29/2021] [Indexed: 12/28/2022] Open
Abstract
Drought is a main factor limiting crop yields. Modern agricultural technologies such as irrigation systems, ground mulching, and rainwater storage can prevent drought, but these are only temporary solutions. Understanding the physiological, biochemical, and molecular reactions of plants to drought stress is therefore urgent. The recent rapid development of genomics tools has led to an increasing interest in phenomics, i.e., the study of phenotypic plant traits. Among phenomic strategies, high-throughput phenotyping (HTP) is attracting increasing attention as a way to address the bottlenecks of genomic and phenomic studies. HTP provides researchers a non-destructive and non-invasive method yet accurate in analyzing large-scale phenotypic data. This review describes plant responses to drought stress and introduces HTP methods that can detect changes in plant phenotypes in response to drought.
Collapse
Affiliation(s)
- Minsu Kim
- National Institute of Agricultural Science, RDA, Wanju 54874, Korea; (M.K.); (C.L.); (S.H.); (S.L.K.); (J.-H.B.)
| | - Chaewon Lee
- National Institute of Agricultural Science, RDA, Wanju 54874, Korea; (M.K.); (C.L.); (S.H.); (S.L.K.); (J.-H.B.)
- Department of Crop Science and Biotechnology, Chonbuk National University, Jeonju 54896, Korea
| | - Subin Hong
- National Institute of Agricultural Science, RDA, Wanju 54874, Korea; (M.K.); (C.L.); (S.H.); (S.L.K.); (J.-H.B.)
| | - Song Lim Kim
- National Institute of Agricultural Science, RDA, Wanju 54874, Korea; (M.K.); (C.L.); (S.H.); (S.L.K.); (J.-H.B.)
| | - Jeong-Ho Baek
- National Institute of Agricultural Science, RDA, Wanju 54874, Korea; (M.K.); (C.L.); (S.H.); (S.L.K.); (J.-H.B.)
| | - Kyung-Hwan Kim
- National Institute of Agricultural Science, RDA, Wanju 54874, Korea; (M.K.); (C.L.); (S.H.); (S.L.K.); (J.-H.B.)
- Correspondence:
| |
Collapse
|
19
|
Souza A, Yang Y. High-Throughput Corn Image Segmentation and Trait Extraction Using Chlorophyll Fluorescence Images. PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9792582. [PMID: 34382005 PMCID: PMC8323024 DOI: 10.34133/2021/9792582] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 06/29/2021] [Indexed: 06/13/2023]
Abstract
Plant segmentation and trait extraction for individual organs are two of the key challenges in high-throughput phenotyping (HTP) operations. To address this challenge, the Ag Alumni Seed Phenotyping Facility (AAPF) at Purdue University utilizes chlorophyll fluorescence images (CFIs) to enable consistent and efficient automatic segmentation of plants of different species, age, or color. A series of image analysis routines were also developed to facilitate the quantitative measurements of key corn plant traits. A proof-of-concept experiment was conducted to demonstrate the utility of the extracted traits in assessing drought stress reaction of corn plants. The image analysis routines successfully measured several corn morphological characteristics for different sizes such as plant height, area, top-node height and diameter, number of leaves, leaf area, and angle in relation to the stem. Data from the proof-of-concept experiment showed how corn plants behaved when treated with different water regiments or grown in pot of different sizes. High-throughput image segmentation and analysis basing on a plant's fluorescence image was proved to be efficient and reliable. Extracted trait on the segmented stem and leaves of a corn plant demonstrated the importance and utility of this kind of trait data in evaluating the performance of corn plant under stress. Data collected from corn plants grown in pots of different volumes showed the importance of using pot of standard size when conducting and reporting plant phenotyping data in a controlled-environment facility.
Collapse
Affiliation(s)
- Augusto Souza
- Institute for Plant Sciences, Purdue University, West Lafayette, IN, USA
| | - Yang Yang
- Institute for Plant Sciences, Purdue University, West Lafayette, IN, USA
| |
Collapse
|
20
|
Review: Application of Artificial Intelligence in Phenomics. SENSORS 2021; 21:s21134363. [PMID: 34202291 PMCID: PMC8271724 DOI: 10.3390/s21134363] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/18/2021] [Accepted: 06/22/2021] [Indexed: 02/04/2023]
Abstract
Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which can enable the high-throughput phenotyping of complex plant traits. The application of artificial intelligence in various domains of science has also grown exponentially in recent years. Notably, the computer vision, machine learning, and deep learning aspects of artificial intelligence have been successfully integrated into non-invasive imaging techniques. This integration is gradually improving the efficiency of data collection and analysis through the application of machine and deep learning for robust image analysis. In addition, artificial intelligence has fostered the development of software and tools applied in field phenotyping for data collection and management. These include open-source devices and tools which are enabling community driven research and data-sharing, thereby availing the large amounts of data required for the accurate study of phenotypes. This paper reviews more than one hundred current state-of-the-art papers concerning AI-applied plant phenotyping published between 2010 and 2020. It provides an overview of current phenotyping technologies and the ongoing integration of artificial intelligence into plant phenotyping. Lastly, the limitations of the current approaches/methods and future directions are discussed.
Collapse
|
21
|
Verbraeken L, Wuyts N, Mertens S, Cannoot B, Maleux K, Demuynck K, De Block J, Merchie J, Dhondt S, Bonaventure G, Crafts-Brandner S, Vogel J, Bruce W, Inzé D, Maere S, Nelissen H. Drought affects the rate and duration of organ growth but not inter-organ growth coordination. PLANT PHYSIOLOGY 2021; 186:1336-1353. [PMID: 33788927 PMCID: PMC8195526 DOI: 10.1093/plphys/kiab155] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 03/02/2021] [Indexed: 05/18/2023]
Abstract
Drought at flowering and grain filling greatly reduces maize (Zea mays) yield. Climate change is causing earlier and longer-lasting periods of drought, which affect the growth of multiple maize organs throughout development. To study how long periods of water deficit impact the dynamic nature of growth, and to determine how these relate to reproductive drought, we employed a high-throughput phenotyping platform featuring precise irrigation, imaging systems, and image-based biomass estimations. Prolonged drought resulted in a reduction of growth rate of individual organs-though an extension of growth duration partially compensated for this-culminating in lower biomass and delayed flowering. However, long periods of drought did not affect the highly organized succession of maximal growth rates of the distinct organs, i.e. leaves, stems, and ears. Two drought treatments negatively affected distinct seed yield components: Prolonged drought mainly reduced the number of spikelets, and drought during the reproductive period increased the anthesis-silking interval. The identification of these divergent biomass and yield components, which were affected by the shift in duration and intensity of drought, will facilitate trait-specific breeding toward future climate-resilient crops.
Collapse
Affiliation(s)
- Lennart Verbraeken
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Nathalie Wuyts
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Stien Mertens
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Bernard Cannoot
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Katrien Maleux
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Kirin Demuynck
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Jolien De Block
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Julie Merchie
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Stijn Dhondt
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | | | | | | | | | - Dirk Inzé
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Steven Maere
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Hilde Nelissen
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
- Author for communication:
| |
Collapse
|
22
|
Smith DT, Potgieter AB, Chapman SC. Scaling up high-throughput phenotyping for abiotic stress selection in the field. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1845-1866. [PMID: 34076731 DOI: 10.1007/s00122-021-03864-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 05/13/2021] [Indexed: 05/18/2023]
Abstract
High-throughput phenotyping (HTP) is in its infancy for deployment in large-scale breeding programmes. With the ability to measure correlated traits associated with physiological ideotypes, in-field phenotyping methods are available for screening of abiotic stress responses. As cropping environments become more hostile and unpredictable due to the effects of climate change, the need to characterise variability across spatial and temporal scales will become increasingly important. The sensor technologies that have enabled HTP from macroscopic through to satellite sensors may also be utilised here to complement spatial characterisation using envirotyping, which can improve estimations of genotypic performance across environments by better accounting for variation at the plot, trial and inter-trial levels. Climate change is leading to increased variation at all physical and temporal scales in the cropping environment. Maintaining yield stability under circumstances with greater levels of abiotic stress while capitalising upon yield potential in good years, requires approaches to plant breeding that target the physiological limitations to crop performance in specific environments. This requires dynamic modelling of conditions within target populations of environments, GxExM predictions, clustering of environments so breeding trajectories can be defined, and the development of screens that enable selection for genetic gain to occur. High-throughput phenotyping (HTP), combined with related technologies used for envirotyping, can help to address these challenges. Non-destructive analysis of the morphological, biochemical and physiological qualities of plant canopies using HTP has great potential to complement whole-genome selection, which is becoming increasingly common in breeding programmes. A range of novel analytic techniques, such as machine learning and deep learning, combined with a widening range of sensors, allow rapid assessment of large breeding populations that are repeatable and objective. Secondary traits underlying radiation use efficiency and water use efficiency can be screened with HTP for selection at the early stages of a breeding programme. HTP and envirotyping technologies can also characterise spatial variability at trial and within-plot levels, which can be used to correct for spatial variations that confound measurements of genotypic values. This review explores HTP for abiotic stress selection through a physiological trait lens and additionally investigates the use of envirotyping and EC to characterise spatial variability at all physical scales in METs.
Collapse
Affiliation(s)
- Daniel T Smith
- The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
| | - Andries B Potgieter
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD, 4072, Australia
| | - Scott C Chapman
- The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia.
| |
Collapse
|
23
|
Zhou S, Chai X, Yang Z, Wang H, Yang C, Sun T. Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping. PLANT METHODS 2021; 17:48. [PMID: 33926480 PMCID: PMC8086349 DOI: 10.1186/s13007-021-00747-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 04/19/2021] [Indexed: 05/26/2023]
Abstract
BACKGROUND Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. RESULTS On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. CONCLUSION The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.
Collapse
Affiliation(s)
- Shuo Zhou
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St., Beijing, 100081 China
- Key Laboratory of Big Agri-Data, Ministry of Agriculture, Beijing, China
| | - Xiujuan Chai
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St., Beijing, 100081 China
- Key Laboratory of Big Agri-Data, Ministry of Agriculture, Beijing, China
| | - Zixuan Yang
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St., Beijing, 100081 China
| | - Hongwu Wang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St., Beijing, 100081 China
| | - Chenxue Yang
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St., Beijing, 100081 China
| | - Tan Sun
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St., Beijing, 100081 China
- Key Laboratory of Big Agri-Data, Ministry of Agriculture, Beijing, China
| |
Collapse
|
24
|
Li D, Quan C, Song Z, Li X, Yu G, Li C, Muhammad A. High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field. Front Bioeng Biotechnol 2021; 8:623705. [PMID: 33520974 PMCID: PMC7838587 DOI: 10.3389/fbioe.2020.623705] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 12/15/2020] [Indexed: 11/13/2022] Open
Abstract
Food scarcity, population growth, and global climate change have propelled crop yield growth driven by high-throughput phenotyping into the era of big data. However, access to large-scale phenotypic data has now become a critical barrier that phenomics urgently must overcome. Fortunately, the high-throughput plant phenotyping platform (HT3P), employing advanced sensors and data collection systems, can take full advantage of non-destructive and high-throughput methods to monitor, quantify, and evaluate specific phenotypes for large-scale agricultural experiments, and it can effectively perform phenotypic tasks that traditional phenotyping could not do. In this way, HT3Ps are novel and powerful tools, for which various commercial, customized, and even self-developed ones have been recently introduced in rising numbers. Here, we review these HT3Ps in nearly 7 years from greenhouses and growth chambers to the field, and from ground-based proximal phenotyping to aerial large-scale remote sensing. Platform configurations, novelties, operating modes, current developments, as well the strengths and weaknesses of diverse types of HT3Ps are thoroughly and clearly described. Then, miscellaneous combinations of HT3Ps for comparative validation and comprehensive analysis are systematically present, for the first time. Finally, we consider current phenotypic challenges and provide fresh perspectives on future development trends of HT3Ps. This review aims to provide ideas, thoughts, and insights for the optimal selection, exploitation, and utilization of HT3Ps, and thereby pave the way to break through current phenotyping bottlenecks in botany.
Collapse
Affiliation(s)
- Daoliang Li
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Chaoqun Quan
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Zhaoyang Song
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Xiang Li
- Department of Psychology, College of Education, Hubei University, Wuhan, China
| | - Guanghui Yu
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Cheng Li
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Akhter Muhammad
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| |
Collapse
|
25
|
Huebbers JW, Buyel JF. On the verge of the market - Plant factories for the automated and standardized production of biopharmaceuticals. Biotechnol Adv 2020; 46:107681. [PMID: 33326816 DOI: 10.1016/j.biotechadv.2020.107681] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 11/19/2020] [Accepted: 12/08/2020] [Indexed: 12/28/2022]
Abstract
The market for biopharmaceuticals is dominated by recombinant proteins and is driven mainly by the development of vaccines and antibodies. Manufacturing predominantly relies on fermentation-based production platforms, which have limited scalability and suffer from high upstream process costs. As an alternative, the production of recombinant proteins in whole plants (plant molecular farming) provides a scalable and cost efficient upstream process because each plant functions as a self-contained bioreactor, avoiding costs associated with single-use devices and cleaning-in-place. Despite many proof-of-concept studies and the approval of a few products as medical devices, the only approved pharmaceutical proteins manufactured in whole plants have been authorized under emergency protocols. The absence of approvals under standard clinical development pathways in part reflects the lack of standardized process equipment and unit operations, leading to industry inertia based on familiarity with fermenter systems. Here we discuss the upstream production steps of plant molecular farming by transient expression in intact plants, including seeding, plant cultivation, infiltration with Agrobacterium tumefaciens, post-infiltration incubation, and harvesting. We focus on cultivation techniques because they strongly affect the subsequent steps and overall process design. We compare the benefits and drawbacks of open field, greenhouse and vertical farm strategies in terms of upfront investment costs, batch reproducibility, and decoupling from environmental impacts. We consider process automation, monitoring and adaptive process design in the context of Industry 4.0, which can boost process efficiency and batch-to-batch uniformity to improve regulatory compliance. Finally, we discuss the costs-benefit aspects of the different cultivation systems.
Collapse
Affiliation(s)
- J W Huebbers
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Forckenbeckstrasse 6, 52074 Aachen, Germany.
| | - J F Buyel
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Forckenbeckstrasse 6, 52074 Aachen, Germany; Institute for Molecular Biotechnology, Worringerweg 1, RWTH Aachen University, 52074 Aachen, Germany.
| |
Collapse
|
26
|
Das Choudhury S, Maturu S, Samal A, Stoerger V, Awada T. Leveraging Image Analysis to Compute 3D Plant Phenotypes Based on Voxel-Grid Plant Reconstruction. FRONTIERS IN PLANT SCIENCE 2020; 11:521431. [PMID: 33362806 PMCID: PMC7755976 DOI: 10.3389/fpls.2020.521431] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 11/17/2020] [Indexed: 05/31/2023]
Abstract
High throughput image-based plant phenotyping facilitates the extraction of morphological and biophysical traits of a large number of plants non-invasively in a relatively short time. It facilitates the computation of advanced phenotypes by considering the plant as a single object (holistic phenotypes) or its components, i.e., leaves and the stem (component phenotypes). The architectural complexity of plants increases over time due to variations in self-occlusions and phyllotaxy, i.e., arrangements of leaves around the stem. One of the central challenges to computing phenotypes from 2-dimensional (2D) single view images of plants, especially at the advanced vegetative stage in presence of self-occluding leaves, is that the information captured in 2D images is incomplete, and hence, the computed phenotypes are inaccurate. We introduce a novel algorithm to compute 3-dimensional (3D) plant phenotypes from multiview images using voxel-grid reconstruction of the plant (3DPhenoMV). The paper also presents a novel method to reliably detect and separate the individual leaves and the stem from the 3D voxel-grid of the plant using voxel overlapping consistency check and point cloud clustering techniques. To evaluate the performance of the proposed algorithm, we introduce the University of Nebraska-Lincoln 3D Plant Phenotyping Dataset (UNL-3DPPD). A generic taxonomy of 3D image-based plant phenotypes are also presented to promote 3D plant phenotyping research. A subset of these phenotypes are computed using computer vision algorithms with discussion of their significance in the context of plant science. The central contributions of the paper are (a) an algorithm for 3D voxel-grid reconstruction of maize plants at the advanced vegetative stages using images from multiple 2D views; (b) a generic taxonomy of 3D image-based plant phenotypes and a public benchmark dataset, i.e., UNL-3DPPD, to promote the development of 3D image-based plant phenotyping research; and (c) novel voxel overlapping consistency check and point cloud clustering techniques to detect and isolate individual leaves and stem of the maize plants to compute the component phenotypes. Detailed experimental analyses demonstrate the efficacy of the proposed method, and also show the potential of 3D phenotypes to explain the morphological characteristics of plants regulated by genetic and environmental interactions.
Collapse
Affiliation(s)
- Sruti Das Choudhury
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Srikanth Maturu
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Ashok Samal
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Vincent Stoerger
- Agricultural Research Division, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Tala Awada
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States
- Agricultural Research Division, University of Nebraska-Lincoln, Lincoln, NE, United States
| |
Collapse
|
27
|
Nguyen GN, Norton SL. Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm. PLANTS (BASEL, SWITZERLAND) 2020; 9:E817. [PMID: 32610615 PMCID: PMC7411623 DOI: 10.3390/plants9070817] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 06/25/2020] [Accepted: 06/26/2020] [Indexed: 02/07/2023]
Abstract
Genetically diverse plant germplasm stored in ex-situ genebanks are excellent resources for breeding new high yielding and sustainable crop varieties to ensure future food security. Novel alleles have been discovered through routine genebank activities such as seed regeneration and characterization, with subsequent utilization providing significant genetic gains and improvements for the selection of favorable traits, including yield, biotic, and abiotic resistance. Although some genebanks have implemented cost-effective genotyping technologies through advances in DNA technology, the adoption of modern phenotyping is lagging. The introduction of advanced phenotyping technologies in recent decades has provided genebank scientists with time and cost-effective screening tools to obtain valuable phenotypic data for more traits on large germplasm collections during routine activities. The utilization of these phenotyping tools, coupled with high-throughput genotyping, will accelerate the use of genetic resources and fast-track the development of more resilient food crops for the future. In this review, we highlight current digital phenotyping methods that can capture traits during annual seed regeneration to enrich genebank phenotypic datasets. Next, we describe strategies for the collection and use of phenotypic data of specific traits for downstream research using high-throughput phenotyping technology. Finally, we examine the challenges and future perspectives of genebank phenomics.
Collapse
Affiliation(s)
- Giao N. Nguyen
- Australian Grains Genebank, Agriculture Victoria, 110 Natimuk Road, Horsham 3400, Australia;
| | | |
Collapse
|
28
|
Wu S, Wen W, Wang Y, Fan J, Wang C, Gou W, Guo X. MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction. PLANT PHENOMICS (WASHINGTON, D.C.) 2020; 2020:1848437. [PMID: 33313542 PMCID: PMC7706320 DOI: 10.34133/2020/1848437] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 02/19/2020] [Indexed: 05/26/2023]
Abstract
Plant phenotyping technologies play important roles in plant research and agriculture. Detailed phenotypes of individual plants can guide the optimization of shoot architecture for plant breeding and are useful to analyze the morphological differences in response to environments for crop cultivation. Accordingly, high-throughput phenotyping technologies for individual plants grown in field conditions are urgently needed, and MVS-Pheno, a portable and low-cost phenotyping platform for individual plants, was developed. The platform is composed of four major components: a semiautomatic multiview stereo (MVS) image acquisition device, a data acquisition console, data processing and phenotype extraction software for maize shoots, and a data management system. The platform's device is detachable and adjustable according to the size of the target shoot. Image sequences for each maize shoot can be captured within 60-120 seconds, yielding 3D point clouds of shoots are reconstructed using MVS-based commercial software, and the phenotypic traits at the organ and individual plant levels are then extracted by the software. The correlation coefficient (R 2) between the extracted and manually measured plant height, leaf width, and leaf area values are 0.99, 0.87, and 0.93, respectively. A data management system has also been developed to store and manage the acquired raw data, reconstructed point clouds, agronomic information, and resulting phenotypic traits. The platform offers an optional solution for high-throughput phenotyping of field-grown plants, which is especially useful for large populations or experiments across many different ecological regions.
Collapse
Affiliation(s)
- Sheng Wu
- Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Key Lab of Digital Plant, Beijing 100097, China
| | - Weiliang Wen
- Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Key Lab of Digital Plant, Beijing 100097, China
| | - Yongjian Wang
- Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Key Lab of Digital Plant, Beijing 100097, China
| | - Jiangchuan Fan
- Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Key Lab of Digital Plant, Beijing 100097, China
| | - Chuanyu Wang
- Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Key Lab of Digital Plant, Beijing 100097, China
| | - Wenbo Gou
- Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Key Lab of Digital Plant, Beijing 100097, China
| | - Xinyu Guo
- Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Key Lab of Digital Plant, Beijing 100097, China
| |
Collapse
|
29
|
Chaudhury A, Godin C. Skeletonization of Plant Point Cloud Data in Stochastic Optimization Framework.. [DOI: 10.1101/2020.02.15.950519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
Abstract
AbstractSkeleton extraction from 3D plant point cloud data is an essential prior for myriads of phenotyping studies. Although skeleton extraction from 3D shapes have been studied extensively in the computer vision and graphics literature, handling the case of plants is still an open problem. Drawbacks of the existing approaches include the zigzag structure of the skeleton, nonuniform density of skeleton points, lack of points in the areas having complex geometry structure, and most importantly the lack of biological relevance. With the aim to improve existing skeleton structures of state-of-the-art, we propose a stochastic framework which is supported by the biological structure of the original plant (we consider plants without any leaves). Initially we estimate the branching structure of the plant by the notion of β-splines to form a curve tree defined as a finite set of curves joined in a tree topology with certain level of smoothness. In the next phase, we force the discrete points in the curve tree to move towards the original point cloud by treating each point in the curve tree as a center of Gaussian, and points in the input cloud data as observations from the Gaussians. The task is to find the correct locations of the Gaussian centroids by maximizing a likelihood. The optimization technique is iterative and is based on the Expectation Maximization (EM) algorithm. The E-step estimates which Gaussian the observed point cloud was sampled from, and the M-step maximizes the negative log-likelihood that the observed points were sampled from the Gaussian Mixture Model (GMM) with respect to the model parameters. We experiment with several real world and synthetic datasets and demonstrate the robustness of the approach over the state-of-the-art.
Collapse
|
30
|
Yang W, Feng H, Zhang X, Zhang J, Doonan JH, Batchelor WD, Xiong L, Yan J. Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives. MOLECULAR PLANT 2020; 13:187-214. [PMID: 31981735 DOI: 10.1016/j.molp.2020.01.008] [Citation(s) in RCA: 239] [Impact Index Per Article: 59.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 01/06/2020] [Accepted: 01/10/2020] [Indexed: 05/18/2023]
Abstract
Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However, acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years. In this article, we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades. We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies. Finally, we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap. It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.
Collapse
Affiliation(s)
- Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China.
| | - Hui Feng
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Xuehai Zhang
- National Key Laboratory of Wheat and Maize Crops Science/College of Agronomy, Henan Agricultural University, Zhengzhou 450002, P.R. China
| | - Jian Zhang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - John H Doonan
- The National Plant Phenomics Centre, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | | | - Lizhong Xiong
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| |
Collapse
|
31
|
Chaudhury A, Godin C. Skeletonization of Plant Point Cloud Data Using Stochastic Optimization Framework. FRONTIERS IN PLANT SCIENCE 2020; 11:773. [PMID: 32612619 PMCID: PMC7309182 DOI: 10.3389/fpls.2020.00773] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Accepted: 05/15/2020] [Indexed: 05/14/2023]
Abstract
Skeleton extraction from 3D plant point cloud data is an essential prior for myriads of phenotyping studies. Although skeleton extraction from 3D shapes have been studied extensively in the computer vision and graphics literature, handling the case of plants is still an open problem. Drawbacks of the existing approaches include the zigzag structure of the skeleton, nonuniform density of skeleton points, lack of points in the areas having complex geometry structure, and most importantly the lack of biological relevance. With the aim to improve existing skeleton structures of state-of-the-art, we propose a stochastic framework which is supported by the biological structure of the original plant (we consider plants without any leaves). Initially we estimate the branching structure of the plant by the notion of β-splines to form a curve tree defined as a finite set of curves joined in a tree topology with certain level of smoothness. In the next phase, we force the discrete points in the curve tree to move toward the original point cloud by treating each point in the curve tree as a center of Gaussian, and points in the input cloud data as observations from the Gaussians. The task is to find the correct locations of the Gaussian centroids by maximizing a likelihood. The optimization technique is iterative and is based on the Expectation Maximization (EM) algorithm. The E-step estimates which Gaussian the observed point cloud was sampled from, and the M-step maximizes the negative log-likelihood that the observed points were sampled from the Gaussian Mixture Model (GMM) with respect to the model parameters. We experiment with several real world and synthetic datasets and demonstrate the robustness of the approach over the state-of-the-art.
Collapse
Affiliation(s)
- Ayan Chaudhury
- INRIA Grenoble Rhône-Alpes, Team MOSAIC, Lyon, France
- Laboratoire Reproduction et Développement des Plantes, Univ Lyon, ENS de Lyon, UCB Lyon 1, CNRS, INRA, Lyon, France
| | - Christophe Godin
- INRIA Grenoble Rhône-Alpes, Team MOSAIC, Lyon, France
- Laboratoire Reproduction et Développement des Plantes, Univ Lyon, ENS de Lyon, UCB Lyon 1, CNRS, INRA, Lyon, France
- *Correspondence: Christophe Godin
| |
Collapse
|
32
|
Gedil M, Menkir A. An Integrated Molecular and Conventional Breeding Scheme for Enhancing Genetic Gain in Maize in Africa. FRONTIERS IN PLANT SCIENCE 2019; 10:1430. [PMID: 31781144 PMCID: PMC6851238 DOI: 10.3389/fpls.2019.01430] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 10/15/2019] [Indexed: 05/22/2023]
Abstract
Maize production in West and Central Africa (WCA) is constrained by a wide range of interacting stresses that keep productivity below potential yields. Among the many problems afflicting maize production in WCA, drought, foliar diseases, and parasitic weeds are the most critical. Several decades of efforts devoted to the genetic improvement of maize have resulted in remarkable genetic gain, leading to increased yields of maize on farmers' fields. The revolution unfolding in the areas of genomics, bioinformatics, and phenomics is generating innovative tools, resources, and technologies for transforming crop breeding programs. It is envisaged that such tools will be integrated within maize breeding programs, thereby advancing these programs and addressing current and future challenges. Accordingly, the maize improvement program within International Institute of Tropical Agriculture (IITA) is undergoing a process of modernization through the introduction of innovative tools and new schemes that are expected to enhance genetic gains and impact on smallholder farmers in the region. Genomic tools enable genetic dissections of complex traits and promote an understanding of the physiological basis of key agronomic and nutritional quality traits. Marker-aided selection and genome-wide selection schemes are being implemented to accelerate genetic gain relating to yield, resilience, and nutritional quality. Therefore, strategies that effectively combine genotypic information with data from field phenotyping and laboratory-based analysis are currently being optimized. Molecular breeding, guided by methodically defined product profiles tailored to different agroecological zones and conditions of climate change, supported by state-of-the-art decision-making tools, is pivotal for the advancement of modern, genomics-aided maize improvement programs. Accelerated genetic gain, in turn, catalyzes a faster variety replacement rate. It is critical to forge and strengthen partnerships for enhancing the impacts of breeding products on farmers' livelihood. IITA has well-established channels for delivering its research products/technologies to partner organizations for further testing, multiplication, and dissemination across various countries within the subregion. Capacity building of national agricultural research system (NARS) will facilitate the smooth transfer of technologies and best practices from IITA and its partners.
Collapse
Affiliation(s)
- Melaku Gedil
- Bioscience Center and Maize Improvement Program, International Institute of Tropical Agriculture, Ibadan, Nigeria
| | - Abebe Menkir
- Maize Improvement Program, International Institute of Tropical Agriculture, Ibadan, Nigeria
| |
Collapse
|
33
|
Koch G, Rolland G, Dauzat M, Bédiée A, Baldazzi V, Bertin N, Guédon Y, Granier C. Leaf Production and Expansion: A Generalized Response to Drought Stresses from Cells to Whole Leaf Biomass-A Case Study in the Tomato Compound Leaf. PLANTS (BASEL, SWITZERLAND) 2019; 8:E409. [PMID: 31614737 PMCID: PMC6843756 DOI: 10.3390/plants8100409] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 10/01/2019] [Accepted: 10/09/2019] [Indexed: 11/30/2022]
Abstract
It is clearly established that there is not a unique response to soil water deficit but that there are as many responses as soil water deficit characteristics: Drought intensity, drought duration, and drought position during plant cycle. For a same soil water deficit, responses can also differ on plant genotype within a same species. In spite of this variability, at least for leaf production and expansion processes, robust tendencies can be extracted from the literature when similar watering regimes are compared. Here, we present response curves and multi-scale dynamics analyses established on tomato plants exposed to different soil water deficit treatments. Results reinforce the trends already observed for other species: Reduction in plant leaf biomass under water stress was due to reduction in individual leaf biomass and areas whereas leaf production and specific leaf area were not affected. The dynamics of leaf expansion was modified both at the leaf and cell scales. Cell division and expansion were reduced by drought treatments as well as the endoreduplication process. Combining response curves analyses together with dynamic analyses of tomato compound leaf growth at different scales not only corroborate results on simple leaf responses to drought but also increases our knowledge on the cellular mechanisms behind leaf growth plasticity.
Collapse
Affiliation(s)
- Garance Koch
- Univ Montpellier, INRA, Montpellier SupAgro, LEPSE, 34095 Montpellier, France.
- Unité Plantes et Systèmes de culture Horticoles, INRA, UR 1115 PSH, F-84000 Avignon, France.
| | - Gaëlle Rolland
- Univ Montpellier, INRA, Montpellier SupAgro, LEPSE, 34095 Montpellier, France.
| | - Myriam Dauzat
- Univ Montpellier, INRA, Montpellier SupAgro, LEPSE, 34095 Montpellier, France.
| | - Alexis Bédiée
- Univ Montpellier, INRA, Montpellier SupAgro, LEPSE, 34095 Montpellier, France.
| | - Valentina Baldazzi
- Unité Plantes et Systèmes de culture Horticoles, INRA, UR 1115 PSH, F-84000 Avignon, France.
- Université Côte d'Azur, INRA, CNRS, ISA, 06903 Sophia-Antipolis, France.
- Université Côte d'Azur, INRIA, INRA, CNRS, Sorbonne Université, BIOCORE Team, 06903 Sophia-Antipolis, France.
| | - Nadia Bertin
- Unité Plantes et Systèmes de culture Horticoles, INRA, UR 1115 PSH, F-84000 Avignon, France.
| | - Yann Guédon
- Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, AGAP, 34095 Montpellier, France.
| | - Christine Granier
- Univ Montpellier, INRA, Montpellier SupAgro, LEPSE, 34095 Montpellier, France.
- Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, AGAP, 34095 Montpellier, France.
| |
Collapse
|
34
|
Perez RPA, Fournier C, Cabrera-Bosquet L, Artzet S, Pradal C, Brichet N, Chen TW, Chapuis R, Welcker C, Tardieu F. Changes in the vertical distribution of leaf area enhanced light interception efficiency in maize over generations of selection. PLANT, CELL & ENVIRONMENT 2019; 42:2105-2119. [PMID: 30801738 DOI: 10.1111/pce.13539] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 02/14/2019] [Accepted: 02/14/2019] [Indexed: 06/09/2023]
Abstract
Breeders select for yield, thereby indirectly selecting for traits that contribute to it. We tested if breeding has affected a range of traits involved in plant architecture and light interception, via the analysis of a panel of 60 maize hybrids released from 1950 to 2015. This was based on novel traits calculated from reconstructions derived from a phenotyping platform. The contribution of these traits to light interception was assessed in virtual field canopies composed of 3D plant reconstructions, with a model tested in a real field. Two categories of traits had different contributions to genetic progress. (a) The vertical distribution of leaf area had a high heritability and showed a marked trend over generations of selection. Leaf area tended to be located at lower positions in the canopy, thereby improving light penetration and distribution in the canopy. This potentially increased the carbon availability to ears, via the amount of light absorbed by the intermediate canopy layer. (b) Neither the horizontal distribution of leaves in the relation to plant rows nor the response of light interception to plant density showed appreciable trends with generations. Hence, among many architectural traits, the vertical distribution of leaf area was the main indirect target of selection.
Collapse
Affiliation(s)
- Raphaël P A Perez
- Université de Montpellier, INRA, Montpellier SupAgro, UMR LEPSE, Montpellier, France
- Université de Montpellier, CIRAD, INRA, Montpellier SupAgro, UMR AGAP, Montpellier, France
| | - Christian Fournier
- Université de Montpellier, INRA, Montpellier SupAgro, UMR LEPSE, Montpellier, France
| | | | - Simon Artzet
- Université de Montpellier, INRA, Montpellier SupAgro, UMR LEPSE, Montpellier, France
| | - Christophe Pradal
- Université de Montpellier, CIRAD, INRA, Montpellier SupAgro, UMR AGAP, Montpellier, France
| | - Nicolas Brichet
- Université de Montpellier, INRA, Montpellier SupAgro, UMR LEPSE, Montpellier, France
| | - Tsu-Wei Chen
- Université de Montpellier, INRA, Montpellier SupAgro, UMR LEPSE, Montpellier, France
- Institute of Horticultural Production Systems, Leibniz Universität Hannover, Hannover, Germany
| | - Romain Chapuis
- Université de Montpellier, INRA, Montpellier SupAgro, UE DIASCOPE, Montpellier, France
| | - Claude Welcker
- Université de Montpellier, INRA, Montpellier SupAgro, UMR LEPSE, Montpellier, France
| | - François Tardieu
- Université de Montpellier, INRA, Montpellier SupAgro, UMR LEPSE, Montpellier, France
| |
Collapse
|
35
|
Zhao C, Zhang Y, Du J, Guo X, Wen W, Gu S, Wang J, Fan J. Crop Phenomics: Current Status and Perspectives. FRONTIERS IN PLANT SCIENCE 2019; 10:714. [PMID: 31214228 PMCID: PMC6557228 DOI: 10.3389/fpls.2019.00714] [Citation(s) in RCA: 136] [Impact Index Per Article: 27.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 05/14/2019] [Indexed: 05/19/2023]
Abstract
Reliable, automatic, multifunctional, and high-throughput phenotypic technologies are increasingly considered important tools for rapid advancement of genetic gain in breeding programs. With the rapid development in high-throughput phenotyping technologies, research in this area is entering a new era called 'phenomics.' The crop phenotyping community not only needs to build a multi-domain, multi-level, and multi-scale crop phenotyping big database, but also to research technical systems for phenotypic traits identification and develop bioinformatics technologies for information extraction from the overwhelming amounts of omics data. Here, we provide an overview of crop phenomics research, focusing on two parts, from phenotypic data collection through various sensors to phenomics analysis. Finally, we discussed the challenges and prospective of crop phenomics in order to provide suggestions to develop new methods of mining genes associated with important agronomic traits, and propose new intelligent solutions for precision breeding.
Collapse
|
36
|
Roitsch T, Cabrera-Bosquet L, Fournier A, Ghamkhar K, Jiménez-Berni J, Pinto F, Ober ES. Review: New sensors and data-driven approaches-A path to next generation phenomics. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2019; 282:2-10. [PMID: 31003608 PMCID: PMC6483971 DOI: 10.1016/j.plantsci.2019.01.011] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 12/15/2018] [Accepted: 01/09/2019] [Indexed: 05/19/2023]
Abstract
At the 4th International Plant Phenotyping Symposium meeting of the International Plant Phenotyping Network (IPPN) in 2016 at CIMMYT in Mexico, a workshop was convened to consider ways forward with sensors for phenotyping. The increasing number of field applications provides new challenges and requires specialised solutions. There are many traits vital to plant growth and development that demand phenotyping approaches that are still at early stages of development or elude current capabilities. Further, there is growing interest in low-cost sensor solutions, and mobile platforms that can be transported to the experiments, rather than the experiment coming to the platform. Various types of sensors are required to address diverse needs with respect to targets, precision and ease of operation and readout. Converting data into knowledge, and ensuring that those data (and the appropriate metadata) are stored in such a way that they will be sensible and available to others now and for future analysis is also vital. Here we are proposing mechanisms for "next generation phenomics" based on our learning in the past decade, current practice and discussions at the IPPN Symposium, to encourage further thinking and collaboration by plant scientists, physicists and engineering experts.
Collapse
Affiliation(s)
- Thomas Roitsch
- Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark; Department of Adaptive Biotechnologies, Global Change Research Institute, CAS, Brno, Czech Republic
| | | | - Antoine Fournier
- Arvalis, Institut du végétal, 45, voie Romaine 41240 Beauce la Romaine, France
| | - Kioumars Ghamkhar
- Forage Science, Grasslands Research Centre, AgResearch, Tennent Drive, Fitzherbert, Palmerston North 4410, New Zealand
| | - José Jiménez-Berni
- Instituto de Agricultura Sostenible, Consejo Superior de Investigaciones Cientificas (CSIC) Avenida Menéndez Pidal, Campus Alameda del Obispo, 14004 Córdoba, Spain
| | - Francisco Pinto
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), El Batán, Texcoco, México C.P. 56237, Mexico
| | - Eric S Ober
- National Institute of Agricultural Botany (NIAB), Huntingdon Road, Cambridge, CB3 0LE, UK.
| |
Collapse
|
37
|
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.
Collapse
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
| | | | | |
Collapse
|
38
|
Das Choudhury S, Samal A, Awada T. Leveraging Image Analysis for High-Throughput Plant Phenotyping. FRONTIERS IN PLANT SCIENCE 2019; 10:508. [PMID: 31068958 PMCID: PMC6491831 DOI: 10.3389/fpls.2019.00508] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Accepted: 04/02/2019] [Indexed: 05/18/2023]
Abstract
The complex interaction between a genotype and its environment controls the biophysical properties of a plant, manifested in observable traits, i.e., plant's phenome, which influences resources acquisition, performance, and yield. High-throughput automated image-based plant phenotyping refers to the sensing and quantifying plant traits non-destructively by analyzing images captured at regular intervals and with precision. While phenomic research has drawn significant attention in the last decade, extracting meaningful and reliable numerical phenotypes from plant images especially by considering its individual components, e.g., leaves, stem, fruit, and flower, remains a critical bottleneck to the translation of advances of phenotyping technology into genetic insights due to various challenges including lighting variations, plant rotations, and self-occlusions. The paper provides (1) a framework for plant phenotyping in a multimodal, multi-view, time-lapsed, high-throughput imaging system; (2) a taxonomy of phenotypes that may be derived by image analysis for better understanding of morphological structure and functional processes in plants; (3) a brief discussion on publicly available datasets to encourage algorithm development and uniform comparison with the state-of-the-art methods; (4) an overview of the state-of-the-art image-based high-throughput plant phenotyping methods; and (5) open problems for the advancement of this research field.
Collapse
Affiliation(s)
- Sruti Das Choudhury
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Ashok Samal
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Tala Awada
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States
- Agricultural Research Division, University of Nebraska-Lincoln, Lincoln, NE, United States
| |
Collapse
|
39
|
3-D Image-Driven Morphological Crop Analysis: A Novel Method for Detection of Sunflower Broomrape Initial Subsoil Parasitism. SENSORS 2019; 19:s19071569. [PMID: 30939774 PMCID: PMC6479595 DOI: 10.3390/s19071569] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 03/18/2019] [Accepted: 03/28/2019] [Indexed: 01/29/2023]
Abstract
Effective control of the parasitic weed sunflower broomrape (Orobanche cumana Wallr.) can be achieved by herbicides application in early parasitism stages. However, the growing environmental concerns associated with herbicide treatments have motivated the adoption of precise chemical control approaches that detect and treat infested areas exclusively. The main challenge in developing such control practices for O. cumana lies in the fact that most of its life-cycle occurs in the soil sub-surface and by the time shoots emerge and become observable, the damage to the crop is irreversible. This paper approaches early O. cumana detection by hypothesizing that its parasitism already impacts the host plant morphology at the sub-soil surface developmental stage. To validate this hypothesis, O. cumana- infested sunflower and non-infested control plants were grown in pots and imaged weekly over 45-day period. Three-dimensional plant models were reconstructed using image-based multi-view stereo followed by derivation of their morphological parameters, down to the organ-level. Among the parameters estimated, height and first internode length were the earliest definitive indicators of infection. Furthermore, the detection timing of both parameters was early enough for herbicide post-emergence application. Considering the fact that 3-D morphological modeling is nondestructive, is based on commercially available RGB sensors and can be used under natural illumination; this approach holds potential contribution for site specific pre-emergence managements of parasitic weeds and as a phenotyping tool in O. cumana resistant sunflower breeding projects.
Collapse
|
40
|
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.
Collapse
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
| |
Collapse
|
41
|
Mochida K, Koda S, Inoue K, Hirayama T, Tanaka S, Nishii R, Melgani F. Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective. Gigascience 2019; 8:5232233. [PMID: 30520975 PMCID: PMC6312910 DOI: 10.1093/gigascience/giy153] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 09/06/2018] [Accepted: 11/24/2018] [Indexed: 11/29/2022] Open
Abstract
Employing computer vision to extract useful information from images and videos is becoming a key technique for identifying phenotypic changes in plants. Here, we review the emerging aspects of computer vision for automated plant phenotyping. Recent advances in image analysis empowered by machine learning-based techniques, including convolutional neural network-based modeling, have expanded their application to assist high-throughput plant phenotyping. Combinatorial use of multiple sensors to acquire various spectra has allowed us to noninvasively obtain a series of datasets, including those related to the development and physiological responses of plants throughout their life. Automated phenotyping platforms accelerate the elucidation of gene functions associated with traits in model plants under controlled conditions. Remote sensing techniques with image collection platforms, such as unmanned vehicles and tractors, are also emerging for large-scale field phenotyping for crop breeding and precision agriculture. Computer vision-based phenotyping will play significant roles in both the nowcasting and forecasting of plant traits through modeling of genotype/phenotype relationships.
Collapse
Affiliation(s)
- Keiichi Mochida
- Bioproductivity Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Microalgae Production Control Technology Laboratory, RIKEN Baton Zone Program, RIKEN Cluster for Science, Technology and Innovation Hub, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Institute of Plant Science and Resources, Okayama University, 2-20-1 Chuo, Kurashiki, Okayama 710-0046, Japan
- Kihara Institute for Biological Research, Yokohama City University, 641-12 Maioka-cho, Totsuka-ku, Yokohama, Kanagawa 244–0813, Japan
- Graduate School of Nanobioscience, Yokohama City University, 22-2 Seto, Kanazawa-ku, Yokohama, Kanagawa 236-0027, Japan
| | - Satoru Koda
- Graduate School of Mathematics, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
| | - Komaki Inoue
- Bioproductivity Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Takashi Hirayama
- Institute of Plant Science and Resources, Okayama University, 2-20-1 Chuo, Kurashiki, Okayama 710-0046, Japan
| | - Shojiro Tanaka
- Hiroshima University of Economics, 5-37-1, Gion, Asaminami, Hiroshima-shi Hiroshima 731-0138, Japan
| | - Ryuei Nishii
- Institute of Mathematics for Industry, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
| | - Farid Melgani
- Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, 38123 Trento, Italy
| |
Collapse
|
42
|
Mochida K, Koda S, Inoue K, Hirayama T, Tanaka S, Nishii R, Melgani F. Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective. Gigascience 2019. [PMID: 30520975 DOI: 10.1093/gigascience/giy153/5232233] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023] Open
Abstract
Employing computer vision to extract useful information from images and videos is becoming a key technique for identifying phenotypic changes in plants. Here, we review the emerging aspects of computer vision for automated plant phenotyping. Recent advances in image analysis empowered by machine learning-based techniques, including convolutional neural network-based modeling, have expanded their application to assist high-throughput plant phenotyping. Combinatorial use of multiple sensors to acquire various spectra has allowed us to noninvasively obtain a series of datasets, including those related to the development and physiological responses of plants throughout their life. Automated phenotyping platforms accelerate the elucidation of gene functions associated with traits in model plants under controlled conditions. Remote sensing techniques with image collection platforms, such as unmanned vehicles and tractors, are also emerging for large-scale field phenotyping for crop breeding and precision agriculture. Computer vision-based phenotyping will play significant roles in both the nowcasting and forecasting of plant traits through modeling of genotype/phenotype relationships.
Collapse
Affiliation(s)
- Keiichi Mochida
- Bioproductivity Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Microalgae Production Control Technology Laboratory, RIKEN Baton Zone Program, RIKEN Cluster for Science, Technology and Innovation Hub, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Institute of Plant Science and Resources, Okayama University, 2-20-1 Chuo, Kurashiki, Okayama 710-0046, Japan
- Kihara Institute for Biological Research, Yokohama City University, 641-12 Maioka-cho, Totsuka-ku, Yokohama, Kanagawa 244-0813, Japan
- Graduate School of Nanobioscience, Yokohama City University, 22-2 Seto, Kanazawa-ku, Yokohama, Kanagawa 236-0027, Japan
| | - Satoru Koda
- Graduate School of Mathematics, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
| | - Komaki Inoue
- Bioproductivity Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Takashi Hirayama
- Institute of Plant Science and Resources, Okayama University, 2-20-1 Chuo, Kurashiki, Okayama 710-0046, Japan
| | - Shojiro Tanaka
- Hiroshima University of Economics, 5-37-1, Gion, Asaminami, Hiroshima-shi Hiroshima 731-0138, Japan
| | - Ryuei Nishii
- Institute of Mathematics for Industry, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
| | - Farid Melgani
- Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, 38123 Trento, Italy
| |
Collapse
|
43
|
Neveu P, Tireau A, Hilgert N, Nègre V, Mineau‐Cesari J, Brichet N, Chapuis R, Sanchez I, Pommier C, Charnomordic B, Tardieu F, Cabrera‐Bosquet L. Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System. THE NEW PHYTOLOGIST 2019; 221:588-601. [PMID: 30152011 PMCID: PMC6585972 DOI: 10.1111/nph.15385] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Accepted: 07/07/2018] [Indexed: 05/13/2023]
Abstract
Phenomic datasets need to be accessible to the scientific community. Their reanalysis requires tracing relevant information on thousands of plants, sensors and events. The open-source Phenotyping Hybrid Information System (PHIS) is proposed for plant phenotyping experiments in various categories of installations (field, glasshouse). It unambiguously identifies all objects and traits in an experiment and establishes their relations via ontologies and semantics that apply to both field and controlled conditions. For instance, the genotype is declared for a plant or plot and is associated with all objects related to it. Events such as successive plant positions, anomalies and annotations are associated with objects so they can be easily retrieved. Its ontology-driven architecture is a powerful tool for integrating and managing data from multiple experiments and platforms, for creating relationships between objects and enriching datasets with knowledge and metadata. It interoperates with external resources via web services, thereby allowing data integration into other systems; for example, modelling platforms or external databases. It has the potential for rapid diffusion because of its ability to integrate, manage and visualize multi-source and multi-scale data, but also because it is based on 10 yr of trial and error in our groups.
Collapse
Affiliation(s)
- Pascal Neveu
- MISTEA, INRA, Montpellier SupAgro, Université de MontpellierMontpellier34060France
| | - Anne Tireau
- MISTEA, INRA, Montpellier SupAgro, Université de MontpellierMontpellier34060France
| | - Nadine Hilgert
- MISTEA, INRA, Montpellier SupAgro, Université de MontpellierMontpellier34060France
| | - Vincent Nègre
- LEPSE, INRA, Montpellier SupAgro, Université de MontpellierMontpellier34060France
| | - Jonathan Mineau‐Cesari
- MISTEA, INRA, Montpellier SupAgro, Université de MontpellierMontpellier34060France
- LEPSE, INRA, Montpellier SupAgro, Université de MontpellierMontpellier34060France
| | - Nicolas Brichet
- LEPSE, INRA, Montpellier SupAgro, Université de MontpellierMontpellier34060France
| | - Romain Chapuis
- UE DIASCOPE, INRA, Montpellier SupAgro, Université de MontpellierMontpellier34060France
| | - Isabelle Sanchez
- MISTEA, INRA, Montpellier SupAgro, Université de MontpellierMontpellier34060France
| | - Cyril Pommier
- INRA, UR1164 URGI – Research Unit in Genomics‐InfoINRA de Versailles‐GrignonRoute de Saint‐CyrVersailles78026France
| | | | - François Tardieu
- LEPSE, INRA, Montpellier SupAgro, Université de MontpellierMontpellier34060France
| | | |
Collapse
|
44
|
Maize Plant Phenotyping: Comparing 3D Laser Scanning, Multi-View Stereo Reconstruction, and 3D Digitizing Estimates. REMOTE SENSING 2018. [DOI: 10.3390/rs11010063] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
High-throughput phenotyping technologies have become an increasingly important topic of crop science in recent years. Various sensors and data acquisition approaches have been applied to acquire the phenotyping traits. It is quite confusing for crop phenotyping researchers to determine an appropriate way for their application. In this study, three representative three-dimensional (3D) data acquisition approaches, including 3D laser scanning, multi-view stereo (MVS) reconstruction, and 3D digitizing, were evaluated for maize plant phenotyping in multi growth stages. Phenotyping traits accuracy, post-processing difficulty, device cost, data acquisition efficiency, and automation were considered during the evaluation process. 3D scanning provided satisfactory point clouds for medium and high maize plants with acceptable efficiency, while the results were not satisfactory for small maize plants. The equipment used in 3D scanning is expensive, but is highly automatic. MVS reconstruction provided satisfactory point clouds for small and medium plants, and point deviations were observed in upper parts of higher plants. MVS data acquisition, using low-cost cameras, exhibited the highest efficiency among the three evaluated approaches. The one-by-one pipeline data acquisition pattern allows the use of MVS high-throughput in further phenotyping platforms. Undoubtedly, enhancement of point cloud processing technologies is required to improve the extracted phenotyping traits accuracy for both 3D scanning and MVS reconstruction. Finally, 3D digitizing was time-consuming and labor intensive. However, it does not depend on any post-processing algorithms to extract phenotyping parameters and reliable phenotyping traits could be derived. The promising accuracy of 3D digitizing is a better verification choice for other 3D phenotyping approaches. Our study provides clear reference about phenotyping data acquisition of maize plants, especially for the affordable and portable field phenotyping platforms to be developed.
Collapse
|
45
|
Turc O, Tardieu F. Drought affects abortion of reproductive organs by exacerbating developmentally driven processes via expansive growth and hydraulics. JOURNAL OF EXPERIMENTAL BOTANY 2018; 69:3245-3254. [PMID: 29546424 DOI: 10.1093/jxb/ery078] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 02/21/2018] [Indexed: 05/18/2023]
Abstract
Abortion of reproductive organs is a major limiting factor of yield under water deficit, but is also a trait selected for by evolutionary processes. The youngest reproductive organs must be prone to abortion so older organs can finish their development in case of limited resources. Water deficit increases natural abortion via two developmentally driven processes, namely a signal from the first fertilized ovaries and a simultaneous arrest of the expansive growth of all ovaries at a precise stage. In maize (Zea mays) subjected to water deficits typically encountered in dryland agriculture, these developmental mechanisms account for 90% of drought-associated abortion and are irreversible 3 d after silk emergence. Consistently, transcripts and enzyme activities suggest that the molecular events associated with abortion affect expansive growth in silks whereas ovaries maintain a favourable carbon status. Abortion due to carbon starvation is only observed for severe drought scenarios occurring after silking. Both kinetic and genetic evidence indicates that vegetative and reproductive structures share a partly common hydraulic control of expansive growth. Hence, the control of expansive growth of reproductive structures probably has a prominent effect on abortion for mild water deficits occurring at flowering time, while carbon starvation dominates in severe post-flowering drought scenarios.
Collapse
Affiliation(s)
- Olivier Turc
- LEPSE, INRA, Montpellier SupAgro, Univ Montpellier, Montpellier, France
| | - François Tardieu
- LEPSE, INRA, Montpellier SupAgro, Univ Montpellier, Montpellier, France
| |
Collapse
|
46
|
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.
Collapse
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
| |
Collapse
|
47
|
Prado SA, Cabrera-Bosquet L, Grau A, Coupel-Ledru A, Millet EJ, Welcker C, Tardieu F. Phenomics allows identification of genomic regions affecting maize stomatal conductance with conditional effects of water deficit and evaporative demand. PLANT, CELL & ENVIRONMENT 2018; 41:314-326. [PMID: 29044609 DOI: 10.1111/pce.13083] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2017] [Revised: 09/20/2017] [Accepted: 09/21/2017] [Indexed: 05/21/2023]
Abstract
Stomatal conductance is central for the trades-off between hydraulics and photosynthesis. We aimed at deciphering its genetic control and that of its responses to evaporative demand and water deficit, a nearly impossible task with gas exchanges measurements. Whole-plant stomatal conductance was estimated via inversion of the Penman-Monteith equation from data of transpiration and plant architecture collected in a phenotyping platform. We have analysed jointly 4 experiments with contrasting environmental conditions imposed to a panel of 254 maize hybrids. Estimated whole-plant stomatal conductance closely correlated with gas-exchange measurements and biomass accumulation rate. Sixteen robust quantitative trait loci (QTLs) were identified by genome wide association studies and co-located with QTLs of transpiration and biomass. Light, vapour pressure deficit, or soil water potential largely accounted for the differences in allelic effects between experiments, thereby providing strong hypotheses for mechanisms of stomatal control and a way to select relevant candidate genes among the 1-19 genes harboured by QTLs. The combination of allelic effects, as affected by environmental conditions, accounted for the variability of stomatal conductance across a range of hybrids and environmental conditions. This approach may therefore contribute to genetic analysis and prediction of stomatal control in diverse environments.
Collapse
Affiliation(s)
| | | | - Antonin Grau
- LEPSE, INRA, Univ. Montpellier, 34060, Montpellier, France
| | | | | | - Claude Welcker
- LEPSE, INRA, Univ. Montpellier, 34060, Montpellier, France
| | | |
Collapse
|