1
|
Yu H, Dong M, Zhao R, Zhang L, Sui Y. Research on precise phenotype identification and growth prediction of lettuce based on deep learning. ENVIRONMENTAL RESEARCH 2024; 252:118845. [PMID: 38570128 DOI: 10.1016/j.envres.2024.118845] [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: 12/19/2023] [Revised: 03/28/2024] [Accepted: 03/30/2024] [Indexed: 04/05/2024]
Abstract
In recent years, precision agriculture, driven by scientific monitoring, precise management, and efficient use of agricultural resources, has become the direction for future agricultural development. The precise identification and assessment of phenotypes, which serve as external representations of a crop's growth, development, and genetic characteristics, are crucial for the realization of precision agriculture. Applications surrounding phenotypic indices also provide significant technical support for optimizing crop cultivation management and advancing smart agriculture, contributing to the efficient and high-quality development of precision agriculture.This paper focuses on lettuce and employs common nutritional stress conditions during growth as experimental settings. By collecting RGB images throughout the lettuce's complete growth cycle, we developed a deep learning-based computational model to tackle key issues in the lettuce's growth and precisely identify and assess phenotypic indices. We discovered that some phenotypic indices, including custom ones defined in this study, are representative of the lettuce's growth status. By dynamically monitoring the changes in phenotypic traits during growth, we quantitatively analyzed the accumulation and evolution of phenotypic indices across different growth stages. On this basis, a predictive model for lettuce growth and development was trained.The model incorporates MSE, SSIM, and perceptual loss, significantly enhancing the predictive accuracy of the lettuce growth images and phenotypic indices. The model trained with the reconstructed loss function outperforms the original model, with the SSIM and PSNR improving by 1.33% and 10.32%, respectively. The model also demonstrates high accuracy in predicting lettuce phenotypic indices, with an average error less than 0.55% for geometric indices and less than 1.7% for color and texture indices. Ultimately, it achieves intelligent monitoring and management throughout the lettuce's life cycle, providing technical support for high-quality and efficient lettuce production.
Collapse
Affiliation(s)
- Haiye Yu
- College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, Jilin, China
| | - Mo Dong
- Mudanjiang Medical University, Mudanjiang, 157000, Heilongjiang, China.
| | - Ruohan Zhao
- Mudanjiang Medical University, Mudanjiang, 157000, Heilongjiang, China
| | - Lei Zhang
- College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, Jilin, China
| | - Yuanyuan Sui
- College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, Jilin, China
| |
Collapse
|
2
|
Ginzburg DN, Cox JA, Rhee SY. Non-destructive, whole-plant phenotyping reveals dynamic changes in water use efficiency, photosynthesis, and rhizosphere acidification of sorghum accessions under osmotic stress. PLANT DIRECT 2024; 8:e571. [PMID: 38464685 PMCID: PMC10918709 DOI: 10.1002/pld3.571] [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: 12/05/2023] [Revised: 02/06/2024] [Accepted: 02/08/2024] [Indexed: 03/12/2024]
Abstract
Noninvasive phenotyping can quantify dynamic plant growth processes at higher temporal resolution than destructive phenotyping and can reveal phenomena that would be missed by end-point analysis alone. Additionally, whole-plant phenotyping can identify growth conditions that are optimal for both above- and below-ground tissues. However, noninvasive, whole-plant phenotyping approaches available today are generally expensive, complex, and non-modular. We developed a low-cost and versatile approach to noninvasively measure whole-plant physiology over time by growing plants in isolated hydroponic chambers. We demonstrate the versatility of our approach by measuring whole-plant biomass accumulation, water use, and water use efficiency every two days on unstressed and osmotically stressed sorghum accessions. We identified relationships between root zone acidification and photosynthesis on whole-plant water use efficiency over time. Our system can be implemented using cheap, basic components, requires no specific technical expertise, and should be suitable for any non-aquatic vascular plant species.
Collapse
Affiliation(s)
- Daniel N. Ginzburg
- Department of Plant BiologyCarnegie Institution for ScienceStanfordCaliforniaUSA
- Present address:
Department of Plant SciencesUniversity of CambridgeCambridgeUK
| | - Jack A. Cox
- Department of Plant BiologyCarnegie Institution for ScienceStanfordCaliforniaUSA
| | - Seung Y. Rhee
- Department of Plant BiologyCarnegie Institution for ScienceStanfordCaliforniaUSA
- Present address:
Plant Resilience Institute, Departments of Biochemistry and Molecular Biology, Plant Biology, and Plant, Soil, and Microbial SciencesMichigan State UniversityEast LansingMichiganUSA
- Present address:
Water and Life Interface InstituteEast LansingMichigan48824USA
| |
Collapse
|
3
|
Gracia-Romero A, Vatter T, Kefauver SC, Rezzouk FZ, Segarra J, Nieto-Taladriz MT, Aparicio N, Araus JL. Defining durum wheat ideotypes adapted to Mediterranean environments through remote sensing traits. FRONTIERS IN PLANT SCIENCE 2023; 14:1254301. [PMID: 37731983 PMCID: PMC10508639 DOI: 10.3389/fpls.2023.1254301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 08/03/2023] [Indexed: 09/22/2023]
Abstract
An acceleration of the genetic advances of durum wheat, as a major crop for the Mediterranean region, is required, but phenotyping still represents a bottleneck for breeding. This study aims to define durum wheat ideotypes under Mediterranean conditions by selecting the most suitable phenotypic remote sensing traits among different ones informing on characteristics related with leaf pigments/photosynthetic status, crop water status, and crop growth/green biomass. A set of 24 post-green revolution durum wheat cultivars were assessed in a wide set of 19 environments, accounted as the specific combinations of a range of latitudes in Spain, under different management conditions (water regimes and planting dates), through 3 consecutive years. Thus, red-green-blue and multispectral derived vegetation indices and canopy temperature were evaluated at anthesis and grain filling. The potential of the assessed remote sensing parameters alone and all combined as grain yield (GY) predictors was evaluated through random forest regression models performed for each environment and phenological stage. Biomass and plot greenness indicators consistently proved to be reliable GY predictors in all of the environments tested for both phenological stages. For the lowest-yielding environment, the contribution of water status measurements was higher during anthesis, whereas, for the highest-yielding environments, better predictions were reported during grain filling. Remote sensing traits measured during the grain filling and informing on pigment content and photosynthetic capacity were highlighted under the environments with warmer conditions, as the late-planting treatments. Overall, canopy greenness indicators were reported as the highest correlated traits for most of the environments and regardless of the phenological moment assessed. The addition of carbon isotope composition of mature kernels was attempted to increase the accuracies, but only a few were slightly benefited, as differences in water status among cultivars were already accounted by the measurement of canopy temperature.
Collapse
Affiliation(s)
- Adrian Gracia-Romero
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
| | - Thomas Vatter
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
| | - Shawn C. Kefauver
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
| | - Fatima Zahra Rezzouk
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
| | - Joel Segarra
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
| | | | - Nieves Aparicio
- Agro-technological Institute of Castilla y León (ITACyL), Valladolid, Spain
| | - José Luis Araus
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
| |
Collapse
|
4
|
Cooper M, Messina CD. Breeding crops for drought-affected environments and improved climate resilience. THE PLANT CELL 2023; 35:162-186. [PMID: 36370076 PMCID: PMC9806606 DOI: 10.1093/plcell/koac321] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/01/2022] [Indexed: 05/12/2023]
Abstract
Breeding climate-resilient crops with improved levels of abiotic and biotic stress resistance as a response to climate change presents both opportunities and challenges. Applying the framework of the "breeder's equation," which is used to predict the response to selection for a breeding program cycle, we review methodologies and strategies that have been used to successfully breed crops with improved levels of drought resistance, where the target population of environments (TPEs) is a spatially and temporally heterogeneous mixture of drought-affected and favorable (water-sufficient) environments. Long-term improvement of temperate maize for the US corn belt is used as a case study and compared with progress for other crops and geographies. Integration of trait information across scales, from genomes to ecosystems, is needed to accurately predict yield outcomes for genotypes within the current and future TPEs. This will require transdisciplinary teams to explore, identify, and exploit novel opportunities to accelerate breeding program outcomes; both improved germplasm resources and improved products (cultivars, hybrids, clones, and populations) that outperform and replace the products in use by farmers, in combination with modified agronomic management strategies suited to their local environments.
Collapse
Affiliation(s)
| | - Carlos D Messina
- Horticultural Sciences Department, University of Florida, Gainesville, Florida 32611, USA
| |
Collapse
|
5
|
Li X, Xu X, Chen M, Xu M, Wang W, Liu C, Yu L, Liu W, Yang W. The field phenotyping platform's next darling: Dicotyledons. FRONTIERS IN PLANT SCIENCE 2022; 13:935748. [PMID: 36092402 PMCID: PMC9449727 DOI: 10.3389/fpls.2022.935748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 07/21/2022] [Indexed: 06/15/2023]
Abstract
The genetic information and functional properties of plants have been further identified with the completion of the whole-genome sequencing of numerous crop species and the rapid development of high-throughput phenotyping technologies, laying a suitable foundation for advanced precision agriculture and enhanced genetic gains. Collecting phenotypic data from dicotyledonous crops in the field has been identified as a key factor in the collection of large-scale phenotypic data of crops. On the one hand, dicotyledonous plants account for 4/5 of all angiosperm species and play a critical role in agriculture. However, their morphology is complex, and an abundance of dicot phenotypic information is available, which is critical for the analysis of high-throughput phenotypic data in the field. As a result, the focus of this paper is on the major advancements in ground-based, air-based, and space-based field phenotyping platforms over the last few decades and the research progress in the high-throughput phenotyping of dicotyledonous field crop plants in terms of morphological indicators, physiological and biochemical indicators, biotic/abiotic stress indicators, and yield indicators. Finally, the future development of dicots in the field is explored from the perspectives of identifying new unified phenotypic criteria, developing a high-performance infrastructure platform, creating a phenotypic big data knowledge map, and merging the data with those of multiomic techniques.
Collapse
Affiliation(s)
- Xiuni Li
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Xiangyao Xu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Menggen Chen
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Mei Xu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Wenyan Wang
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Chunyan Liu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Liang Yu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Weiguo Liu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Wenyu Yang
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| |
Collapse
|
6
|
Flutre T, Le Cunff L, Fodor A, Launay A, Romieu C, Berger G, Bertrand Y, Terrier N, Beccavin I, Bouckenooghe V, Roques M, Pinasseau L, Verbaere A, Sommerer N, Cheynier V, Bacilieri R, Boursiquot JM, Lacombe T, Laucou V, This P, Péros JP, Doligez A. A genome-wide association and prediction study in grapevine deciphers the genetic architecture of multiple traits and identifies genes under many new QTLs. G3 (BETHESDA, MD.) 2022; 12:6575896. [PMID: 35485948 PMCID: PMC9258538 DOI: 10.1093/g3journal/jkac103] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 04/21/2022] [Indexed: 12/11/2022]
Abstract
To cope with the challenges facing agriculture, speeding-up breeding programs is a worthy endeavor, especially for perennial species such as grapevine, but requires understanding the genetic architecture of target traits. To go beyond the mapping of quantitative trait loci in bi-parental crosses, we exploited a diversity panel of 279 Vitis vinifera L. cultivars planted in 5 blocks in the vineyard. This panel was phenotyped over several years for 127 traits including yield components, organic acids, aroma precursors, polyphenols, and a water stress indicator. The panel was genotyped for 63k single nucleotide polymorphisms by combining an 18K microarray and genotyping-by-sequencing. The experimental design allowed to reliably assess the genotypic values for most traits. Marker densification via genotyping-by-sequencing markedly increased the proportion of genetic variance explained by single nucleotide polymorphisms, and 2 multi-single nucleotide polymorphism models identified quantitative trait loci not found by a single nucleotide polymorphism-by-single nucleotide polymorphism model. Overall, 489 reliable quantitative trait loci were detected for 41% more response variables than by a single nucleotide polymorphism-by-single nucleotide polymorphism model with microarray-only single nucleotide polymorphisms, many new ones compared with the results from bi-parental crosses. A prediction accuracy higher than 0.42 was obtained for 50% of the response variables. Our overall approach as well as quantitative trait locus and prediction results provide insights into the genetic architecture of target traits. New candidate genes and the application into breeding are discussed.
Collapse
Affiliation(s)
- Timothée Flutre
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398 Montpellier, France.,UMT Géno-Vigne, 34398 Montpellier, France.,Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Loïc Le Cunff
- UMT Géno-Vigne, 34398 Montpellier, France.,IFV, 30240 Le Grau-du-Roi, France
| | - Agota Fodor
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398 Montpellier, France.,UMT Géno-Vigne, 34398 Montpellier, France
| | - Amandine Launay
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398 Montpellier, France.,UMT Géno-Vigne, 34398 Montpellier, France
| | - Charles Romieu
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398 Montpellier, France.,UMT Géno-Vigne, 34398 Montpellier, France
| | - Gilles Berger
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398 Montpellier, France.,UMT Géno-Vigne, 34398 Montpellier, France
| | - Yves Bertrand
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398 Montpellier, France.,UMT Géno-Vigne, 34398 Montpellier, France
| | - Nancy Terrier
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398 Montpellier, France
| | | | | | - Maryline Roques
- UMT Géno-Vigne, 34398 Montpellier, France.,IFV, 30240 Le Grau-du-Roi, France
| | - Lucie Pinasseau
- SPO, Univ Montpellier, INRAE, Institut Agro, 34060 Montpellier, France
| | - Arnaud Verbaere
- SPO, Univ Montpellier, INRAE, Institut Agro, 34060 Montpellier, France
| | - Nicolas Sommerer
- SPO, Univ Montpellier, INRAE, Institut Agro, 34060 Montpellier, France
| | | | - Roberto Bacilieri
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398 Montpellier, France.,UMT Géno-Vigne, 34398 Montpellier, France
| | - Jean-Michel Boursiquot
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398 Montpellier, France.,UMT Géno-Vigne, 34398 Montpellier, France
| | - Thierry Lacombe
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398 Montpellier, France.,UMT Géno-Vigne, 34398 Montpellier, France
| | - Valérie Laucou
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398 Montpellier, France.,UMT Géno-Vigne, 34398 Montpellier, France
| | - Patrice This
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398 Montpellier, France.,UMT Géno-Vigne, 34398 Montpellier, France
| | - Jean-Pierre Péros
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398 Montpellier, France.,UMT Géno-Vigne, 34398 Montpellier, France
| | - Agnès Doligez
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398 Montpellier, France.,UMT Géno-Vigne, 34398 Montpellier, France
| |
Collapse
|
7
|
Zhang Y, Henke M, Li Y, Xu D, Liu A, Liu X, Li T. Analyzing the Impact of Greenhouse Planting Strategy and Plant Architecture on Tomato Plant Physiology and Estimated Dry Matter. FRONTIERS IN PLANT SCIENCE 2022; 13:828252. [PMID: 35242156 PMCID: PMC8885523 DOI: 10.3389/fpls.2022.828252] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 01/13/2022] [Indexed: 05/17/2023]
Abstract
Determine the level of significance of planting strategy and plant architecture and how they affect plant physiology and dry matter accumulation within greenhouses is essential to actual greenhouse plant management and breeding. We thus analyzed four planting strategies (plant spacing, furrow distance, row orientation, planting pattern) and eight different plant architectural traits (internode length, leaf azimuth angle, leaf elevation angle, leaf length, leaflet curve, leaflet elevation, leaflet number/area ratio, leaflet length/width ratio) with the same plant leaf area using a formerly developed functional-structural model for a Chinese Liaoshen-solar greenhouse and tomato plant, which used to simulate the plant physiology of light interception, temperature, stomatal conductance, photosynthesis, and dry matter. Our study led to the conclusion that the planting strategies have a more significant impact overall on plant radiation, temperature, photosynthesis, and dry matter compared to plant architecture changes. According to our findings, increasing the plant spacing will have the most significant impact to increase light interception. E-W orientation has better total light interception but yet weaker light uniformity. Changes in planting patterns have limited influence on the overall canopy physiology. Increasing the plant leaflet area by leaflet N/A ratio from what we could observe for a rose the total dry matter by 6.6%, which is significantly better than all the other plant architecture traits. An ideal tomato plant architecture which combined all the above optimal architectural traits was also designed to provide guidance on phenotypic traits selection of breeding process. The combined analysis approach described herein established the causal relationship between investigated traits, which could directly apply to provide management and breeding insights on other plant species with different solar greenhouse structures.
Collapse
Affiliation(s)
- Yue Zhang
- College of Horticulture, Shenyang Agricultural University, Shenyang, China
- Key Laboratory of Protected Horticulture, Ministry of Education, Shenyang, China
- National & Local Joint Engineering Research Center of Northern Horticultural Facilities Design & Application Technology (Liaoning), Shenyang, China
| | - Michael Henke
- Plant Sciences Core Facility, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czechia
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Stadt Seeland, Germany
| | - Yiming Li
- Key Laboratory of Protected Horticulture, Ministry of Education, Shenyang, China
- National & Local Joint Engineering Research Center of Northern Horticultural Facilities Design & Application Technology (Liaoning), Shenyang, China
- College of Engineering, Shenyang Agricultural University, Shenyang, China
| | - Demin Xu
- College of Horticulture, Shenyang Agricultural University, Shenyang, China
- Key Laboratory of Protected Horticulture, Ministry of Education, Shenyang, China
- National & Local Joint Engineering Research Center of Northern Horticultural Facilities Design & Application Technology (Liaoning), Shenyang, China
| | - Anhua Liu
- College of Horticulture, Shenyang Agricultural University, Shenyang, China
- Key Laboratory of Protected Horticulture, Ministry of Education, Shenyang, China
- National & Local Joint Engineering Research Center of Northern Horticultural Facilities Design & Application Technology (Liaoning), Shenyang, China
| | - Xingan Liu
- College of Horticulture, Shenyang Agricultural University, Shenyang, China
- Key Laboratory of Protected Horticulture, Ministry of Education, Shenyang, China
- National & Local Joint Engineering Research Center of Northern Horticultural Facilities Design & Application Technology (Liaoning), Shenyang, China
| | - Tianlai Li
- College of Horticulture, Shenyang Agricultural University, Shenyang, China
- Key Laboratory of Protected Horticulture, Ministry of Education, Shenyang, China
- National & Local Joint Engineering Research Center of Northern Horticultural Facilities Design & Application Technology (Liaoning), Shenyang, China
| |
Collapse
|
8
|
PlantNet: transfer learning-based fine-grained network for high-throughput plants recognition. Soft comput 2022. [DOI: 10.1007/s00500-021-06689-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
9
|
Soualiou S, Wang Z, Sun W, de Reffye P, Collins B, Louarn G, Song Y. Functional-Structural Plant Models Mission in Advancing Crop Science: Opportunities and Prospects. FRONTIERS IN PLANT SCIENCE 2021; 12:747142. [PMID: 35003151 PMCID: PMC8733959 DOI: 10.3389/fpls.2021.747142] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 11/22/2021] [Indexed: 06/02/2023]
Abstract
Functional-structural plant models (FSPMs) have been evolving for over 2 decades and their future development, to some extent, depends on the value of potential applications in crop science. To date, stabilizing crop production by identifying valuable traits for novel cultivars adapted to adverse environments is topical in crop science. Thus, this study will examine how FSPMs are able to address new challenges in crop science for sustainable crop production. FSPMs developed to simulate organogenesis, morphogenesis, and physiological activities under various environments and are amenable to downscale to the tissue, cellular, and molecular level or upscale to the whole plant and ecological level. In a modeling framework with independent and interactive modules, advanced algorithms provide morphophysiological details at various scales. FSPMs are shown to be able to: (i) provide crop ideotypes efficiently for optimizing the resource distribution and use for greater productivity and less disease risk, (ii) guide molecular design breeding via linking molecular basis to plant phenotypes as well as enrich crop models with an additional architectural dimension to assist breeding, and (iii) interact with plant phenotyping for molecular breeding in embracing three-dimensional (3D) architectural traits. This study illustrates that FSPMs have great prospects in speeding up precision breeding for specific environments due to the capacity for guiding and integrating ideotypes, phenotyping, molecular design, and linking molecular basis to target phenotypes. Consequently, the promising great applications of FSPMs in crop science will, in turn, accelerate their evolution and vice versa.
Collapse
Affiliation(s)
| | - Zhiwei Wang
- School of Agronomy, Anhui Agricultural University, Hefei, China
| | - Weiwei Sun
- School of Agronomy, Anhui Agricultural University, Hefei, China
| | - Philippe de Reffye
- The French Agricultural Research and International Cooperation Organization, Montpellier, France
| | - Brian Collins
- College of Science and Engineering, James Cook University, Townsville, QLD, Australia
| | | | - Youhong Song
- School of Agronomy, Anhui Agricultural University, Hefei, China
- Centre for Crop Science, The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Brisbane, QLD, Australia
| |
Collapse
|
10
|
Chang S, Lee U, Hong MJ, Jo YD, Kim JB. Time-Series Growth Prediction Model Based on U-Net and Machine Learning in Arabidopsis. FRONTIERS IN PLANT SCIENCE 2021; 12:721512. [PMID: 34858446 PMCID: PMC8631871 DOI: 10.3389/fpls.2021.721512] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 10/08/2021] [Indexed: 06/13/2023]
Abstract
Yield prediction for crops is essential information for food security. A high-throughput phenotyping platform (HTPP) generates the data of the complete life cycle of a plant. However, the data are rarely used for yield prediction because of the lack of quality image analysis methods, yield data associated with HTPP, and the time-series analysis method for yield prediction. To overcome limitations, this study employed multiple deep learning (DL) networks to extract high-quality HTTP data, establish an association between HTTP data and the yield performance of crops, and select essential time intervals using machine learning (ML). The images of Arabidopsis were taken 12 times under environmentally controlled HTPP over 23 days after sowing (DAS). First, the features from images were extracted using DL network U-Net with SE-ResXt101 encoder and divided into early (15-21 DAS) and late (∼21-23 DAS) pre-flowering developmental stages using the physiological characteristics of the Arabidopsis plant. Second, the late pre-flowering stage at 23 DAS can be predicted using the ML algorithm XGBoost, based only on a portion of the early pre-flowering stage (17-21 DAS). This was confirmed using an additional biological experiment (P < 0.01). Finally, the projected area (PA) was estimated into fresh weight (FW), and the correlation coefficient between FW and predicted FW was calculated as 0.85. This was the first study that analyzed time-series data to predict the FW of related but different developmental stages and predict the PA. The results of this study were informative and enabled the understanding of the FW of Arabidopsis or yield of leafy plants and total biomass consumed in vertical farming. Moreover, this study highlighted the reduction of time-series data for examining interesting traits and future application of time-series analysis in various HTPPs.
Collapse
Affiliation(s)
- Sungyul Chang
- Radiation Breeding Research Team, Advanced Radiation Technology Institute (ARTI), Korea Atomic Energy Research Institute (KAERI), Jeongeup-si, South Korea
| | - Unseok Lee
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung-si, South Korea
| | - Min Jeong Hong
- Radiation Breeding Research Team, Advanced Radiation Technology Institute (ARTI), Korea Atomic Energy Research Institute (KAERI), Jeongeup-si, South Korea
| | - Yeong Deuk Jo
- Radiation Breeding Research Team, Advanced Radiation Technology Institute (ARTI), Korea Atomic Energy Research Institute (KAERI), Jeongeup-si, South Korea
| | - Jin-Baek Kim
- Radiation Breeding Research Team, Advanced Radiation Technology Institute (ARTI), Korea Atomic Energy Research Institute (KAERI), Jeongeup-si, South Korea
| |
Collapse
|
11
|
Kienbaum L, Correa Abondano M, Blas R, Schmid K. DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics. PLANT METHODS 2021; 17:91. [PMID: 34419093 PMCID: PMC8379755 DOI: 10.1186/s13007-021-00787-6] [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: 03/17/2021] [Accepted: 07/30/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Maize cobs are an important component of crop yield that exhibit a high diversity in size, shape and color in native landraces and modern varieties. Various phenotyping approaches were developed to measure maize cob parameters in a high throughput fashion. More recently, deep learning methods like convolutional neural networks (CNNs) became available and were shown to be highly useful for high-throughput plant phenotyping. We aimed at comparing classical image segmentation with deep learning methods for maize cob image segmentation and phenotyping using a large image dataset of native maize landrace diversity from Peru. RESULTS Comparison of three image analysis methods showed that a Mask R-CNN trained on a diverse set of maize cob images was highly superior to classical image analysis using the Felzenszwalb-Huttenlocher algorithm and a Window-based CNN due to its robustness to image quality and object segmentation accuracy ([Formula: see text]). We integrated Mask R-CNN into a high-throughput pipeline to segment both maize cobs and rulers in images and perform an automated quantitative analysis of eight phenotypic traits, including diameter, length, ellipticity, asymmetry, aspect ratio and average values of red, green and blue color channels for cob color. Statistical analysis identified key training parameters for efficient iterative model updating. We also show that a small number of 10-20 images is sufficient to update the initial Mask R-CNN model to process new types of cob images. To demonstrate an application of the pipeline we analyzed phenotypic variation in 19,867 maize cobs extracted from 3449 images of 2484 accessions from the maize genebank of Peru to identify phenotypically homogeneous and heterogeneous genebank accessions using multivariate clustering. CONCLUSIONS Single Mask R-CNN model and associated analysis pipeline are widely applicable tools for maize cob phenotyping in contexts like genebank phenomics or plant breeding.
Collapse
Affiliation(s)
- Lydia Kienbaum
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, Germany
| | - Miguel Correa Abondano
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, Germany
| | - Raul Blas
- Universidad National Agraria La Molina (UNALM), Lima, Peru
| | - Karl Schmid
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, Germany.
- Computational Science Lab, University of Hohenheim, Stuttgart, Germany.
| |
Collapse
|
12
|
Folk RA, Siniscalchi CM. Biodiversity at the global scale: the synthesis continues. AMERICAN JOURNAL OF BOTANY 2021; 108:912-924. [PMID: 34181762 DOI: 10.1002/ajb2.1694] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 04/14/2021] [Indexed: 06/13/2023]
Abstract
Traditionally, the generation and use of biodiversity data and their associated specimen objects have been primarily the purview of individuals and small research groups. While deposition of data and specimens in herbaria and other repositories has long been the norm, throughout most of their history, these resources have been accessible only to a small community of specialists. Through recent concerted efforts, primarily at the level of national and international governmental agencies over the last two decades, the pace of biodiversity data accumulation has accelerated, and a wider array of biodiversity scientists has gained access to this massive accumulation of resources, applying them to an ever-widening compass of research pursuits. We review how these new resources and increasing access to them are affecting the landscape of biodiversity research in plants today, focusing on new applications across evolution, ecology, and other fields that have been enabled specifically by the availability of these data and the global scope that was previously beyond the reach of individual investigators. We give an overview of recent advances organized along three lines: broad-scale analyses of distributional data and spatial information, phylogenetic research circumscribing large clades with comprehensive taxon sampling, and data sets derived from improved accessibility of biodiversity literature. We also review synergies between large data resources and more traditional data collection paradigms, describe shortfalls and how to overcome them, and reflect on the future of plant biodiversity analyses in light of increasing linkages between data types and scientists in our field.
Collapse
Affiliation(s)
- Ryan A Folk
- Department of Biological Sciences, Mississippi State University, Mississippi State, Mississippi, USA
| | - Carolina M Siniscalchi
- Department of Biological Sciences, Mississippi State University, Mississippi State, Mississippi, USA
| |
Collapse
|
13
|
Plant Transcription Factors Involved in Drought and Associated Stresses. Int J Mol Sci 2021; 22:ijms22115662. [PMID: 34073446 PMCID: PMC8199153 DOI: 10.3390/ijms22115662] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 05/14/2021] [Accepted: 05/19/2021] [Indexed: 11/16/2022] Open
Abstract
Transcription factors (TFs) play a significant role in signal transduction networks spanning the perception of a stress signal and the expression of corresponding stress-responsive genes. TFs are multi-functional proteins that may simultaneously control numerous pathways during stresses in plants-this makes them powerful tools for the manipulation of regulatory and stress-responsive pathways. In recent years, the structure-function relationships of numerous plant TFs involved in drought and associated stresses have been defined, which prompted devising practical strategies for engineering plants with enhanced stress tolerance. Vast data have emerged on purposely basic leucine zipper (bZIP), WRKY, homeodomain-leucine zipper (HD-Zip), myeloblastoma (MYB), drought-response elements binding proteins/C-repeat binding factor (DREB/CBF), shine (SHN), and wax production-like (WXPL) TFs that reflect the understanding of their 3D structure and how the structure relates to function. Consequently, this information is useful in the tailored design of variant TFs that enhances our understanding of their functional states, such as oligomerization, post-translational modification patterns, protein-protein interactions, and their abilities to recognize downstream target DNA sequences. Here, we report on the progress of TFs based on their interaction pathway participation in stress-responsive networks, and pinpoint strategies and applications for crops and the impact of these strategies for improving plant stress tolerance.
Collapse
|
14
|
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
|
15
|
PhenoMIP: High-Throughput Phenotyping of Diverse Caenorhabditis elegans Populations via Molecular Inversion Probes. G3-GENES GENOMES GENETICS 2020; 10:3977-3990. [PMID: 32868407 PMCID: PMC7642933 DOI: 10.1534/g3.120.401656] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Whether generated within a lab setting or isolated from the wild, variant alleles continue to be an important resource for decoding gene function in model organisms such as Caenorhabditis elegans. With advances in massively parallel sequencing, multiple whole-genome sequenced (WGS) strain collections are now available to the research community. The Million Mutation Project (MMP) for instance, analyzed 2007 N2-derived, mutagenized strains. Individually, each strain averages ∼400 single nucleotide variants amounting to ∼80 protein-coding variants. The effects of these variants, however, remain largely uncharacterized and querying the breadth of these strains for phenotypic changes requires a method amenable to rapid and sensitive high-throughput analysis. Here we present a pooled competitive fitness approach to quantitatively phenotype subpopulations of sequenced collections via molecular inversion probes (PhenoMIP). We phenotyped the relative fitness of 217 mutant strains on multiple food sources and classified these into five categories. We also demonstrate on a subset of these strains, that their fitness defects can be genetically mapped. Overall, our results suggest that approximately 80% of MMP mutant strains may have a decreased fitness relative to the lab reference, N2. The costs of generating this form of analysis through WGS methods would be prohibitive while PhenoMIP analysis in this manner is accomplished at less than one-tenth of projected WGS costs. We propose methods for applying PhenoMIP to a broad range of population selection experiments in a cost-efficient manner that would be useful to the community at large.
Collapse
|
16
|
Moreira FF, Oliveira HR, Volenec JJ, Rainey KM, Brito LF. Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops. FRONTIERS IN PLANT SCIENCE 2020; 11:681. [PMID: 32528513 PMCID: PMC7264266 DOI: 10.3389/fpls.2020.00681] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 04/30/2020] [Indexed: 05/28/2023]
Abstract
The rapid development of remote sensing in agronomic research allows the dynamic nature of longitudinal traits to be adequately described, which may enhance the genetic improvement of crop efficiency. For traits such as light interception, biomass accumulation, and responses to stressors, the data generated by the various high-throughput phenotyping (HTP) methods requires adequate statistical techniques to evaluate phenotypic records throughout time. As a consequence, information about plant functioning and activation of genes, as well as the interaction of gene networks at different stages of plant development and in response to environmental stimulus can be exploited. In this review, we outline the current analytical approaches in quantitative genetics that are applied to longitudinal traits in crops throughout development, describe the advantages and pitfalls of each approach, and indicate future research directions and opportunities.
Collapse
Affiliation(s)
- Fabiana F. Moreira
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Hinayah R. Oliveira
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| | - Jeffrey J. Volenec
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Katy M. Rainey
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Luiz F. Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| |
Collapse
|
17
|
Feldmann MJ, Hardigan MA, Famula RA, López CM, Tabb A, Cole GS, Knapp SJ. Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry. Gigascience 2020; 9:giaa030. [PMID: 32352533 PMCID: PMC7191992 DOI: 10.1093/gigascience/giaa030] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 02/06/2020] [Accepted: 03/10/2020] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND Shape is a critical element of the visual appeal of strawberry fruit and is influenced by both genetic and non-genetic determinants. Current fruit phenotyping approaches for external characteristics in strawberry often rely on the human eye to make categorical assessments. However, fruit shape is an inherently multi-dimensional, continuously variable trait and not adequately described by a single categorical or quantitative feature. Morphometric approaches enable the study of complex, multi-dimensional forms but are often abstract and difficult to interpret. In this study, we developed a mathematical approach for transforming fruit shape classifications from digital images onto an ordinal scale called the Principal Progression of k Clusters (PPKC). We use these human-recognizable shape categories to select quantitative features extracted from multiple morphometric analyses that are best fit for genetic dissection and analysis. RESULTS We transformed images of strawberry fruit into human-recognizable categories using unsupervised machine learning, discovered 4 principal shape categories, and inferred progression using PPKC. We extracted 68 quantitative features from digital images of strawberries using a suite of morphometric analyses and multivariate statistical approaches. These analyses defined informative feature sets that effectively captured quantitative differences between shape classes. Classification accuracy ranged from 68% to 99% for the newly created phenotypic variables for describing a shape. CONCLUSIONS Our results demonstrated that strawberry fruit shapes could be robustly quantified, accurately classified, and empirically ordered using image analyses, machine learning, and PPKC. We generated a dictionary of quantitative traits for studying and predicting shape classes and identifying genetic factors underlying phenotypic variability for fruit shape in strawberry. The methods and approaches that we applied in strawberry should apply to other fruits, vegetables, and specialty crops.
Collapse
Affiliation(s)
- Mitchell J Feldmann
- Department of Plant Sciences, University of California, Davis, 1 Shields Ave, Davis, CA 95616, USA
| | - Michael A Hardigan
- Department of Plant Sciences, University of California, Davis, 1 Shields Ave, Davis, CA 95616, USA
| | - Randi A Famula
- Department of Plant Sciences, University of California, Davis, 1 Shields Ave, Davis, CA 95616, USA
| | - Cindy M López
- Department of Plant Sciences, University of California, Davis, 1 Shields Ave, Davis, CA 95616, USA
| | - Amy Tabb
- USDA-ARS-AFRS, 2217 Wiltshire Rd, Kearneysville, WV 25430, USA
| | - Glenn S Cole
- Department of Plant Sciences, University of California, Davis, 1 Shields Ave, Davis, CA 95616, USA
| | - Steven J Knapp
- Department of Plant Sciences, University of California, Davis, 1 Shields Ave, Davis, CA 95616, USA
| |
Collapse
|
18
|
Rowland SD, Zumstein K, Nakayama H, Cheng Z, Flores AM, Chitwood DH, Maloof JN, Sinha NR. Leaf shape is a predictor of fruit quality and cultivar performance in tomato. THE NEW PHYTOLOGIST 2020; 226:851-865. [PMID: 31880321 PMCID: PMC7187315 DOI: 10.1111/nph.16403] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 12/14/2019] [Indexed: 05/04/2023]
Abstract
Commercial tomato (Solanum lycopersicum) is one of the most widely grown vegetable crops worldwide. Heirloom tomatoes retain extensive genetic diversity and a considerable range of fruit quality and leaf morphological traits. Here the role of leaf morphology was investigated for its impact on fruit quality. Heirloom cultivars were grown in field conditions, and BRIX by yield (BY) and other traits were measured over a 14-wk period. The complex relationships among these morphological and physiological traits were evaluated using partial least-squares path modeling, and a consensus model was developed. Photosynthesis contributed strongly to vegetative biomass and sugar content of fruits but had a negative impact on yield. Conversely leaf shape, specifically rounder leaves, had a strong positive impact on both fruit sugar content and yield. Cultivars such as Stupice and Glacier, with very round leaves, had the highest performance in both fruit sugar and yield. Our model accurately predicted BY for two commercial cultivars using leaf shape data as input. This study revealed the importance of leaf shape to fruit quality in tomato, with rounder leaves having significantly improved fruit quality. This correlation was maintained across a range of diverse genetic backgrounds and shows the importance of leaf morphology in tomato crop improvement.
Collapse
Affiliation(s)
| | | | - Hokuto Nakayama
- Department of Plant BiologyUniversity of CaliforniaDavisCA95616USA
- Gradute School of ScienceUniversity of TokyoHongo Bunkyo‐kuTokyo113‐0033Japan
| | - Zizhang Cheng
- College of ScienceSichuan Agriculture UniversityYaanSichuan Province625014China
| | - Amber M. Flores
- Department of Plant BiologyUniversity of CaliforniaDavisCA95616USA
| | - Daniel H. Chitwood
- Department of Plant BiologyUniversity of CaliforniaDavisCA95616USA
- Department of HorticultureMichigan State UniversityEast LansingMI48824USA
| | - Julin N. Maloof
- Department of Plant BiologyUniversity of CaliforniaDavisCA95616USA
| | - Neelima R. Sinha
- Department of Plant BiologyUniversity of CaliforniaDavisCA95616USA
| |
Collapse
|
19
|
Santini F, Climent JM, Voltas J. Phenotypic integration and life history strategies among populations of Pinus halepensis: an insight through structural equation modelling. ANNALS OF BOTANY 2020; 124:1161-1172. [PMID: 31115443 PMCID: PMC6943711 DOI: 10.1093/aob/mcz088] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 05/20/2019] [Indexed: 05/26/2023]
Abstract
BACKGROUND AND AIMS Understanding inter-population variation in the allocation of resources to specific anatomical compartments and physiological processes is crucial to disentangle adaptive patterns in forest species. This work aims to evaluate phenotypic integration and trade-offs among functional traits as determinants of life history strategies in populations of a circum-Mediterranean pine that dwells in environments where water and other resources are in limited supply. METHODS Adult individuals of 51 populations of Pinus halepensis grown in a common garden were characterized for 11 phenotypic traits, including direct and indirect measures of water uptake at different depths, leaf area, stomatal conductance, chlorophyll content, non-structural carbohydrates, stem diameter and tree height, age at first reproduction and cone production. The population differentiation in these traits was tested through analysis of variance (ANOVA). The resulting populations' means were carried forward to a structural equation model evaluating phenotypic integration between six latent variables (summer water uptake depth, summer transpiration, spring photosynthetic capacity, growth, reserve accumulation and reproduction). KEY RESULTS Water uptake depth and transpiration covaried negatively among populations, as the likely result of a common selective pressure for drought resistance, while spring photosynthetic capacity was lower in populations originating from dry areas. Transpiration positively influenced growth, while growth was negatively related to reproduction and reserves among populations. Water uptake depth negatively influenced reproduction. CONCLUSIONS The observed patterns indicate a differentiation in life cycle features between fast-growing and slow-growing populations, with the latter investing significantly more in reproduction and reserves. We speculate that such contrasting strategies result from different arrays of life history traits underlying the very different ecological conditions that the Aleppo pine must face across its distribution range. These comprise, principally, drought as the main stressor and fire as the main ecological disturbance of the Mediterranean basin.
Collapse
Affiliation(s)
- Filippo Santini
- Joint Research Unit CTFC – AGROTECNIO, Lleida, Spain
- Department of Crop and Forest Sciences, University of Lleida, Lleida, Spain
| | - José M Climent
- INIA-CIFOR, Department of Ecology and Forest Genetics, Madrid, Spain
| | - Jordi Voltas
- Joint Research Unit CTFC – AGROTECNIO, Lleida, Spain
- Department of Crop and Forest Sciences, University of Lleida, Lleida, Spain
| |
Collapse
|
20
|
Rahaman MM, Ahsan MA, Chen M. Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification. Sci Rep 2019; 9:19526. [PMID: 31862925 PMCID: PMC6925301 DOI: 10.1038/s41598-019-55609-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 11/21/2019] [Indexed: 11/09/2022] Open
Abstract
Statistical data-mining (DM) and machine learning (ML) are promising tools to assist in the analysis of complex dataset. In recent decades, in the precision of agricultural development, plant phenomics study is crucial for high-throughput phenotyping of local crop cultivars. Therefore, integrated or a new analytical approach is needed to deal with these phenomics data. We proposed a statistical framework for the analysis of phenomics data by integrating DM and ML methods. The most popular supervised ML methods; Linear Discriminant Analysis (LDA), Random Forest (RF), Support Vector Machine with linear (SVM-l) and radial basis (SVM-r) kernel are used for classification/prediction plant status (stress/non-stress) to validate our proposed approach. Several simulated and real plant phenotype datasets were analyzed. The results described the significant contribution of the features (selected by our proposed approach) throughout the analysis. In this study, we showed that the proposed approach removed phenotype data analysis complexity, reduced computational time of ML algorithms, and increased prediction accuracy.
Collapse
Affiliation(s)
- Md Matiur Rahaman
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China.,Department of Statistics, Faculty of Science, Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Gopalganj, 8100, Bangladesh
| | - Md Asif Ahsan
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China.
| |
Collapse
|
21
|
3D Morphological Processing for Wheat Spike Phenotypes Using Computed Tomography Images. REMOTE SENSING 2019. [DOI: 10.3390/rs11091110] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Wheat is the main food crop today world-wide. In order to improve its yields, researchers are committed to understand the relationships between wheat genotypes and phenotypes. Compared to progressive technology of wheat gene section identification, wheat trait measurement is mostly done manually in a destructive, labor-intensive and time-consuming way. Therefore, this study will be greatly accelerated and promoted if we can automatically discover wheat phenotype in a nondestructive and fast manner. In this paper, we propose a novel pipeline based on 3D morphological processing to detect wheat spike grains and stem nodes from 3D X-ray micro computed tomography (CT) images. We also introduce a set of newly defined 3D phenotypes, including grain aspect ratio, porosity, Grain-to-Grain distance, and grain angle, which are very difficult to be manually measured. The analysis of the associations among these traits would be very helpful for wheat breeding. Experimental results show that our method is able to count grains more accurately than normal human performance. By analyzing the relationships between traits and environment conditions, we find that the Grain-to-Grain distance, aspect ratio and porosity are more likely affected by the genome than environment (only tested temperature and water conditions). We also find that close grains will inhibit grain volume growth and that the aspect ratio 3.5 may be the best for higher yield in wheat breeding.
Collapse
|
22
|
Mir RR, Reynolds M, Pinto F, Khan MA, Bhat MA. High-throughput phenotyping for crop improvement in the genomics era. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2019; 282:60-72. [PMID: 31003612 DOI: 10.1016/j.plantsci.2019.01.007] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Revised: 12/10/2018] [Accepted: 01/09/2019] [Indexed: 05/24/2023]
Abstract
Tremendous progress has been made with continually expanding genomics technologies to unravel and understand crop genomes. However, the impact of genomics data on crop improvement is still far from satisfactory, in large part due to a lack of effective phenotypic data; our capacity to collect useful high quality phenotypic data lags behind the current capacity to generate high-throughput genomics data. Thus, the research bottleneck in plant sciences is shifting from genotyping to phenotyping. This article review the current status of efforts made in the last decade to systematically collect phenotypic data to alleviate this 'phenomics bottlenecks' by recording trait data through sophisticated non-invasive imaging, spectroscopy, image analysis, robotics, high-performance computing facilities and phenomics databases. These modern phenomics platforms and tools aim to record data on traits like plant development, architecture, plant photosynthesis, growth or biomass productivity, on hundreds to thousands of plants in a single day, as a phenomics revolution. It is believed that this revolution will provide plant scientists with the knowledge and tools necessary for unlocking information coded in plant genomes. Efforts have been also made to present the advances made in the last 10 years in phenomics platforms and their use in generating phenotypic data on different traits in several major crops including rice, wheat, barley, and maize. The article also highlights the need for phenomics databases and phenotypic data sharing for crop improvement. The phenomics data generated has been used to identify genes/QTL through QTL mapping, association mapping and genome-wide association studies (GWAS) for genomics-assisted breeding (GAB) for crop improvement.
Collapse
Affiliation(s)
- Reyazul Rouf Mir
- Division of Genetics & Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Wadura Campus, Sopore-193201, Kashmir, India.
| | - Mathew Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT), Mexico, D.F., Mexico
| | - Francisco Pinto
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT), Mexico, D.F., Mexico
| | - Mohd Anwar Khan
- Division of Genetics & Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Wadura Campus, Sopore-193201, Kashmir, India
| | - Mohd Ashraf Bhat
- Division of Genetics & Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Wadura Campus, Sopore-193201, Kashmir, India
| |
Collapse
|
23
|
Gosa SC, Lupo Y, Moshelion M. Quantitative and comparative analysis of whole-plant performance for functional physiological traits phenotyping: New tools to support pre-breeding and plant stress physiology studies. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2019; 282:49-59. [PMID: 31003611 DOI: 10.1016/j.plantsci.2018.05.008] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Revised: 04/14/2018] [Accepted: 05/14/2018] [Indexed: 05/18/2023]
Abstract
Plants are autotrophic organisms in which there are linear relationships between the rate at which organic biomass is accumulated and many ambient parameters such as water, nutrients, CO2 and solar radiation. These linear relationships are the result of good feedback regulation between a plants sensing of the environment and the optimization of its performance response. In this review, we suggest that continuous monitoring of the plant physiological profile in response to changing ambient conditions could be a useful new phenotyping tool, allowing the characterization and comparison of different levels of functional phenotypes and productivity. This functional physiological phenotyping (FPP) approach can be integrated into breeding programs, which are facing difficulties in selecting plants that perform well under abiotic stress. Moreover, high-throughput FPP will increase the efficiency of the selection of traits that are closely related to environmental interactions (such as plant water status, water-use efficiency, stomatal conductance, etc.) thanks to its high resolution and dynamic measurements. One of the important advantages of FPP is, its simplicity and effectiveness and compatibility with experimental methods that use load-cell lysimeters and ambient sensors. In the future, this platform could help with phenotyping of complex physiological traits, beneficial for yield gain to enhance functional breeding approaches and guide in crop modeling.
Collapse
Affiliation(s)
- Sanbon Chaka Gosa
- The Robert H Smith Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot 7610001, Israel
| | - Yaniv Lupo
- The Robert H Smith Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot 7610001, Israel
| | - Menachem Moshelion
- The Robert H Smith Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot 7610001, Israel.
| |
Collapse
|
24
|
Sang M, Shi H, Wei K, Ye M, Jiang L, Sun L, Wu R. A dissection model for mapping complex traits. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 97:1168-1182. [PMID: 30536697 DOI: 10.1111/tpj.14185] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 10/26/2018] [Accepted: 11/26/2018] [Indexed: 06/09/2023]
Abstract
Many quantitative traits are composites of other traits that contribute differentially to genetic variation. Quantitative trait locus (QTL) mapping of these composite traits can benefit by incorporating the mechanistic process of how their formation is mediated by the underlying components. We propose a dissection model by which to map these interconnected components traits under a joint likelihood setting. The model can test how a composite trait is determined by pleiotropic QTLs for its component traits or jointly by different sets of QTLs each responsible for a different component. The model can visualize the pattern of time-varying genetic effects for individual components and their impacts on composite traits. The dissection model was used to map two composite traits, stemwood volume growth decomposed into its stem height, stem diameter and stem form components for Euramerican poplar adult trees, and total lateral root length constituted by its average lateral root length and lateral root number components for Euphrates poplar seedlings. We found the pattern of how QTLs for different components contribute to phenotypic variation in composite traits. The detailed understanding of the genetic machineries of composite traits will not only help in the design of molecular breeding in plants and animals, but also shed light on the evolutionary processes of quantitative traits under natural selection.
Collapse
Affiliation(s)
- Mengmeng Sang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | - Hexin Shi
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | - Kun Wei
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | - Meixia Ye
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | - Libo Jiang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | - Lidan Sun
- Beijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding, National Engineering Research Center for Floriculture, College of Landscape Architecture, Beijing Forestry University, Beijing, 100083, China
| | - Rongling Wu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
- State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing, 100091, China
- Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, Pennsylvania State University, Hershey, PA, 17033, USA
| |
Collapse
|
25
|
Serôdio J, Schmidt W, Frommlet JC, Christa G, Nitschke MR. An LED-based multi-actinic illumination system for the high throughput study of photosynthetic light responses. PeerJ 2018; 6:e5589. [PMID: 30202661 PMCID: PMC6128260 DOI: 10.7717/peerj.5589] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 08/15/2018] [Indexed: 11/20/2022] Open
Abstract
The responses of photosynthetic organisms to light stress are of interest for both fundamental and applied research. Functional traits related to the photoinhibition, the light-induced loss of photosynthetic efficiency, are particularly interesting as this process is a key limiting factor of photosynthetic productivity in algae and plants. The quantitative characterization of light responses is often time-consuming and calls for cost-effective high throughput approaches that enable the fast screening of multiple samples. Here we present a novel illumination system based on the concept of ‘multi-actinic imaging’ of in vivo chlorophyll fluorescence. The system is based on the combination of an array of individually addressable low power RGBW LEDs and custom-designed well plates, allowing for the independent illumination of 64 samples through the digital manipulation of both exposure duration and light intensity. The illumination system is inexpensive and easily fabricated, based on open source electronics, off-the-shelf components, and 3D-printed parts, and is optimized for imaging of chlorophyll fluorescence. The high-throughput potential of the system is illustrated by assessing the functional diversity in light responses of marine macroalgal species, through the fast and simultaneous determination of kinetic parameters characterizing the response to light stress of multiple samples. Although the presented illumination system was primarily designed for the measurement of phenotypic traits related to photosynthetic activity and photoinhibition, it can be potentially used for a number of alternative applications, including the measurement of chloroplast phototaxis and action spectra, or as the basis for microphotobioreactors.
Collapse
Affiliation(s)
- João Serôdio
- Department of Biology and CESAM-Centre for Environmental and Marine Studies, University of Aveiro, Aveiro, Portugal
| | - William Schmidt
- Department of Biology and CESAM-Centre for Environmental and Marine Studies, University of Aveiro, Aveiro, Portugal
| | - Jörg C Frommlet
- Department of Biology and CESAM-Centre for Environmental and Marine Studies, University of Aveiro, Aveiro, Portugal
| | - Gregor Christa
- Department of Biology and CESAM-Centre for Environmental and Marine Studies, University of Aveiro, Aveiro, Portugal
| | - Matthew R Nitschke
- Department of Biology and CESAM-Centre for Environmental and Marine Studies, University of Aveiro, Aveiro, Portugal
| |
Collapse
|
26
|
Taghavi Namin S, Esmaeilzadeh M, Najafi M, Brown TB, Borevitz JO. Deep phenotyping: deep learning for temporal phenotype/genotype classification. PLANT METHODS 2018; 14:66. [PMID: 30087695 PMCID: PMC6076396 DOI: 10.1186/s13007-018-0333-4] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Accepted: 07/24/2018] [Indexed: 05/20/2023]
Abstract
BACKGROUND High resolution and high throughput genotype to phenotype studies in plants are underway to accelerate breeding of climate ready crops. In the recent years, deep learning techniques and in particular Convolutional Neural Networks (CNNs), Recurrent Neural Networks and Long-Short Term Memories (LSTMs), have shown great success in visual data recognition, classification, and sequence learning tasks. More recently, CNNs have been used for plant classification and phenotyping, using individual static images of the plants. On the other hand, dynamic behavior of the plants as well as their growth has been an important phenotype for plant biologists, and this motivated us to study the potential of LSTMs in encoding these temporal information for the accession classification task, which is useful in automation of plant production and care. METHODS In this paper, we propose a CNN-LSTM framework for plant classification of various genotypes. Here, we exploit the power of deep CNNs for automatic joint feature and classifier learning, compared to using hand-crafted features. In addition, we leverage the potential of LSTMs to study the growth of the plants and their dynamic behaviors as important discriminative phenotypes for accession classification. Moreover, we collected a dataset of time-series image sequences of four accessions of Arabidopsis, captured in similar imaging conditions, which could be used as a standard benchmark by researchers in the field. We made this dataset publicly available. CONCLUSION The results provide evidence of the benefits of our accession classification approach over using traditional hand-crafted image analysis features and other accession classification frameworks. We also demonstrate that utilizing temporal information using LSTMs can further improve the performance of the system. The proposed framework can be used in other applications such as in plant classification given the environment conditions or in distinguishing diseased plants from healthy ones.
Collapse
Affiliation(s)
- Sarah Taghavi Namin
- Research School of Biology, Australian National University, Canberra, Australia
- Research School of Engineering and Computer Science, Australian National University, Canberra, Australia
| | - Mohammad Esmaeilzadeh
- Research School of Biology, Australian National University, Canberra, Australia
- Research School of Engineering and Computer Science, Australian National University, Canberra, Australia
| | - Mohammad Najafi
- Research School of Engineering and Computer Science, Australian National University, Canberra, Australia
| | - Tim B. Brown
- Research School of Biology, Australian National University, Canberra, Australia
| | - Justin O. Borevitz
- Research School of Biology, Australian National University, Canberra, Australia
| |
Collapse
|
27
|
Manacorda CA, Asurmendi S. Arabidopsis phenotyping through geometric morphometrics. Gigascience 2018; 7:5039702. [PMID: 29917076 PMCID: PMC6041757 DOI: 10.1093/gigascience/giy073] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 02/07/2018] [Accepted: 06/11/2018] [Indexed: 12/18/2022] Open
Abstract
Background Recently, great technical progress has been achieved in the field of plant phenotyping. High-throughput platforms and the development of improved algorithms for rosette image segmentation make it possible to extract shape and size parameters for genetic, physiological, and environmental studies on a large scale. The development of low-cost phenotyping platforms and freeware resources make it possible to widely expand phenotypic analysis tools for Arabidopsis. However, objective descriptors of shape parameters that could be used independently of the platform and segmentation software used are still lacking, and shape descriptions still rely on ad hoc or even contradictory descriptors, which could make comparisons difficult and perhaps inaccurate. Modern geometric morphometrics is a family of methods in quantitative biology proposed to be the main source of data and analytical tools in the emerging field of phenomics studies. Based on the location of landmarks (corresponding points) over imaged specimens and by combining geometry, multivariate analysis, and powerful statistical techniques, these tools offer the possibility to reproducibly and accurately account for shape variations among groups and measure them in shape distance units. Results Here, a particular scheme of landmark placement on Arabidopsis rosette images is proposed to study shape variation in viral infection processes. Shape differences between controls and infected plants are quantified throughout the infectious process and visualized. Quantitative comparisons between two unrelated ssRNA+ viruses are shown, and reproducibility issues are assessed. Conclusions Combined with the newest automated platforms and plant segmentation procedures, geometric morphometric tools could boost phenotypic features extraction and processing in an objective, reproducible manner.
Collapse
Affiliation(s)
- Carlos A Manacorda
- Instituto de Biotecnología, CICVyA, INTA, Nicolas Repetto y de los Reseros s/n, Hurlingham, (1686) Buenos Aires, Argentina
| | - Sebastian Asurmendi
- Instituto de Biotecnología, CICVyA, INTA, Nicolas Repetto y de los Reseros s/n, Hurlingham, (1686) Buenos Aires, Argentina
- CONICET, Nicolas Repetto y de los Reseros s/n, Hurlingham, (1686) Buenos Aires, Argentina
| |
Collapse
|
28
|
Zhang D, Zhou X, Zhang J, Lan Y, Xu C, Liang D. Detection of rice sheath blight using an unmanned aerial system with high-resolution color and multispectral imaging. PLoS One 2018; 13:e0187470. [PMID: 29746473 PMCID: PMC5945033 DOI: 10.1371/journal.pone.0187470] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2017] [Accepted: 10/20/2017] [Indexed: 11/18/2022] Open
Abstract
Detection and monitoring are the first essential step for effective management of sheath blight (ShB), a major disease in rice worldwide. Unmanned aerial systems have a high potential of being utilized to improve this detection process since they can reduce the time needed for scouting for the disease at a field scale, and are affordable and user-friendly in operation. In this study, a commercialized quadrotor unmanned aerial vehicle (UAV), equipped with digital and multispectral cameras, was used to capture imagery data of research plots with 67 rice cultivars and elite lines. Collected imagery data were then processed and analyzed to characterize the development of ShB and quantify different levels of the disease in the field. Through color features extraction and color space transformation of images, it was found that the color transformation could qualitatively detect the infected areas of ShB in the field plots. However, it was less effective to detect different levels of the disease. Five vegetation indices were then calculated from the multispectral images, and ground truths of disease severity and GreenSeeker measured NDVI (Normalized Difference Vegetation Index) were collected. The results of relationship analyses indicate that there was a strong correlation between ground-measured NDVIs and image-extracted NDVIs with the R2 of 0.907 and the root mean square error (RMSE) of 0.0854, and a good correlation between image-extracted NDVIs and disease severity with the R2 of 0.627 and the RMSE of 0.0852. Use of image-based NDVIs extracted from multispectral images could quantify different levels of ShB in the field plots with an accuracy of 63%. These results demonstrate that a customer-grade UAV integrated with digital and multispectral cameras can be an effective tool to detect the ShB disease at a field scale.
Collapse
Affiliation(s)
- Dongyan Zhang
- Anhui Engineering Laboratory of Agro-Ecological Big Data, Anhui University, Hefei, Anhui, China
| | - Xingen Zhou
- Texas A&M AgriLife Research Center, Texas A&M University System, Beaumont, Texas, United States of America
| | - Jian Zhang
- College of Resources and Environment, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Yubin Lan
- Texas A&M AgriLife Research Center, Texas A&M University System, Beaumont, Texas, United States of America
- College of Engineering, South China Agricultural University, Guangzhou, Guangdong, China
| | - Chao Xu
- Anhui Engineering Laboratory of Agro-Ecological Big Data, Anhui University, Hefei, Anhui, China
| | - Dong Liang
- Anhui Engineering Laboratory of Agro-Ecological Big Data, Anhui University, Hefei, Anhui, China
- * E-mail:
| |
Collapse
|
29
|
Rymaszewski W, Dauzat M, Bédiée A, Rolland G, Luchaire N, Granier C, Hennig J, Vile D. Measurement of Arabidopsis thaliana Plant Traits Using the PHENOPSIS Phenotyping Platform. Bio Protoc 2018; 8:e2739. [PMID: 34179267 DOI: 10.21769/bioprotoc.2739] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Revised: 01/30/2018] [Accepted: 02/08/2018] [Indexed: 12/22/2022] Open
Abstract
High-throughput phenotyping of plant traits is a powerful tool to further our understanding of plant growth and its underlying physiological, molecular, and genetic determinisms. This protocol describes the methodology of a standard phenotyping experiment in PHENOPSIS automated platform, which was engineered in INRA-LEPSE (https://www6.montpellier.inra.fr/lepse) and custom-made by Optimalog company. The seminal method was published by Granier et al. (2006). The platform is used to explore and test various ecophysiological hypotheses (Tisné et al., 2010; Baerenfaller et al., 2012; Vile et al., 2012; Bac-Molenaar et al., 2015; Rymaszewski et al., 2017). Here, the focus concerns the preparation and management of experiments, as well as measurements of growth-related traits (e.g., projected rosette area, total leaf area and growth rate), water status-related traits (e.g., leaf dry matter content and relative water content), and plant architecture-related traits (e.g., stomatal density and index and lamina/petiole ratio). Briefly, a completely randomized (block) design is set up in the growth chamber. Next, the substrate is prepared, its initial water content is measured and pots are filled. Seeds are sown onto the soil surface and germinated prior to the experiment. After germination, soil watering and image (visible, infra-red, fluorescence) acquisition are planned by the user and performed by the automaton. Destructive measurements may be performed during the experiment. Data extraction from images and estimation of growth-related trait values involves semi-automated procedures and statistical processing.
Collapse
Affiliation(s)
- Wojciech Rymaszewski
- Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Pawinskiego 5a, Warsaw, Poland
| | - Myriam Dauzat
- LEPSE, Univ Montpellier, INRA, Montpellier SupAgro, Montpellier, France
| | - Alexis Bédiée
- LEPSE, Univ Montpellier, INRA, Montpellier SupAgro, Montpellier, France
| | - Gaëlle Rolland
- LEPSE, Univ Montpellier, INRA, Montpellier SupAgro, Montpellier, France
| | - Nathalie Luchaire
- LEPSE, Univ Montpellier, INRA, Montpellier SupAgro, Montpellier, France
| | - Christine Granier
- LEPSE, Univ Montpellier, INRA, Montpellier SupAgro, Montpellier, France.,AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France
| | - Jacek Hennig
- Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Pawinskiego 5a, Warsaw, Poland
| | - Denis Vile
- LEPSE, Univ Montpellier, INRA, Montpellier SupAgro, Montpellier, France
| |
Collapse
|
30
|
Taghavi Namin S, Esmaeilzadeh M, Najafi M, Brown TB, Borevitz JO. Deep phenotyping: deep learning for temporal phenotype/genotype classification. PLANT METHODS 2018; 14:66. [PMID: 30087695 DOI: 10.1101/134205] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Accepted: 07/24/2018] [Indexed: 05/21/2023]
Abstract
BACKGROUND High resolution and high throughput genotype to phenotype studies in plants are underway to accelerate breeding of climate ready crops. In the recent years, deep learning techniques and in particular Convolutional Neural Networks (CNNs), Recurrent Neural Networks and Long-Short Term Memories (LSTMs), have shown great success in visual data recognition, classification, and sequence learning tasks. More recently, CNNs have been used for plant classification and phenotyping, using individual static images of the plants. On the other hand, dynamic behavior of the plants as well as their growth has been an important phenotype for plant biologists, and this motivated us to study the potential of LSTMs in encoding these temporal information for the accession classification task, which is useful in automation of plant production and care. METHODS In this paper, we propose a CNN-LSTM framework for plant classification of various genotypes. Here, we exploit the power of deep CNNs for automatic joint feature and classifier learning, compared to using hand-crafted features. In addition, we leverage the potential of LSTMs to study the growth of the plants and their dynamic behaviors as important discriminative phenotypes for accession classification. Moreover, we collected a dataset of time-series image sequences of four accessions of Arabidopsis, captured in similar imaging conditions, which could be used as a standard benchmark by researchers in the field. We made this dataset publicly available. CONCLUSION The results provide evidence of the benefits of our accession classification approach over using traditional hand-crafted image analysis features and other accession classification frameworks. We also demonstrate that utilizing temporal information using LSTMs can further improve the performance of the system. The proposed framework can be used in other applications such as in plant classification given the environment conditions or in distinguishing diseased plants from healthy ones.
Collapse
Affiliation(s)
- Sarah Taghavi Namin
- 1Research School of Biology, Australian National University, Canberra, Australia
- 2Research School of Engineering and Computer Science, Australian National University, Canberra, Australia
| | - Mohammad Esmaeilzadeh
- 1Research School of Biology, Australian National University, Canberra, Australia
- 2Research School of Engineering and Computer Science, Australian National University, Canberra, Australia
| | - Mohammad Najafi
- 2Research School of Engineering and Computer Science, Australian National University, Canberra, Australia
| | - Tim B Brown
- 1Research School of Biology, Australian National University, Canberra, Australia
| | - Justin O Borevitz
- 1Research School of Biology, Australian National University, Canberra, Australia
| |
Collapse
|
31
|
Valle B, Simonneau T, Boulord R, Sourd F, Frisson T, Ryckewaert M, Hamard P, Brichet N, Dauzat M, Christophe A. PYM: a new, affordable, image-based method using a Raspberry Pi to phenotype plant leaf area in a wide diversity of environments. PLANT METHODS 2017; 13:98. [PMID: 29151844 PMCID: PMC5678554 DOI: 10.1186/s13007-017-0248-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Accepted: 10/26/2017] [Indexed: 05/24/2023]
Abstract
BACKGROUND Plant science uses increasing amounts of phenotypic data to unravel the complex interactions between biological systems and their variable environments. Originally, phenotyping approaches were limited by manual, often destructive operations, causing large errors. Plant imaging emerged as a viable alternative allowing non-invasive and automated data acquisition. Several procedures based on image analysis were developed to monitor leaf growth as a major phenotyping target. However, in most proposals, a time-consuming parameterization of the analysis pipeline is required to handle variable conditions between images, particularly in the field due to unstable light and interferences with soil surface or weeds. To cope with these difficulties, we developed a low-cost, 2D imaging method, hereafter called PYM. The method is based on plant leaf ability to absorb blue light while reflecting infrared wavelengths. PYM consists of a Raspberry Pi computer equipped with an infrared camera and a blue filter and is associated with scripts that compute projected leaf area. This new method was tested on diverse species placed in contrasting conditions. Application to field conditions was evaluated on lettuces grown under photovoltaic panels. The objective was to look for possible acclimation of leaf expansion under photovoltaic panels to optimise the use of solar radiation per unit soil area. RESULTS The new PYM device proved to be efficient and accurate for screening leaf area of various species in wide ranges of environments. In the most challenging conditions that we tested, error on plant leaf area was reduced to 5% using PYM compared to 100% when using a recently published method. A high-throughput phenotyping cart, holding 6 chained PYM devices, was designed to capture up to 2000 pictures of field-grown lettuce plants in less than 2 h. Automated analysis of image stacks of individual plants over their growth cycles revealed unexpected differences in leaf expansion rate between lettuces rows depending on their position below or between the photovoltaic panels. CONCLUSIONS The imaging device described here has several benefits, such as affordability, low cost, reliability and flexibility for online analysis and storage. It should be easily appropriated and customized to meet the needs of various users.
Collapse
Affiliation(s)
- Benoît Valle
- UMR759 Laboratoire d’Ecophysiologie des Plantes sous Stress Environnementaux (LEPSE), INRA, Montpellier SupAgro, 2 Place Pierre Viala, 34060 Montpellier Cedex 2, France
- Sun’R SAS, 7 rue de Clichy, 75009 Paris, France
| | - Thierry Simonneau
- UMR759 Laboratoire d’Ecophysiologie des Plantes sous Stress Environnementaux (LEPSE), INRA, Montpellier SupAgro, 2 Place Pierre Viala, 34060 Montpellier Cedex 2, France
| | - Romain Boulord
- UMR759 Laboratoire d’Ecophysiologie des Plantes sous Stress Environnementaux (LEPSE), INRA, Montpellier SupAgro, 2 Place Pierre Viala, 34060 Montpellier Cedex 2, France
| | | | | | | | - Philippe Hamard
- UMR759 Laboratoire d’Ecophysiologie des Plantes sous Stress Environnementaux (LEPSE), INRA, Montpellier SupAgro, 2 Place Pierre Viala, 34060 Montpellier Cedex 2, France
| | - Nicolas Brichet
- UMR759 Laboratoire d’Ecophysiologie des Plantes sous Stress Environnementaux (LEPSE), INRA, Montpellier SupAgro, 2 Place Pierre Viala, 34060 Montpellier Cedex 2, France
| | - Myriam Dauzat
- UMR759 Laboratoire d’Ecophysiologie des Plantes sous Stress Environnementaux (LEPSE), INRA, Montpellier SupAgro, 2 Place Pierre Viala, 34060 Montpellier Cedex 2, France
| | - Angélique Christophe
- UMR759 Laboratoire d’Ecophysiologie des Plantes sous Stress Environnementaux (LEPSE), INRA, Montpellier SupAgro, 2 Place Pierre Viala, 34060 Montpellier Cedex 2, France
| |
Collapse
|
32
|
Perez-Sanz F, Navarro PJ, Egea-Cortines M. Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms. Gigascience 2017; 6:1-18. [PMID: 29048559 PMCID: PMC5737281 DOI: 10.1093/gigascience/gix092] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Revised: 06/20/2017] [Accepted: 09/17/2017] [Indexed: 11/25/2022] Open
Abstract
The study of phenomes or phenomics has been a central part of biology. The field of automatic phenotype acquisition technologies based on images has seen an important advance in the last years. As with other high-throughput technologies, it addresses a common set of problems, including data acquisition and analysis. In this review, we give an overview of the main systems developed to acquire images. We give an in-depth analysis of image processing with its major issues and the algorithms that are being used or emerging as useful to obtain data out of images in an automatic fashion.
Collapse
Affiliation(s)
- Fernando Perez-Sanz
- Genetics, ETSIA, Instituto de Biotecnología Vegetal, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
| | - Pedro J Navarro
- Genetics, Instituto de Biotecnología Vegetal, Universidad Politécnica de Cartagena, Campus Muralla del Mar, s/n, Cartagena 30202, Spain
| | - Marcos Egea-Cortines
- Genetics, ETSIA, Instituto de Biotecnología Vegetal, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
| |
Collapse
|
33
|
Ndour A, Vadez V, Pradal C, Lucas M. Virtual Plants Need Water Too: Functional-Structural Root System Models in the Context of Drought Tolerance Breeding. FRONTIERS IN PLANT SCIENCE 2017; 8:1577. [PMID: 29018456 PMCID: PMC5622977 DOI: 10.3389/fpls.2017.01577] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Accepted: 08/29/2017] [Indexed: 05/04/2023]
Abstract
Developing a sustainable agricultural model is one of the great challenges of the coming years. The agricultural practices inherited from the Green Revolution of the 1960s show their limits today, and new paradigms need to be explored to counter rising issues such as the multiplication of climate-change related drought episodes. Two such new paradigms are the use of functional-structural plant models to complement and rationalize breeding approaches and a renewed focus on root systems as untapped sources of plant amelioration. Since the late 1980s, numerous functional and structural models of root systems were developed and used to investigate the properties of root systems in soil or lab-conditions. In this review, we focus on the conception and use of such root models in the broader context of research on root-driven drought tolerance, on the basis of root system architecture (RSA) phenotyping. Such models result from the integration of architectural, physiological and environmental data. Here, we consider the different phenotyping techniques allowing for root architectural and physiological study and their limits. We discuss how QTL and breeding studies support the manipulation of RSA as a way to improve drought resistance. We then go over the integration of the generated data within architectural models, how those architectural models can be coupled with functional hydraulic models, and how functional parameters can be measured to feed those models. We then consider the assessment and validation of those hydraulic models through confrontation of simulations to experimentations. Finally, we discuss the up and coming challenges facing root systems functional-structural modeling approaches in the context of breeding.
Collapse
Affiliation(s)
- Adama Ndour
- Laboratoire Mixte International Adaptation des Plantes et Microorganismes Associés Aux Stress Environnementaux (LAPSE), Dakar, Senegal
- Laboratoire Commun de Microbiologie (IRD-ISRA-UCAD), Dakar, Senegal
- CERES, IRD, Université de Montpellier, UMR DIADE, Montpellier, France
- Département Maths/Informatique, Faculté des Sciences et Techniques, Université Cheikh Anta Diop, Dakar, Senegal
| | - Vincent Vadez
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
| | - Christophe Pradal
- UMR AGAP, Univiversité de Montpellier, CIRAD, INRA, Inria, Montpellier SupAgro, Montpellier, France
| | - Mikaël Lucas
- Laboratoire Mixte International Adaptation des Plantes et Microorganismes Associés Aux Stress Environnementaux (LAPSE), Dakar, Senegal
- Laboratoire Commun de Microbiologie (IRD-ISRA-UCAD), Dakar, Senegal
- CERES, IRD, Université de Montpellier, UMR DIADE, Montpellier, France
| |
Collapse
|
34
|
Rahaman MM, Ahsan MA, Gillani Z, Chen M. Digital Biomass Accumulation Using High-Throughput Plant Phenotype Data Analysis. J Integr Bioinform 2017; 14:/j/jib.2017.14.issue-3/jib-2017-0028/jib-2017-0028.xml. [PMID: 28862986 PMCID: PMC6042821 DOI: 10.1515/jib-2017-0028] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Revised: 05/29/2017] [Accepted: 07/12/2017] [Indexed: 12/20/2022] Open
Abstract
Biomass is an important phenotypic trait in functional ecology and growth analysis. The typical methods for measuring biomass are destructive, and they require numerous individuals to be cultivated for repeated measurements. With the advent of image-based high-throughput plant phenotyping facilities, non-destructive biomass measuring methods have attempted to overcome this problem. Thus, the estimation of plant biomass of individual plants from their digital images is becoming more important. In this paper, we propose an approach to biomass estimation based on image derived phenotypic traits. Several image-based biomass studies state that the estimation of plant biomass is only a linear function of the projected plant area in images. However, we modeled the plant volume as a function of plant area, plant compactness, and plant age to generalize the linear biomass model. The obtained results confirm the proposed model and can explain most of the observed variance during image-derived biomass estimation. Moreover, a small difference was observed between actual and estimated digital biomass, which indicates that our proposed approach can be used to estimate digital biomass accurately.
Collapse
Affiliation(s)
- Md. Matiur Rahaman
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Md. Asif Ahsan
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Zeeshan Gillani
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
- COMSATS Institute of Information Technology – MA Jinnah Campus, Computer Science 1km Defense Road, Lahore 54000, Pakistan
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| |
Collapse
|
35
|
Feng H, Guo Z, Yang W, Huang C, Chen G, Fang W, Xiong X, Zhang H, Wang G, Xiong L, Liu Q. An integrated hyperspectral imaging and genome-wide association analysis platform provides spectral and genetic insights into the natural variation in rice. Sci Rep 2017; 7:4401. [PMID: 28667309 PMCID: PMC5493659 DOI: 10.1038/s41598-017-04668-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Accepted: 05/17/2017] [Indexed: 01/24/2023] Open
Abstract
With progress of genetic sequencing technology, plant genomics has experienced rapid development and subsequently triggered the progress of plant phenomics. In this study, a high-throughput hyperspectral imaging system (HHIS) was developed to obtain 1,540 hyperspectral indices at whole-plant level during tillering, heading, and ripening stages. These indices were used to quantify traditional agronomic traits and to explore genetic variation. We performed genome-wide association study (GWAS) of these indices and traditional agronomic traits in a global rice collection of 529 accessions. With the genome-level suggestive P-value threshold, 989 loci were identified. Of the 1,540 indices, we detected 502 significant indices (designated as hyper-traits) that exhibited phenotypic and genetic relationship with traditional agronomic traits and had high heritability. Many hyper-trait-associated loci could not be detected using traditional agronomic traits. For example, we identified a candidate gene controlling chlorophyll content (Chl). This gene, which was not identified based on Chl, was significantly associated with a chlorophyll-related hyper-trait in GWAS and was demonstrated to control Chl. Moreover, our study demonstrates that red edge (680-760 nm) is vital for rice research for phenotypic and genetic insights. Thus, combination of HHIS and GWAS provides a novel platform for dissection of complex traits and for crop breeding.
Collapse
Affiliation(s)
- Hui Feng
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, China.,Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, Wuhan, 430070, China.,Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Zilong Guo
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, China
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, China.,Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, Wuhan, 430070, China
| | - Chenglong Huang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, China.,Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, Wuhan, 430070, China
| | - Guoxing Chen
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, China
| | - Wei Fang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Xiong Xiong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Hongyu Zhang
- Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, Wuhan, 430070, China
| | - Gongwei Wang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, China
| | - Lizhong Xiong
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, China.
| | - Qian Liu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China. .,MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
| |
Collapse
|
36
|
In-Field High-Throughput Phenotyping of Cotton Plant Height Using LiDAR. REMOTE SENSING 2017. [DOI: 10.3390/rs9040377] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|
37
|
Bellucci A, Tondelli A, Fangel JU, Torp AM, Xu X, Willats WGT, Flavell A, Cattivelli L, Rasmussen SK. Genome-wide association mapping in winter barley for grain yield and culm cell wall polymer content using the high-throughput CoMPP technique. PLoS One 2017; 12:e0173313. [PMID: 28301509 PMCID: PMC5354286 DOI: 10.1371/journal.pone.0173313] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2016] [Accepted: 02/17/2017] [Indexed: 12/21/2022] Open
Abstract
A collection of 112 winter barley varieties (Hordeum vulgare L.) was grown in the field for two years (2008/09 and 2009/10) in northern Italy and grain and straw yields recorded. In the first year of the trial, a severe attack of barley yellow mosaic virus (BaYMV) strongly influenced final performances with an average reduction of ~ 50% for grain and straw harvested in comparison to the second year. The genetic determination (GD) for grain yield was 0.49 and 0.70, for the two years respectively, and for straw yield GD was low in 2009 (0.09) and higher in 2010 (0.29). Cell wall polymers in culms were quantified by means of the monoclonal antibodies LM6, LM11, JIM13 and BS-400-3 and the carbohydrate-binding module CBM3a using the high-throughput CoMPP technique. Of these, LM6, which detects arabinan components, showed a relatively high GD in both years and a significantly negative correlation with grain yield (GYLD). Overall, heritability (H2) was calculated for GYLD, LM6 and JIM and resulted to be 0.42, 0.32 and 0.20, respectively. A total of 4,976 SNPs from the 9K iSelect array were used in the study for the analysis of population structure, linkage disequilibrium (LD) and genome-wide association study (GWAS). Marker-trait associations (MTA) were analyzed for grain yield and cell wall determination by LM6 and JIM13 as these were the traits showing significant correlations between the years. A single QTL for GYLD containing three MTAs was found on chromosome 3H located close to the Hv-eIF4E gene, which is known to regulate resistance to BaYMV. Subsequently the QTL was shown to be tightly linked to rym4, a locus for resistance to the virus. GWAs on arabinans quantified by LM6 resulted in the identification of major QTLs closely located on 3H and hypotheses regarding putative candidate genes were formulated through the study of gene expression levels based on bioinformatics tools.
Collapse
Affiliation(s)
- Andrea Bellucci
- Department of Plant and Environmental Sciences, Faculty of Sciences, University of Copenhagen, Frederiksberg, Denmark
| | - Alessandro Tondelli
- Consiglio per la Ricerca e la Sperimentazione in Agricoltura e l’Analisi dell’Economia Agraria, Centro di Ricerca per la Genomica Vegetale, Fiorenzuola d’Arda, Italy
| | - Jonatan U. Fangel
- Department of Plant and Environmental Sciences, Faculty of Sciences, University of Copenhagen, Frederiksberg, Denmark
| | - Anna Maria Torp
- Department of Plant and Environmental Sciences, Faculty of Sciences, University of Copenhagen, Frederiksberg, Denmark
| | - Xin Xu
- School of Life Science, University of Dundee, Dundee, United Kingdom
| | - William G. T. Willats
- Department of Plant and Environmental Sciences, Faculty of Sciences, University of Copenhagen, Frederiksberg, Denmark
| | - Andrew Flavell
- School of Life Science, University of Dundee, Dundee, United Kingdom
| | - Luigi Cattivelli
- Consiglio per la Ricerca e la Sperimentazione in Agricoltura e l’Analisi dell’Economia Agraria, Centro di Ricerca per la Genomica Vegetale, Fiorenzuola d’Arda, Italy
| | - Søren K. Rasmussen
- Department of Plant and Environmental Sciences, Faculty of Sciences, University of Copenhagen, Frederiksberg, Denmark
- * E-mail:
| |
Collapse
|
38
|
Tanger P, Klassen S, Mojica JP, Lovell JT, Moyers BT, Baraoidan M, Naredo MEB, McNally KL, Poland J, Bush DR, Leung H, Leach JE, McKay JK. Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice. Sci Rep 2017; 7:42839. [PMID: 28220807 PMCID: PMC5318881 DOI: 10.1038/srep42839] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Accepted: 01/16/2017] [Indexed: 11/16/2022] Open
Abstract
To ensure food security in the face of population growth, decreasing water and land for agriculture, and increasing climate variability, crop yields must increase faster than the current rates. Increased yields will require implementing novel approaches in genetic discovery and breeding. Here we demonstrate the potential of field-based high throughput phenotyping (HTP) on a large recombinant population of rice to identify genetic variation underlying important traits. We find that detecting quantitative trait loci (QTL) with HTP phenotyping is as accurate and effective as traditional labor-intensive measures of flowering time, height, biomass, grain yield, and harvest index. Genetic mapping in this population, derived from a cross of an modern cultivar (IR64) with a landrace (Aswina), identified four alleles with negative effect on grain yield that are fixed in IR64, demonstrating the potential for HTP of large populations as a strategy for the second green revolution.
Collapse
Affiliation(s)
- Paul Tanger
- Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO, USA
| | - Stephen Klassen
- International Rice Research Institute (IRRI), Los Baños, Philippines
| | - Julius P. Mojica
- Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO, USA
- Department of Biology, Duke University, Durham, NC, USA
| | - John T. Lovell
- Department of Integrative Biology, University of Texas, Austin, Austin, TX, USA
| | - Brook T. Moyers
- Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO, USA
| | | | | | | | - Jesse Poland
- Departments of Plant Pathology and Agronomy, Kansas State University, Manhattan, KS, USA
| | - Daniel R. Bush
- Department of Biology, Colorado State University, Fort Collins, CO, USA
| | - Hei Leung
- International Rice Research Institute (IRRI), Los Baños, Philippines
| | - Jan E. Leach
- Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO, USA
| | - John K. McKay
- Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO, USA
| |
Collapse
|
39
|
Zhao J, Bodner G, Rewald B, Leitner D, Nagel KA, Nakhforoosh A. Root architecture simulation improves the inference from seedling root phenotyping towards mature root systems. JOURNAL OF EXPERIMENTAL BOTANY 2017; 68:965-982. [PMID: 28168270 PMCID: PMC5441853 DOI: 10.1093/jxb/erw494] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Root phenotyping provides trait information for plant breeding. A shortcoming of high-throughput root phenotyping is the limitation to seedling plants and failure to make inferences on mature root systems. We suggest root system architecture (RSA) models to predict mature root traits and overcome the inference problem. Sixteen pea genotypes were phenotyped in (i) seedling (Petri dishes) and (ii) mature (sand-filled columns) root phenotyping platforms. The RSA model RootBox was parameterized with seedling traits to simulate the fully developed root systems. Measured and modelled root length, first-order lateral number, and root distribution were compared to determine key traits for model-based prediction. No direct relationship in root traits (tap, lateral length, interbranch distance) was evident between phenotyping systems. RootBox significantly improved the inference over phenotyping platforms. Seedling plant tap and lateral root elongation rates and interbranch distance were sufficient model parameters to predict genotype ranking in total root length with an RSpearman of 0.83. Parameterization including uneven lateral spacing via a scaling function substantially improved the prediction of architectures underlying the differently sized root systems. We conclude that RSA models can solve the inference problem of seedling root phenotyping. RSA models should be included in the phenotyping pipeline to provide reliable information on mature root systems to breeding research.
Collapse
Affiliation(s)
- Jiangsan Zhao
- Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences, Vienna (BOKU), Peter-Jordan-Straße 82, 1190 Vienna, Austria
| | - Gernot Bodner
- Division of Agronomy, Department of Crop Sciences, University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad Lorenz-Straße 24, 3430 Tulln an der Donau, Austria
| | - Boris Rewald
- Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences, Vienna (BOKU), Peter-Jordan-Straße 82, 1190 Vienna, Austria
| | - Daniel Leitner
- Computational Science Center, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria
| | - Kerstin A Nagel
- Institute of Biosciences and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, D-52425 Jülich, Germany
| | - Alireza Nakhforoosh
- Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences, Vienna (BOKU), Peter-Jordan-Straße 82, 1190 Vienna, Austria
- Division of Agronomy, Department of Crop Sciences, University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad Lorenz-Straße 24, 3430 Tulln an der Donau, Austria
| |
Collapse
|
40
|
Thoen MPM, Davila Olivas NH, Kloth KJ, Coolen S, Huang P, Aarts MGM, Bac‐Molenaar JA, Bakker J, Bouwmeester HJ, Broekgaarden C, Bucher J, Busscher‐Lange J, Cheng X, Fradin EF, Jongsma MA, Julkowska MM, Keurentjes JJB, Ligterink W, Pieterse CMJ, Ruyter‐Spira C, Smant G, Testerink C, Usadel B, van Loon JJA, van Pelt JA, van Schaik CC, van Wees SCM, Visser RGF, Voorrips R, Vosman B, Vreugdenhil D, Warmerdam S, Wiegers GL, van Heerwaarden J, Kruijer W, van Eeuwijk FA, Dicke M. Genetic architecture of plant stress resistance: multi-trait genome-wide association mapping. THE NEW PHYTOLOGIST 2017; 213:1346-1362. [PMID: 27699793 PMCID: PMC5248600 DOI: 10.1111/nph.14220] [Citation(s) in RCA: 95] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2016] [Accepted: 08/17/2016] [Indexed: 05/19/2023]
Abstract
Plants are exposed to combinations of various biotic and abiotic stresses, but stress responses are usually investigated for single stresses only. Here, we investigated the genetic architecture underlying plant responses to 11 single stresses and several of their combinations by phenotyping 350 Arabidopsis thaliana accessions. A set of 214 000 single nucleotide polymorphisms (SNPs) was screened for marker-trait associations in genome-wide association (GWA) analyses using tailored multi-trait mixed models. Stress responses that share phytohormonal signaling pathways also share genetic architecture underlying these responses. After removing the effects of general robustness, for the 30 most significant SNPs, average quantitative trait locus (QTL) effect sizes were larger for dual stresses than for single stresses. Plants appear to deploy broad-spectrum defensive mechanisms influencing multiple traits in response to combined stresses. Association analyses identified QTLs with contrasting and with similar responses to biotic vs abiotic stresses, and below-ground vs above-ground stresses. Our approach allowed for an unprecedented comprehensive genetic analysis of how plants deal with a wide spectrum of stress conditions.
Collapse
|
41
|
Viaud G, Loudet O, Cournède PH. Leaf Segmentation and Tracking in Arabidopsis thaliana Combined to an Organ-Scale Plant Model for Genotypic Differentiation. FRONTIERS IN PLANT SCIENCE 2017; 7:2057. [PMID: 28123392 PMCID: PMC5225094 DOI: 10.3389/fpls.2016.02057] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Accepted: 12/23/2016] [Indexed: 05/29/2023]
Abstract
A promising method for characterizing the phenotype of a plant as an interaction between its genotype and its environment is to use refined organ-scale plant growth models that use the observation of architectural traits, such as leaf area, containing a lot of information on the whole history of the functioning of the plant. The Phenoscope, a high-throughput automated platform, allowed the acquisition of zenithal images of Arabidopsis thaliana over twenty one days for 4 different genotypes. A novel image processing algorithm involving both segmentation and tracking of the plant leaves allows to extract areas of the latter. First, all the images in the series are segmented independently using a watershed-based approach. A second step based on ellipsoid-shaped leaves is then applied on the segments found to refine the segmentation. Taking into account all the segments at every time, the whole history of each leaf is reconstructed by choosing recursively through time the most probable segment achieving the best score, computed using some characteristics of the segment such as its orientation, its distance to the plant mass center and its area. These results are compared to manually extracted segments, showing a very good accordance in leaf rank and that they therefore provide low-biased data in large quantity for leaf areas. Such data can therefore be exploited to design an organ-scale plant model adapted from the existing GreenLab model for A. thaliana and subsequently parameterize it. This calibration of the model parameters should pave the way for differentiation between the Arabidopsis genotypes.
Collapse
Affiliation(s)
- Gautier Viaud
- Laboratory MICS, CentraleSupélec, University of Paris-SaclayChâtenay-Malabry, France
| | - Olivier Loudet
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-SaclayVersailles, France
| | - Paul-Henry Cournède
- Laboratory MICS, CentraleSupélec, University of Paris-SaclayChâtenay-Malabry, France
| |
Collapse
|
42
|
Lyu JIL, Baek SH, Jung S, Chu H, Nam HG, Kim J, Lim PO. High-Throughput and Computational Study of Leaf Senescence through a Phenomic Approach. FRONTIERS IN PLANT SCIENCE 2017; 8:250. [PMID: 28280501 PMCID: PMC5322180 DOI: 10.3389/fpls.2017.00250] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Accepted: 02/09/2017] [Indexed: 05/19/2023]
Abstract
Leaf senescence is influenced by its life history, comprising a series of developmental and physiological experiences. Exploration of the biological principles underlying leaf lifespan and senescence requires a schema to trace leaf phenotypes, based on the interaction of genetic and environmental factors. We developed a new approach and concept that will facilitate systemic biological understanding of leaf lifespan and senescence, utilizing the phenome high-throughput investigator (PHI) with a single-leaf-basis phenotyping platform. Our pilot tests showed empirical evidence for the feasibility of PHI for quantitative measurement of leaf senescence responses and improved performance in order to dissect the progression of senescence triggered by different senescence-inducing factors as well as genetic mutations. Such an establishment enables new perspectives to be proposed, which will be challenged for enhancing our fundamental understanding on the complex process of leaf senescence. We further envision that integration of phenomic data with other multi-omics data obtained from transcriptomic, proteomic, and metabolic studies will enable us to address the underlying principles of senescence, passing through different layers of information from molecule to organism.
Collapse
Affiliation(s)
- Jae IL Lyu
- Center for Plant Aging Research, Institute for Basic ScienceDaegu, South Korea
| | - Seung Hee Baek
- Department of New Biology, Daegu Gyeongbuk Institute of Science and TechnologyDaegu, South Korea
| | - Sukjoon Jung
- Department of New Biology, Daegu Gyeongbuk Institute of Science and TechnologyDaegu, South Korea
| | - Hyosub Chu
- Center for Plant Aging Research, Institute for Basic ScienceDaegu, South Korea
| | - Hong Gil Nam
- Center for Plant Aging Research, Institute for Basic ScienceDaegu, South Korea
- Department of New Biology, Daegu Gyeongbuk Institute of Science and TechnologyDaegu, South Korea
| | - Jeongsik Kim
- Center for Plant Aging Research, Institute for Basic ScienceDaegu, South Korea
- *Correspondence: Jeongsik Kim, Pyung Ok Lim,
| | - Pyung Ok Lim
- Department of New Biology, Daegu Gyeongbuk Institute of Science and TechnologyDaegu, South Korea
- *Correspondence: Jeongsik Kim, Pyung Ok Lim,
| |
Collapse
|
43
|
Nakayama H, Sinha NR, Kimura S. How Do Plants and Phytohormones Accomplish Heterophylly, Leaf Phenotypic Plasticity, in Response to Environmental Cues. FRONTIERS IN PLANT SCIENCE 2017; 8:1717. [PMID: 29046687 PMCID: PMC5632738 DOI: 10.3389/fpls.2017.01717] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 09/20/2017] [Indexed: 05/05/2023]
Abstract
Plant species are known to respond to variations in environmental conditions. Many plant species have the ability to alter their leaf morphology in response to such changes. This phenomenon is termed heterophylly and is widespread among land plants. In some cases, heterophylly is thought to be an adaptive mechanism that allows plants to optimally respond to environmental heterogeneity. Recently, many research studies have investigated the occurrence of heterophylly in a wide variety of plants. Several studies have suggested that heterophylly in plants is regulated by phytohormones. Herein, we reviewed the existing knowledge on the relationship and role of phytohormones, especially abscisic acid, ethylene, gibberellins, and auxins (IAA), in regulating heterophylly and attempted to elucidate the mechanisms that regulate heterophylly.
Collapse
Affiliation(s)
- Hokuto Nakayama
- Department of Plant Biology, University of California, Davis, Davis CA, United States
| | - Neelima R. Sinha
- Department of Plant Biology, University of California, Davis, Davis CA, United States
| | - Seisuke Kimura
- Department of Bioresource and Environmental Sciences, Kyoto Sangyo University, Kyoto, Japan
- Center for Ecological Evolutionary Developmental Biology, Kyoto Sangyo University, Kyoto, Japan
- *Correspondence: Seisuke Kimura,
| |
Collapse
|
44
|
|
45
|
San-Miguel A, Kurshan PT, Crane MM, Zhao Y, McGrath PT, Shen K, Lu H. Deep phenotyping unveils hidden traits and genetic relations in subtle mutants. Nat Commun 2016; 7:12990. [PMID: 27876787 PMCID: PMC5122966 DOI: 10.1038/ncomms12990] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Accepted: 08/24/2016] [Indexed: 12/29/2022] Open
Abstract
Discovering mechanistic insights from phenotypic information is critical for the understanding of biological processes. For model organisms, unlike in cell culture, this is currently bottlenecked by the non-quantitative nature and perceptive biases of human observations, and the limited number of reporters that can be simultaneously incorporated in live animals. An additional challenge is that isogenic populations exhibit significant phenotypic heterogeneity. These difficulties limit genetic approaches to many biological questions. To overcome these bottlenecks, we developed tools to extract complex phenotypic traits from images of fluorescently labelled subcellular landmarks, using C. elegans synapses as a test case. By population-wide comparisons, we identified subtle but relevant differences inaccessible to subjective conceptualization. Furthermore, the models generated testable hypotheses of how individual alleles relate to known mechanisms or belong to new pathways. We show that our model not only recapitulates current knowledge in synaptic patterning but also identifies novel alleles overlooked by traditional methods. Experimenter scoring of cellular imaging data can be biased. This study describes an automated and unbiased multidimensional phenotyping method that relies on machine learning and complex feature computation of imaging data, and identifies weak alleles affecting synapse morphology in live C. elegans.
Collapse
Affiliation(s)
- Adriana San-Miguel
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Peri T Kurshan
- Department of Biology, Howard Hughes Medical Institute, Stanford University, Stanford, California 94305, USA
| | - Matthew M Crane
- Interdisciplinary Program in Bioengineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Yuehui Zhao
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Patrick T McGrath
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Kang Shen
- Department of Biology, Howard Hughes Medical Institute, Stanford University, Stanford, California 94305, USA
| | - Hang Lu
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.,Interdisciplinary Program in Bioengineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| |
Collapse
|
46
|
Kuhlgert S, Austic G, Zegarac R, Osei-Bonsu I, Hoh D, Chilvers MI, Roth MG, Bi K, TerAvest D, Weebadde P, Kramer DM. MultispeQ Beta: a tool for large-scale plant phenotyping connected to the open PhotosynQ network. ROYAL SOCIETY OPEN SCIENCE 2016; 3:160592. [PMID: 27853580 PMCID: PMC5099005 DOI: 10.1098/rsos.160592] [Citation(s) in RCA: 125] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2016] [Accepted: 09/26/2016] [Indexed: 05/08/2023]
Abstract
Large-scale high-throughput plant phenotyping (sometimes called phenomics) is becoming increasingly important in plant biology and agriculture and is essential to cutting-edge plant breeding and management approaches needed to meet the food and fuel needs for the next century. Currently, the application of these approaches is severely limited by the availability of appropriate instrumentation and by the ability to communicate experimental protocols, results and analyses. To address these issues, we have developed a low-cost, yet sophisticated open-source scientific instrument designed to enable communities of researchers, plant breeders, educators, farmers and citizen scientists to collect high-quality field data on a large scale. The MultispeQ provides measurements in the field or laboratory of both, environmental conditions (light intensity and quality, temperature, humidity, CO2 levels, time and location) and useful plant phenotypes, including photosynthetic parameters-photosystem II quantum yield (ΦII), non-photochemical exciton quenching (NPQ), photosystem II photoinhibition, light-driven proton translocation and thylakoid proton motive force, regulation of the chloroplast ATP synthase and potentially many others-and leaf chlorophyll and other pigments. Plant phenotype data are transmitted from the MultispeQ to mobile devices, laptops or desktop computers together with key metadata that gets saved to the PhotosynQ platform (https://photosynq.org) and provides a suite of web-based tools for sharing, visualization, filtering, dissemination and analyses. We present validation experiments, comparing MultispeQ results with established platforms, and show that it can be usefully deployed in both laboratory and field settings. We present evidence that MultispeQ can be used by communities of researchers to rapidly measure, store and analyse multiple environmental and plant properties, allowing for deeper understanding of the complex interactions between plants and their environment.
Collapse
Affiliation(s)
- Sebastian Kuhlgert
- MSU-DOE-Plant Research Laboratory, Michigan State University, East Lansing, MI, USA
| | - Greg Austic
- MSU-DOE-Plant Research Laboratory, Michigan State University, East Lansing, MI, USA
| | - Robert Zegarac
- MSU-DOE-Plant Research Laboratory, Michigan State University, East Lansing, MI, USA
| | - Isaac Osei-Bonsu
- MSU-DOE-Plant Research Laboratory, Michigan State University, East Lansing, MI, USA
| | - Donghee Hoh
- MSU-DOE-Plant Research Laboratory, Michigan State University, East Lansing, MI, USA
| | - Martin I. Chilvers
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, USA
- Genetics Graduate Program, Michigan State University, East Lansing, MI, USA
| | - Mitchell G. Roth
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, USA
- Genetics Graduate Program, Michigan State University, East Lansing, MI, USA
| | - Kevin Bi
- MSU-DOE-Plant Research Laboratory, Michigan State University, East Lansing, MI, USA
| | - Dan TerAvest
- MSU-DOE-Plant Research Laboratory, Michigan State University, East Lansing, MI, USA
| | | | - David M. Kramer
- MSU-DOE-Plant Research Laboratory, Michigan State University, East Lansing, MI, USA
- Author for correspondence: David M. Kramer e-mail:
| |
Collapse
|
47
|
Nakhforoosh A, Bodewein T, Fiorani F, Bodner G. Identification of Water Use Strategies at Early Growth Stages in Durum Wheat from Shoot Phenotyping and Physiological Measurements. FRONTIERS IN PLANT SCIENCE 2016; 7:1155. [PMID: 27547208 PMCID: PMC4974299 DOI: 10.3389/fpls.2016.01155] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2016] [Accepted: 07/19/2016] [Indexed: 05/03/2023]
Abstract
Modern imaging technology provides new approaches to plant phenotyping for traits relevant to crop yield and resource efficiency. Our objective was to investigate water use strategies at early growth stages in durum wheat genetic resources using shoot imaging at the ScreenHouse phenotyping facility combined with physiological measurements. Twelve durum landraces from different pedoclimatic backgrounds were compared to three modern check cultivars in a greenhouse pot experiment under well-watered (75% plant available water, PAW) and drought (25% PAW) conditions. Transpiration rate was analyzed for the underlying main morphological (leaf area duration) and physiological (stomata conductance) factors. Combining both morphological and physiological regulation of transpiration, four distinct water use types were identified. Most landraces had high transpiration rates either due to extensive leaf area (area types) or both large leaf areas together with high stomata conductance (spender types). All modern cultivars were distinguished by high stomata conductance with comparatively compact canopies (conductance types). Only few landraces were water saver types with both small canopy and low stomata conductance. During early growth, genotypes with large leaf area had high dry-matter accumulation under both well-watered and drought conditions compared to genotypes with compact stature. However, high stomata conductance was the basis to achieve high dry matter per unit leaf area, indicating high assimilation capacity as a key for productivity in modern cultivars. We conclude that the identified water use strategies based on early growth shoot phenotyping combined with stomata conductance provide an appropriate framework for targeted selection of distinct pre-breeding material adapted to different types of water limited environments.
Collapse
Affiliation(s)
- Alireza Nakhforoosh
- Division of Agronomy, Department of Crop Sciences, University of Natural Resources and Life SciencesVienna, Austria
| | - Thomas Bodewein
- Institute for Bio- and Geosciences-2 Plant Sciences, Forschungszentrum JülichJülich, Germany
| | - Fabio Fiorani
- Institute for Bio- and Geosciences-2 Plant Sciences, Forschungszentrum JülichJülich, Germany
| | - Gernot Bodner
- Division of Agronomy, Department of Crop Sciences, University of Natural Resources and Life SciencesVienna, Austria
| |
Collapse
|
48
|
Foyer CH, Lam HM, Nguyen HT, Siddique KHM, Varshney RK, Colmer TD, Cowling W, Bramley H, Mori TA, Hodgson JM, Cooper JW, Miller AJ, Kunert K, Vorster J, Cullis C, Ozga JA, Wahlqvist ML, Liang Y, Shou H, Shi K, Yu J, Fodor N, Kaiser BN, Wong FL, Valliyodan B, Considine MJ. Neglecting legumes has compromised human health and sustainable food production. NATURE PLANTS 2016; 2:16112. [PMID: 28221372 DOI: 10.1038/nplants.2016.112] [Citation(s) in RCA: 307] [Impact Index Per Article: 38.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
The United Nations declared 2016 as the International Year of Pulses (grain legumes) under the banner 'nutritious seeds for a sustainable future'. A second green revolution is required to ensure food and nutritional security in the face of global climate change. Grain legumes provide an unparalleled solution to this problem because of their inherent capacity for symbiotic atmospheric nitrogen fixation, which provides economically sustainable advantages for farming. In addition, a legume-rich diet has health benefits for humans and livestock alike. However, grain legumes form only a minor part of most current human diets, and legume crops are greatly under-used. Food security and soil fertility could be significantly improved by greater grain legume usage and increased improvement of a range of grain legumes. The current lack of coordinated focus on grain legumes has compromised human health, nutritional security and sustainable food production.
Collapse
Affiliation(s)
- Christine H Foyer
- Centre for Plant Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK
- School of Plant Biology, Faculty of Science, The University of Western Australia, Perth, Western Australia 6009, Australia
| | - Hon-Ming Lam
- School of Life Sciences and Center for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region
| | - Henry T Nguyen
- Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri, Columbia, Missouri 65211, USA
| | - Kadambot H M Siddique
- The UWA Institute of Agriculture, The University of Western Australia, Perth, Western Australia 6009, Australia
| | - Rajeev K Varshney
- The UWA Institute of Agriculture, The University of Western Australia, Perth, Western Australia 6009, Australia
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502 324, Greater Hyderabad, India
| | - Timothy D Colmer
- School of Plant Biology, Faculty of Science, The University of Western Australia, Perth, Western Australia 6009, Australia
- The UWA Institute of Agriculture, The University of Western Australia, Perth, Western Australia 6009, Australia
| | - Wallace Cowling
- The UWA Institute of Agriculture, The University of Western Australia, Perth, Western Australia 6009, Australia
| | - Helen Bramley
- Plant Breeding Institute, Faculty of Agriculture and Environment, The University of Sydney, Narrabri, New South Wales 2390, Australia
| | - Trevor A Mori
- The UWA Institute of Agriculture, The University of Western Australia, Perth, Western Australia 6009, Australia
- School of Medicine and Pharmacology, Royal Perth Hospital Unit, The University of Western Australia, Perth, Western Australia 6000, Australia
| | - Jonathan M Hodgson
- The UWA Institute of Agriculture, The University of Western Australia, Perth, Western Australia 6009, Australia
- School of Medicine and Pharmacology, Royal Perth Hospital Unit, The University of Western Australia, Perth, Western Australia 6000, Australia
| | - James W Cooper
- Centre for Plant Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK
| | - Anthony J Miller
- Department of Metabolic Biology, John Innes Centre, Norwich Research Park, NR4 7UH, UK
| | - Karl Kunert
- Department of Plant Production and Soil Science, Forestry and Agricultural Biotechnology Institute, University of Pretoria, Pretoria 0002, South Africa
| | - Juan Vorster
- Department of Plant Production and Soil Science, Forestry and Agricultural Biotechnology Institute, University of Pretoria, Pretoria 0002, South Africa
| | - Christopher Cullis
- Department of Biology, Case Western Reserve University, Cleveland, Ohio 44106-7080, USA
| | - Jocelyn A Ozga
- Plant BioSystems Division, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, T6G 2P5, Canada
| | - Mark L Wahlqvist
- Fuli Institute of Food Science, Zhejiang University, Hangzhou, 310058, China
- Monash Asia Institute, Monash University, Melbourne, Victoria 3800, Australia
| | - Yan Liang
- College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, China
| | - Huixia Shou
- College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Kai Shi
- College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, China
| | - Jingquan Yu
- College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, China
| | - Nandor Fodor
- Centre for Plant Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK
| | - Brent N Kaiser
- Centre for Carbon Water and Food, Faculty of Agriculture and Environment, The University of Sydney, Brownlow Hill, New South Wales 2570, Australia
| | - Fuk-Ling Wong
- School of Life Sciences and Center for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region
| | - Babu Valliyodan
- Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri, Columbia, Missouri 65211, USA
| | - Michael J Considine
- School of Plant Biology, Faculty of Science, The University of Western Australia, Perth, Western Australia 6009, Australia
- The UWA Institute of Agriculture, The University of Western Australia, Perth, Western Australia 6009, Australia
- The Department of Agriculture and Food, Western Australia, South Perth, Western Australia 6151, Australia
| |
Collapse
|
49
|
Refahi Y, Brunoud G, Farcot E, Jean-Marie A, Pulkkinen M, Vernoux T, Godin C. A stochastic multicellular model identifies biological watermarks from disorders in self-organized patterns of phyllotaxis. eLife 2016; 5. [PMID: 27380805 PMCID: PMC4947393 DOI: 10.7554/elife.14093] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2015] [Accepted: 05/03/2016] [Indexed: 01/03/2023] Open
Abstract
Exploration of developmental mechanisms classically relies on analysis of pattern regularities. Whether disorders induced by biological noise may carry information on building principles of developmental systems is an important debated question. Here, we addressed theoretically this question using phyllotaxis, the geometric arrangement of plant aerial organs, as a model system. Phyllotaxis arises from reiterative organogenesis driven by lateral inhibitions at the shoot apex. Motivated by recurrent observations of disorders in phyllotaxis patterns, we revisited in depth the classical deterministic view of phyllotaxis. We developed a stochastic model of primordia initiation at the shoot apex, integrating locality and stochasticity in the patterning system. This stochastic model recapitulates phyllotactic patterns, both regular and irregular, and makes quantitative predictions on the nature of disorders arising from noise. We further show that disorders in phyllotaxis instruct us on the parameters governing phyllotaxis dynamics, thus that disorders can reveal biological watermarks of developmental systems. DOI:http://dx.doi.org/10.7554/eLife.14093.001 Plants grow throughout their lifetime, forming new flowers and leaves at the tips of their stems through a patterning process called phyllotaxis, which occurs in spirals for a vast number of plant species. The classical view suggests that the positioning of each new leaf or flower bud at the tip of a growing stem is based on a small set of principles. This includes the idea that buds produce inhibitory signals that prevent other buds from forming too close to each other. When computational models of phyllotaxis follow these ‘deterministic’ principles, they are able to recreate the spiral pattern the buds form on a growing stem. In real plants, however, the spiral pattern is not always perfect. The observed disturbances in the pattern are believed to reflect the presence of random fluctuations – regarded as noise – in phyllotaxis. Here, using numerical simulations, Refahi et al. noticed that the patterns of inhibitory signals in a shoot tip pre-determine the locations of several competing sites where buds could form in a robust manner. However, random fluctuations in the way cells perceive these inhibitory signals could greatly disturb the timing of organ formation and affect phyllotaxis patterns. Building on this, Refahi et al. created a new computational model of bud patterning that takes into account some randomness in how cells perceive the inhibitory signals released by existing buds. The model can accurately recreate the classical spiral patterns of buds and also produces occasional disrupted patterns that are similar to those seen in real plants. Unexpectedly, Refahi et al. show that these ‘errors’ reveal key information about how the signals that control phyllotaxis might work. These findings open up new avenues of research into the role of noise in phyllotaxis. The model can be used to predict how altering the activities of genes or varying plant growth conditions might disturb this patterning process. Furthermore, the work highlights how the structure of disturbances in a biological system can shed new light on how the system works. DOI:http://dx.doi.org/10.7554/eLife.14093.002
Collapse
Affiliation(s)
- Yassin Refahi
- Laboratoire de Reproduction de développement des plantes, Lyon, France.,Sainsbury Laboratory, University of Cambridge, Cambridge, United Kingdom
| | - Géraldine Brunoud
- Laboratoire de Reproduction de développement des plantes, Lyon, France
| | - Etienne Farcot
- School of Mathematical Sciences, The University of Nottingham, Nottingham, United Kingdom.,Center for Integrative Plant Biology, The University of Nottingham, Notthingam, United Kingdom
| | - Alain Jean-Marie
- INRIA Project-Team Maestro, INRIA Sophia-Antipolis Méditerranée Research Center, Montpellier, France
| | | | - Teva Vernoux
- Laboratoire de Reproduction de développement des plantes, Lyon, France
| | - Christophe Godin
- INRIA Project-Team Virtual Plants, CIRAD, INRA and INRIA Sophia-Antipolis Méditerranée Research Center, Montpellier, France
| |
Collapse
|
50
|
Haghighattalab A, González Pérez L, Mondal S, Singh D, Schinstock D, Rutkoski J, Ortiz-Monasterio I, Singh RP, Goodin D, Poland J. Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. PLANT METHODS 2016; 12:35. [PMID: 27347001 PMCID: PMC4921008 DOI: 10.1186/s13007-016-0134-6] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Accepted: 06/15/2016] [Indexed: 05/18/2023]
Abstract
BACKGROUND Low cost unmanned aerial systems (UAS) have great potential for rapid proximal measurements of plants in agriculture. In the context of plant breeding and genetics, current approaches for phenotyping a large number of breeding lines under field conditions require substantial investments in time, cost, and labor. For field-based high-throughput phenotyping (HTP), UAS platforms can provide high-resolution measurements for small plot research, while enabling the rapid assessment of tens-of-thousands of field plots. The objective of this study was to complete a baseline assessment of the utility of UAS in assessment field trials as commonly implemented in wheat breeding programs. We developed a semi-automated image-processing pipeline to extract plot level data from UAS imagery. The image dataset was processed using a photogrammetric pipeline based on image orientation and radiometric calibration to produce orthomosaic images. We also examined the relationships between vegetation indices (VIs) extracted from high spatial resolution multispectral imagery collected with two different UAS systems (eBee Ag carrying MultiSpec 4C camera, and IRIS+ quadcopter carrying modified NIR Canon S100) and ground truth spectral data from hand-held spectroradiometer. RESULTS We found good correlation between the VIs obtained from UAS platforms and ground-truth measurements and observed high broad-sense heritability for VIs. We determined radiometric calibration methods developed for satellite imagery significantly improved the precision of VIs from the UAS. We observed VIs extracted from calibrated images of Canon S100 had a significantly higher correlation to the spectroradiometer (r = 0.76) than VIs from the MultiSpec 4C camera (r = 0.64). Their correlation to spectroradiometer readings was as high as or higher than repeated measurements with the spectroradiometer per se. CONCLUSION The approaches described here for UAS imaging and extraction of proximal sensing data enable collection of HTP measurements on the scale and with the precision needed for powerful selection tools in plant breeding. Low-cost UAS platforms have great potential for use as a selection tool in plant breeding programs. In the scope of tools development, the pipeline developed in this study can be effectively employed for other UAS and also other crops planted in breeding nurseries.
Collapse
Affiliation(s)
| | - Lorena González Pérez
- />International Maize and Wheat Improvement Center (CIMMYT), Int. Apdo., Postal 6-641, 06600 Mexico, D.F. Mexico
| | - Suchismita Mondal
- />International Maize and Wheat Improvement Center (CIMMYT), Int. Apdo., Postal 6-641, 06600 Mexico, D.F. Mexico
| | - Daljit Singh
- />Interdepartmental Genetics Program, Kansas State University, Manhattan, KS 66506 USA
| | - Dale Schinstock
- />Department of Mechanical and Nuclear Engineering, Kansas State University, Manhattan, KS 66506 USA
| | - Jessica Rutkoski
- />International Maize and Wheat Improvement Center (CIMMYT), Int. Apdo., Postal 6-641, 06600 Mexico, D.F. Mexico
- />International Programs of the College of Agriculture and Life Science, Cornell University, Ithaca, NY 14853 USA
| | - Ivan Ortiz-Monasterio
- />International Maize and Wheat Improvement Center (CIMMYT), Int. Apdo., Postal 6-641, 06600 Mexico, D.F. Mexico
| | - Ravi Prakash Singh
- />International Maize and Wheat Improvement Center (CIMMYT), Int. Apdo., Postal 6-641, 06600 Mexico, D.F. Mexico
| | - Douglas Goodin
- />Department of Geography, Kansas State University, Manhattan, KS 66506 USA
| | - Jesse Poland
- />Wheat Genetics Resource Center, Department of Plant Pathology and Department of Agronomy, Kansas State University, Manhattan, KS 66506 USA
| |
Collapse
|