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Oskam L, Snoek BL, Pantazopoulou CK, van Veen H, Matton SEA, Dijkhuizen R, Pierik R. A low-cost open-source imaging platform reveals spatiotemporal insight into leaf elongation and movement. PLANT PHYSIOLOGY 2024; 195:1866-1879. [PMID: 38401532 PMCID: PMC11213255 DOI: 10.1093/plphys/kiae097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/08/2024] [Accepted: 01/25/2024] [Indexed: 02/26/2024]
Abstract
Plant organs move throughout the diurnal cycle, changing leaf and petiole positions to balance light capture, leaf temperature, and water loss under dynamic environmental conditions. Upward movement of the petiole, called hyponasty, is one of several traits of the shade avoidance syndrome (SAS). SAS traits are elicited upon perception of vegetation shade signals such as far-red light (FR) and improve light capture in dense vegetation. Monitoring plant movement at a high temporal resolution allows studying functionality and molecular regulation of hyponasty. However, high temporal resolution imaging solutions are often very expensive, making this unavailable to many researchers. Here, we present a modular and low-cost imaging setup, based on small Raspberry Pi computers that can track leaf movements and elongation growth with high temporal resolution. We also developed an open-source, semiautomated image analysis pipeline. Using this setup, we followed responses to FR enrichment, light intensity, and their interactions. Tracking both elongation and the angle of the petiole, lamina, and entire leaf in Arabidopsis (Arabidopsis thaliana) revealed insight into R:FR sensitivities of leaf growth and movement dynamics and the interactions of R:FR with background light intensity. The detailed imaging options of this system allowed us to identify spatially separate bending points for petiole and lamina positioning of the leaf.
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Affiliation(s)
- Lisa Oskam
- Plant-Environment Signaling, Department of Biology, Utrecht University, Utrecht 3584 CH, The Netherlands
| | - Basten L Snoek
- Theoretical Biology and Bioinformatics, Department of Biology, Utrecht University, Utrecht 3584 CH, The Netherlands
| | - Chrysoula K Pantazopoulou
- Plant-Environment Signaling, Department of Biology, Utrecht University, Utrecht 3584 CH, The Netherlands
| | - Hans van Veen
- Plant-Environment Signaling, Department of Biology, Utrecht University, Utrecht 3584 CH, The Netherlands
| | - Sanne E A Matton
- Laboratory of Molecular Biology, Wageningen University and Research, Wageningen 6700 AA, The Netherlands
| | - Rens Dijkhuizen
- Plant-Environment Signaling, Department of Biology, Utrecht University, Utrecht 3584 CH, The Netherlands
| | - Ronald Pierik
- Plant-Environment Signaling, Department of Biology, Utrecht University, Utrecht 3584 CH, The Netherlands
- Laboratory of Molecular Biology, Wageningen University and Research, Wageningen 6700 AA, The Netherlands
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Ohlsson JA, Leong JX, Elander PH, Ballhaus F, Holla S, Dauphinee AN, Johansson J, Lommel M, Hofmann G, Betnér S, Sandgren M, Schumacher K, Bozhkov PV, Minina EA. SPIRO - the automated Petri plate imaging platform designed by biologists, for biologists. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024; 118:584-600. [PMID: 38141174 DOI: 10.1111/tpj.16587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/04/2023] [Indexed: 12/25/2023]
Abstract
Phenotyping of model organisms grown on Petri plates is often carried out manually, despite the procedures being time-consuming and laborious. The main reason for this is the limited availability of automated phenotyping facilities, whereas constructing a custom automated solution can be a daunting task for biologists. Here, we describe SPIRO, the Smart Plate Imaging Robot, an automated platform that acquires time-lapse photographs of up to four vertically oriented Petri plates in a single experiment, corresponding to 192 seedlings for a typical root growth assay and up to 2500 seeds for a germination assay. SPIRO is catered specifically to biologists' needs, requiring no engineering or programming expertise for assembly and operation. Its small footprint is optimized for standard incubators, the inbuilt green LED enables imaging under dark conditions, and remote control provides access to the data without interfering with sample growth. SPIRO's excellent image quality is suitable for automated image processing, which we demonstrate on the example of seed germination and root growth assays. Furthermore, the robot can be easily customized for specific uses, as all information about SPIRO is released under open-source licenses. Importantly, uninterrupted imaging allows considerably more precise assessment of seed germination parameters and root growth rates compared with manual assays. Moreover, SPIRO enables previously technically challenging assays such as phenotyping in the dark. We illustrate the benefits of SPIRO in proof-of-concept experiments which yielded a novel insight on the interplay between autophagy, nitrogen sensing, and photoblastic response.
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Affiliation(s)
- Jonas A Ohlsson
- Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala, SE-750 07, Sweden
| | - Jia Xuan Leong
- Department of Algal Development and Evolution, Max Planck Institute for Biology Tübingen, Tübingen, 72076, Germany
- Centre for Organismal Studies (COS), Heidelberg University, Im Neuenheimer Feld 230, Heidelberg, 69120, Germany
- Center for Plant Molecular Biology (ZMBP), University of Tübingen, Auf der Morgenstelle 32, Tübingen, D-72076, Germany
| | - Pernilla H Elander
- Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala, SE-750 07, Sweden
| | - Florentine Ballhaus
- Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala, SE-750 07, Sweden
| | - Sanjana Holla
- Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala, SE-750 07, Sweden
| | - Adrian N Dauphinee
- Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala, SE-750 07, Sweden
| | | | - Mark Lommel
- Centre for Organismal Studies (COS), Heidelberg University, Im Neuenheimer Feld 230, Heidelberg, 69120, Germany
- Department of Microbiology, Saarland University, Campus A1.5, Saarbrücken, 66123, Germany
| | - Gero Hofmann
- Centre for Organismal Studies (COS), Heidelberg University, Im Neuenheimer Feld 230, Heidelberg, 69120, Germany
| | - Staffan Betnér
- Northern Registry Centre, Department of Public Health and Clinical Medicine, Umeå University, Umeå, 90187, Sweden
| | - Mats Sandgren
- Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala, SE-750 07, Sweden
| | - Karin Schumacher
- Centre for Organismal Studies (COS), Heidelberg University, Im Neuenheimer Feld 230, Heidelberg, 69120, Germany
| | - Peter V Bozhkov
- Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala, SE-750 07, Sweden
| | - Elena A Minina
- Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala, SE-750 07, Sweden
- Centre for Organismal Studies (COS), Heidelberg University, Im Neuenheimer Feld 230, Heidelberg, 69120, Germany
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3
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Parthiban S, Vijeesh T, Gayathri T, Shanmugaraj B, Sharma A, Sathishkumar R. Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals. FRONTIERS IN PLANT SCIENCE 2023; 14:1252166. [PMID: 38034587 PMCID: PMC10684705 DOI: 10.3389/fpls.2023.1252166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/17/2023] [Indexed: 12/02/2023]
Abstract
Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.
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Affiliation(s)
- Subramanian Parthiban
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Thandarvalli Vijeesh
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Thashanamoorthi Gayathri
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Balamurugan Shanmugaraj
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Ashutosh Sharma
- Tecnologico de Monterrey, School of Engineering and Sciences, Centre of Bioengineering, Queretaro, Mexico
| | - Ramalingam Sathishkumar
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
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Rahmati Ishka M, Julkowska M. Tapping into the plasticity of plant architecture for increased stress resilience. F1000Res 2023; 12:1257. [PMID: 38434638 PMCID: PMC10905174 DOI: 10.12688/f1000research.140649.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/24/2023] [Indexed: 03/05/2024] Open
Abstract
Plant architecture develops post-embryonically and emerges from a dialogue between the developmental signals and environmental cues. Length and branching of the vegetative and reproductive tissues were the focus of improvement of plant performance from the early days of plant breeding. Current breeding priorities are changing, as we need to prioritize plant productivity under increasingly challenging environmental conditions. While it has been widely recognized that plant architecture changes in response to the environment, its contribution to plant productivity in the changing climate remains to be fully explored. This review will summarize prior discoveries of genetic control of plant architecture traits and their effect on plant performance under environmental stress. We review new tools in phenotyping that will guide future discoveries of genes contributing to plant architecture, its plasticity, and its contributions to stress resilience. Subsequently, we provide a perspective into how integrating the study of new species, modern phenotyping techniques, and modeling can lead to discovering new genetic targets underlying the plasticity of plant architecture and stress resilience. Altogether, this review provides a new perspective on the plasticity of plant architecture and how it can be harnessed for increased performance under environmental stress.
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5
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Huang L, Zhang Y, Guo J, Peng Q, Zhou Z, Duan X, Tanveer M, Guo Y. High-throughput root phenotyping of crop cultivars tolerant to low N in waterlogged soils. FRONTIERS IN PLANT SCIENCE 2023; 14:1271539. [PMID: 37780519 PMCID: PMC10533935 DOI: 10.3389/fpls.2023.1271539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 08/29/2023] [Indexed: 10/03/2023]
Affiliation(s)
- Liping Huang
- International Research Center for Environmental Membrane Biology, College of Food Science and Engineering, Foshan University, Foshan, China
- Foshan ZhiBao Ecological Technology Co. Ltd., Foshan, China
| | - Yujing Zhang
- International Research Center for Environmental Membrane Biology, College of Food Science and Engineering, Foshan University, Foshan, China
- Foshan ZhiBao Ecological Technology Co. Ltd., Foshan, China
| | - Jieru Guo
- International Research Center for Environmental Membrane Biology, College of Food Science and Engineering, Foshan University, Foshan, China
- Foshan ZhiBao Ecological Technology Co. Ltd., Foshan, China
| | - Qianlan Peng
- International Research Center for Environmental Membrane Biology, College of Food Science and Engineering, Foshan University, Foshan, China
| | - Zhaoyang Zhou
- International Research Center for Environmental Membrane Biology, College of Food Science and Engineering, Foshan University, Foshan, China
| | - Xiaosong Duan
- International Research Center for Environmental Membrane Biology, College of Food Science and Engineering, Foshan University, Foshan, China
| | - Mohsin Tanveer
- Tasmanian Institute of Agriculture, University of Tasmania, Hobart, TAS, Australia
| | - Yongjun Guo
- International Research Center for Environmental Membrane Biology, College of Food Science and Engineering, Foshan University, Foshan, China
- Foshan ZhiBao Ecological Technology Co. Ltd., Foshan, China
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Panda K, Mohanasundaram B, Gutierrez J, McLain L, Castillo SE, Sheng H, Casto A, Gratacós G, Chakrabarti A, Fahlgren N, Pandey S, Gehan MA, Slotkin RK. The plant response to high CO 2 levels is heritable and orchestrated by DNA methylation. THE NEW PHYTOLOGIST 2023; 238:2427-2439. [PMID: 36918471 DOI: 10.1111/nph.18876] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 03/07/2023] [Indexed: 05/19/2023]
Abstract
Plant responses to abiotic environmental challenges are known to have lasting effects on the plant beyond the initial stress exposure. Some of these lasting effects are transgenerational, affecting the next generation. The plant response to elevated carbon dioxide (CO2 ) levels has been well studied. However, these investigations are typically limited to plants grown for a single generation in a high CO2 environment while transgenerational studies are rare. We aimed to determine transgenerational growth responses in plants after exposure to high CO2 by investigating the direct progeny when returned to baseline CO2 levels. We found that both the flowering plant Arabidopsis thaliana and seedless nonvascular plant Physcomitrium patens continue to display accelerated growth rates in the progeny of plants exposed to high CO2 . We used the model species Arabidopsis to dissect the molecular mechanism and found that DNA methylation pathways are necessary for heritability of this growth response. More specifically, the pathway of RNA-directed DNA methylation is required to initiate methylation and the proteins CMT2 and CMT3 are needed for the transgenerational propagation of this DNA methylation to the progeny plants. Together, these two DNA methylation pathways establish and then maintain a cellular memory to high CO2 exposure.
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Affiliation(s)
- Kaushik Panda
- Donald Danforth Plant Science Center, St Louis, MO, 63132, USA
| | | | - Jorge Gutierrez
- Donald Danforth Plant Science Center, St Louis, MO, 63132, USA
| | - Lauren McLain
- Donald Danforth Plant Science Center, St Louis, MO, 63132, USA
| | | | - Hudanyun Sheng
- Donald Danforth Plant Science Center, St Louis, MO, 63132, USA
| | - Anna Casto
- Donald Danforth Plant Science Center, St Louis, MO, 63132, USA
| | - Gustavo Gratacós
- Department of Computer Science & Engineering, Washington University in St Louis, St Louis, MO, 63130, USA
| | - Ayan Chakrabarti
- Department of Computer Science & Engineering, Washington University in St Louis, St Louis, MO, 63130, USA
| | - Noah Fahlgren
- Donald Danforth Plant Science Center, St Louis, MO, 63132, USA
| | - Sona Pandey
- Donald Danforth Plant Science Center, St Louis, MO, 63132, USA
| | - Malia A Gehan
- Donald Danforth Plant Science Center, St Louis, MO, 63132, USA
| | - R Keith Slotkin
- Donald Danforth Plant Science Center, St Louis, MO, 63132, USA
- Division of Biological Sciences, University of Missouri, MO, 65211, Columbia, USA
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7
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Zhu Z, Esche F, Babben S, Trenner J, Serfling A, Pillen K, Maurer A, Quint M. An exotic allele of barley EARLY FLOWERING 3 contributes to developmental plasticity at elevated temperatures. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:2912-2931. [PMID: 36449391 DOI: 10.1093/jxb/erac470] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 11/28/2022] [Indexed: 06/06/2023]
Abstract
Increase in ambient temperatures caused by climate change affects various morphological and developmental traits of plants, threatening crop yield stability. In the model plant Arabidopsis thaliana, EARLY FLOWERING 3 (ELF3) plays prominent roles in temperature sensing and thermomorphogenesis signal transduction. However, how crop species respond to elevated temperatures is poorly understood. Here, we show that the barley ortholog of AtELF3 interacts with high temperature to control growth and development. We used heterogeneous inbred family (HIF) pairs generated from a segregating mapping population and systematically studied the role of exotic ELF3 variants in barley temperature responses. An exotic ELF3 allele of Syrian origin promoted elongation growth in barley at elevated temperatures, whereas plant area and estimated biomass were drastically reduced, resulting in an open canopy architecture. The same allele accelerated inflorescence development at high temperature, which correlated with early transcriptional induction of MADS-box floral identity genes BM3 and BM8. Consequently, barley plants carrying the exotic ELF3 allele displayed stable total grain number at elevated temperatures. Our findings therefore demonstrate that exotic ELF3 variants can contribute to phenotypic and developmental acclimation to elevated temperatures, providing a stimulus for breeding of climate-resilient crops.
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Affiliation(s)
- Zihao Zhu
- Institute of Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, Betty-Heimann-Str. 5, D-06120, Halle (Saale), Germany
| | - Finn Esche
- Institute of Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, Betty-Heimann-Str. 5, D-06120, Halle (Saale), Germany
| | - Steve Babben
- Institute of Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, Betty-Heimann-Str. 5, D-06120, Halle (Saale), Germany
| | - Jana Trenner
- Institute of Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, Betty-Heimann-Str. 5, D-06120, Halle (Saale), Germany
| | - Albrecht Serfling
- Institute for Resistance Research and Stress Tolerance, Julius Kuehn-Institute, Erwin-Baur-Str. 27, D-06484, Quedlinburg, Germany
| | - Klaus Pillen
- Chair of Plant Breeding, Institute of Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, Betty-Heimann-Str. 3, D-06120, Halle (Saale), Germany
| | - Andreas Maurer
- Chair of Plant Breeding, Institute of Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, Betty-Heimann-Str. 3, D-06120, Halle (Saale), Germany
| | - Marcel Quint
- Institute of Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, Betty-Heimann-Str. 5, D-06120, Halle (Saale), Germany
- German Centre for Integrative Biodiversity Research, Halle-Jena-Leipzig, Puschstrasse 4, D-04103, Leipzig, Germany
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Walsh JJ, Mangina E, Negrão S. Advancements in Imaging Sensors and AI for Plant Stress Detection: A Systematic Literature Review. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 6:0153. [PMID: 38435466 PMCID: PMC10905704 DOI: 10.34133/plantphenomics.0153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 01/27/2024] [Indexed: 03/05/2024]
Abstract
Integrating imaging sensors and artificial intelligence (AI) have contributed to detecting plant stress symptoms, yet data analysis remains a key challenge. Data challenges include standardized data collection, analysis protocols, selection of imaging sensors and AI algorithms, and finally, data sharing. Here, we present a systematic literature review (SLR) scrutinizing plant imaging and AI for identifying stress responses. We performed a scoping review using specific keywords, namely abiotic and biotic stress, machine learning, plant imaging and deep learning. Next, we used programmable bots to retrieve relevant papers published since 2006. In total, 2,704 papers from 4 databases (Springer, ScienceDirect, PubMed, and Web of Science) were found, accomplished by using a second layer of keywords (e.g., hyperspectral imaging and supervised learning). To bypass the limitations of search engines, we selected OneSearch to unify keywords. We carefully reviewed 262 studies, summarizing key trends in AI algorithms and imaging sensors. We demonstrated that the increased availability of open-source imaging repositories such as PlantVillage or Kaggle has strongly contributed to a widespread shift to deep learning, requiring large datasets to train in stress symptom interpretation. Our review presents current trends in AI-applied algorithms to develop effective methods for plant stress detection using image-based phenotyping. For example, regression algorithms have seen substantial use since 2021. Ultimately, we offer an overview of the course ahead for AI and imaging technologies to predict stress responses. Altogether, this SLR highlights the potential of AI imaging in both biotic and abiotic stress detection to overcome challenges in plant data analysis.
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Affiliation(s)
- Jason John Walsh
- School of Biology & Environmental Science,
University College Dublin, Belfield, Dublin, Ireland
- School of Computer Science,
University College Dublin, Belfield, Dublin, Ireland
| | - Eleni Mangina
- School of Computer Science,
University College Dublin, Belfield, Dublin, Ireland
| | - Sonia Negrão
- School of Biology & Environmental Science,
University College Dublin, Belfield, Dublin, Ireland
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9
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Stamford JD, Vialet-Chabrand S, Cameron I, Lawson T. Development of an accurate low cost NDVI imaging system for assessing plant health. PLANT METHODS 2023; 19:9. [PMID: 36717879 PMCID: PMC9887843 DOI: 10.1186/s13007-023-00981-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 01/11/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Spectral imaging is a key method for high throughput phenotyping that can be related to a large variety of biological parameters. The Normalised Difference Vegetation Index (NDVI), uses specific wavelengths to compare crop health and performance. Increasing the accessibility of spectral imaging systems through the development of small, low cost, and easy to use platforms will generalise its use for precision agriculture. We describe a method for using a dual camera system connected to a Raspberry Pi to produce NDVI imagery, referred to as NDVIpi. Spectral reference targets were used to calibrate images into values of reflectance, that are then used to calculated NDVI with improved accuracy compared with systems that use single references/standards. RESULTS NDVIpi imagery showed strong performance against standard spectrometry, as an accurate measurement of leaf NDVI. The NDVIpi was also compared to a relatively more expensive commercial camera (Micasense RedEdge), with both cameras having a comparable performance in measuring NDVI. There were differences between the NDVI values of the NDVIpi and the RedEdge, which could be attributed to the measurement of different wavelengths for use in the NDVI calculation by each camera. Subsequently, the wavelengths used by the NDVIpi show greater sensitivity to changes in chlorophyll content than the RedEdge. CONCLUSION We present a methodology for a Raspberry Pi based NDVI imaging system that utilizes low cost, off-the-shelf components, and a robust multi-reference calibration protocols that provides accurate NDVI measurements. When compared with a commercial system, comparable NDVI values were obtained, despite the fact that our system was a fraction of the cost. Our results also highlight the importance of the choice of red wavelengths in the calculation of NDVI, which resulted in differences in sensitivity between camera systems.
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Affiliation(s)
- John D Stamford
- School of Life Sciences, University of Essex, Colchester, CO4 3SQ, Essex, UK
| | - Silvere Vialet-Chabrand
- School of Life Sciences, University of Essex, Colchester, CO4 3SQ, Essex, UK
- Horticulture and Product Physiology, Department of Plant Sciences, Wageningen University & Research, 16, 6700 AA, Wageningen, The Netherlands
| | - Iain Cameron
- Environment Systems, 9 Cefn Llan Science Park, Aberystwyth, SY23 3AH, Ceredigion, UK
| | - Tracy Lawson
- School of Life Sciences, University of Essex, Colchester, CO4 3SQ, Essex, UK.
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Ginzburg DN, Rhee SY. Evaluating Plant Drought Resistance with a Raspberry Pi and Time-lapse Photography. Bio Protoc 2023; 13:e4593. [PMID: 36789161 PMCID: PMC9901466 DOI: 10.21769/bioprotoc.4593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 11/07/2022] [Accepted: 12/25/2022] [Indexed: 01/20/2023] Open
Abstract
Identifying genetic variations or treatments that confer greater resistance to drought is paramount to ensuring sustainable crop productivity. Accurate and reproducible measurement of drought stress symptoms can be achieved via automated, image-based phenotyping. Many phenotyping platforms are either cost-prohibitive, require specific technical expertise, or are simply more complex than necessary to effectively evaluate drought resistance. Certain mutations, allelic variations, or treatments result in plants that constitutively use less water. To accurately identify genetic differences or treatments that confer a drought phenotype, plants from all experimental groups must be subjected to equal levels of drought stress. This can be easily achieved by growing and imaging plants that are grown in the same pot. Here, we provide a detailed protocol to configure a Raspberry Pi computer and camera module to image seedlings of multiple genotypes growing in shared pots and to transfer images and metadata via the cloud for downstream analyses. Also detailed is a method to calculate percent soil water content of pots while being imaged to allow for comparison of stress symptoms with water availability. This protocol was recently used to uncouple differential water usage from drought resistance in a dwarf Arabidopsis thaliana mutant chiquita1-1/cost1 compared to the wild-type control. It is cost effective, suitable for any plant species, customizable to various biological questions, and requires no prior experience with electronics or basic software programming.
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Affiliation(s)
- Daniel N. Ginzburg
- Department of Plant Biology, Carnegie Institution for Science, Stanford, United States
| | - Seung Y. Rhee
- Department of Plant Biology, Carnegie Institution for Science, Stanford, United States
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11
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Pierz LD, Heslinga DR, Buell CR, Haus MJ. An image-based technique for automated root disease severity assessment using PlantCV. APPLICATIONS IN PLANT SCIENCES 2023; 11:e11507. [PMID: 36818784 PMCID: PMC9934521 DOI: 10.1002/aps3.11507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 08/31/2022] [Accepted: 09/23/2022] [Indexed: 06/18/2023]
Abstract
PREMISE Plant disease severity assessments are used to quantify plant-pathogen interactions and identify disease-resistant lines. One common method for disease assessment involves scoring tissue manually using a semi-quantitative scale. Automating assessments would provide fast, unbiased, and quantitative measurements of root disease severity, allowing for improved consistency within and across large data sets. However, using traditional Root System Markup Language (RSML) software in the study of root responses to pathogens presents additional challenges; these include the removal of necrotic tissue during the thresholding process, which results in inaccurate image analysis. METHODS Using PlantCV, we developed a Python-based pipeline, herein called RootDS, with two main objectives: (1) improving disease severity phenotyping and (2) generating binary images as inputs for RSML software. We tested the pipeline in common bean inoculated with Fusarium root rot. RESULTS Quantitative disease scores and root area generated by this pipeline had a strong correlation with manually curated values (R 2 = 0.92 and 0.90, respectively) and provided a broader capture of variation than manual disease scores. Compared to traditional manual thresholding, images generated using our pipeline did not affect RSML output. DISCUSSION Overall, the RootDS pipeline provides greater functionality in disease score data sets and provides an alternative method for generating image sets for use in available RSML software.
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Affiliation(s)
- Logan D. Pierz
- Department of Plant BiologyMichigan State UniversityEast LansingMichigan48824USA
- Plant Resilience InstituteMichigan State UniversityEast LansingMichigan48824USA
| | - Dilyn R. Heslinga
- Department of HorticultureMichigan State UniversityEast LansingMichigan48824USA
| | - C. Robin Buell
- Department of Plant BiologyMichigan State UniversityEast LansingMichigan48824USA
- Plant Resilience InstituteMichigan State UniversityEast LansingMichigan48824USA
- Department of Crop and Soil SciencesUniversity of GeorgiaAthensGeorgia30602USA
| | - Miranda J. Haus
- Department of Plant BiologyMichigan State UniversityEast LansingMichigan48824USA
- Plant Resilience InstituteMichigan State UniversityEast LansingMichigan48824USA
- Department of HorticultureMichigan State UniversityEast LansingMichigan48824USA
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12
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Thrash T, Lee H, Baker RL. A low-cost high-throughput phenotyping system for automatically quantifying foliar area and greenness. APPLICATIONS IN PLANT SCIENCES 2022; 10:e11502. [PMID: 36518945 PMCID: PMC9742822 DOI: 10.1002/aps3.11502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 06/01/2022] [Indexed: 06/17/2023]
Abstract
PREMISE With modern advances in genetic sequencing technology, plant phenotyping has become a substantial bottleneck in crop improvement programs. Traditionally, researchers have manually measured phenotypic traits to help determine genotype-phenotype relationships, but manual measurements can be time consuming and expensive. Recently, automated phenotyping systems have increased the spatial and temporal density of measurements, but most of these systems are extremely expensive and require specialized expertise. In the present paper, we develop and validate a low-cost, scalable, high-throughput phenotyping (HTP) system for automating the measurement of foliar area and greenness. METHODS During a greenhouse experiment on the effects of abiotic stress on Brassica rapa, we collected images of hundreds of plants every hour for over a month with a system that cost approximately US$1000. RESULTS In comparison with manually acquired images, this HTP system was able to produce similar estimates of foliar area and greenness, developmental trends, and treatment effects. Foliar area was correlated between the two image sets, but greenness was not. DISCUSSION These findings highlight the potential of HTP systems built from low-cost hardware and freely available software. Future work can use this system to investigate genotype-environment interactions and the genetic loci underlying morphological changes resulting from abiotic stress.
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Affiliation(s)
- Tyler Thrash
- Department of BiologyMiami University212 Pearson Hall, OxfordOhio45056USA
- Graduate Program in BiologySaint Louis University301 Macelwane HallSt. LouisMissouri63103USA
| | - Hansol Lee
- Department of BiologyMiami University212 Pearson Hall, OxfordOhio45056USA
- Graduate Program in Ecology, Evolution, and Environmental BiologyMiami University212 Pearson HallOxfordOhio45056USA
| | - Robert L. Baker
- Department of BiologyMiami University212 Pearson Hall, OxfordOhio45056USA
- Inventory and Monitoring DivisionNational Park Service1201 Oakridge Drive, Suite 150Fort CollinsColorado80525USA
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13
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Low-cost modular systems for agarose gel documentation. Biotechniques 2022; 73:227-232. [DOI: 10.2144/btn-2022-0060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
While conducting recombinant DNA technology procedures, such as DNA purification, agarose gel electrophoresis is often used for identification, characterization and quantification of DNA. The collection of data for experiments involving such techniques frequently involves capturing images using systems that are expensive and/or proprietary, such that they are not user-serviceable when they malfunction or become antiquated. In response to these limitations, work was done to replace the authors' existing aging Mac OS-based modular system with open-source software and generic hardware. Several versions of a modular imaging system that can be adjusted to fit nearly all use cases are described. The systems developed can accommodate diverse uses from research laboratories to educational environments where commercial systems could be unaffordable.
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14
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Langan P, Bernád V, Walsh J, Henchy J, Khodaeiaminjan M, Mangina E, Negrão S. Phenotyping for waterlogging tolerance in crops: current trends and future prospects. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:5149-5169. [PMID: 35642593 PMCID: PMC9440438 DOI: 10.1093/jxb/erac243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 05/30/2022] [Indexed: 06/15/2023]
Abstract
Yield losses to waterlogging are expected to become an increasingly costly and frequent issue in some regions of the world. Despite the extensive work that has been carried out examining the molecular and physiological responses to waterlogging, phenotyping for waterlogging tolerance has proven difficult. This difficulty is largely due to the high variability of waterlogging conditions such as duration, temperature, soil type, and growth stage of the crop. In this review, we highlight use of phenotyping to assess and improve waterlogging tolerance in temperate crop species. We start by outlining the experimental methods that have been utilized to impose waterlogging stress, ranging from highly controlled conditions of hydroponic systems to large-scale screenings in the field. We also describe the phenotyping traits used to assess tolerance ranging from survival rates and visual scoring to precise photosynthetic measurements. Finally, we present an overview of the challenges faced in attempting to improve waterlogging tolerance, the trade-offs associated with phenotyping in controlled conditions, limitations of classic phenotyping methods, and future trends using plant-imaging methods. If effectively utilized to increase crop resilience to changing climates, crop phenotyping has a major role to play in global food security.
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Affiliation(s)
- Patrick Langan
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Villő Bernád
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Jason Walsh
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
- School of Computer Science and UCD Energy Institute, University College Dublin, Dublin, Ireland
| | - Joey Henchy
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | | | - Eleni Mangina
- School of Computer Science and UCD Energy Institute, University College Dublin, Dublin, Ireland
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15
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Al-Tamimi N, Langan P, Bernád V, Walsh J, Mangina E, Negrão S. Capturing crop adaptation to abiotic stress using image-based technologies. Open Biol 2022; 12:210353. [PMID: 35728624 PMCID: PMC9213114 DOI: 10.1098/rsob.210353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Farmers and breeders aim to improve crop responses to abiotic stresses and secure yield under adverse environmental conditions. To achieve this goal and select the most resilient genotypes, plant breeders and researchers rely on phenotyping to quantify crop responses to abiotic stress. Recent advances in imaging technologies allow researchers to collect physiological data non-destructively and throughout time, making it possible to dissect complex plant responses into quantifiable traits. The use of image-based technologies enables the quantification of crop responses to stress in both controlled environmental conditions and field trials. This paper summarizes phenotyping imaging technologies (RGB, multispectral and hyperspectral sensors, among others) that have been used to assess different abiotic stresses including salinity, drought and nitrogen deficiency, while discussing their advantages and drawbacks. We present a detailed review of traits involved in abiotic tolerance, which have been quantified by a range of imaging sensors under high-throughput phenotyping facilities or using unmanned aerial vehicles in the field. We also provide an up-to-date compilation of spectral tolerance indices and discuss the progress and challenges in machine learning, including supervised and unsupervised models as well as deep learning.
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Affiliation(s)
- Nadia Al-Tamimi
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Patrick Langan
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Villő Bernád
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Jason Walsh
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland,School of Computer Science and UCD Energy Institute, University College Dublin, Dublin, Ireland
| | - Eleni Mangina
- School of Computer Science and UCD Energy Institute, University College Dublin, Dublin, Ireland
| | - Sónia Negrão
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
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16
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Oellermann M, Jolles JW, Ortiz D, Seabra R, Wenzel T, Wilson H, Tanner RL. Open Hardware in Science: The Benefits of Open Electronics. Integr Comp Biol 2022; 62:1061-1075. [PMID: 35595471 PMCID: PMC9617215 DOI: 10.1093/icb/icac043] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/30/2022] [Accepted: 05/13/2022] [Indexed: 11/30/2022] Open
Abstract
Openly shared low-cost electronic hardware applications, known as open electronics, have sparked a new open-source movement, with much untapped potential to advance scientific research. Initially designed to appeal to electronic hobbyists, open electronics have formed a global “maker” community and are increasingly used in science and industry. In this perspective article, we review the current costs and benefits of open electronics for use in scientific research ranging from the experimental to the theoretical sciences. We discuss how user-made electronic applications can help (I) individual researchers, by increasing the customization, efficiency, and scalability of experiments, while improving data quantity and quality; (II) scientific institutions, by improving access to customizable high-end technologies, sustainability, visibility, and interdisciplinary collaboration potential; and (III) the scientific community, by improving transparency and reproducibility, helping decouple research capacity from funding, increasing innovation, and improving collaboration potential among researchers and the public. We further discuss how current barriers like poor awareness, knowledge access, and time investments can be resolved by increased documentation and collaboration, and provide guidelines for academics to enter this emerging field. We highlight that open electronics are a promising and powerful tool to help scientific research to become more innovative and reproducible and offer a key practical solution to improve democratic access to science.
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Affiliation(s)
- Michael Oellermann
- Technical University of Munich, TUM School of Life Sciences, Aquatic Systems Biology Unit, Mühlenweg 22, D-85354 Freising, Germany.,University of Tasmania, Institute for Marine and Antarctic Studies, Fisheries and Aquaculture Centre, Private Bag 49, Hobart, TAS 7001, Australia
| | - Jolle W Jolles
- Centre for Research on Ecology and Forestry Applications (CREAF), Campus UAB, Edifici C. 08193 Bellaterra Barcelona, Spain
| | - Diego Ortiz
- INTA, Instituto Nacional de Tecnología Agropecuaria, Estación Experimental Manfredi, Ruta 9 Km 636, 5988, Manfredi, Córdoba, Argentina
| | - Rui Seabra
- CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Campus Agrário de Vairão, 4485-661, Vairão, Portugal
| | - Tobias Wenzel
- Pontificia Universidad Católica de Chile, Institute for Biological and Medical Engineering, Schools of Engineering (IIBM), Medicine and Biological Sciences, Santiago, Chile
| | - Hannah Wilson
- Utah State University, College of Science, Biology Department, 5305 Old Main Hill, Logan, UT, 84321, USA
| | - Richelle L Tanner
- Chapman University, Environmental Science and Policy Program, 1 University Drive, Orange, CA 92866, USA
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17
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Knapp A, Bloom AJ. Easy as piadcs: A low-cost, ultra-high-resolution data acquisition system using a Raspberry Pi. APPLICATIONS IN PLANT SCIENCES 2022; 10:e11485. [PMID: 35774990 PMCID: PMC9215268 DOI: 10.1002/aps3.11485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 04/26/2022] [Indexed: 06/15/2023]
Abstract
PREMISE High-precision data acquisition (DAQ) is essential for developing new methods in the plant sciences. Commercial high-resolution DAQ systems are cost prohibitive, whereas the less expensive systems that are currently available lack the resolution and precision required for many physiological measurements. METHODS AND RESULTS We developed the software libraries, called piadcs, and hardware design for a DAQ system based on an ultra-high-resolution analog-to-digital converter and a Raspberry Pi computer. We tested the system precision with and without a thermocouple attached and found the precision with the sensor to be better than ±0.01°C and the maximum possible system resolution to be 0.4 ppm. CONCLUSIONS The ultra-high-resolution DAQ system described here is inexpensive, flexible enough to be used with many different sensors, and can be built by researchers with rudimentary electronic and computer skills. This system is most applicable in the development of new measurement techniques and the improvement of existing methods.
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Affiliation(s)
- Anna Knapp
- Department of Plant Sciences, University of CaliforniaDavis, One Shields AvenueDavisCalifornia95616USA
| | - Arnold J. Bloom
- Department of Plant Sciences, University of CaliforniaDavis, One Shields AvenueDavisCalifornia95616USA
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18
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Tanabata T, Kodama K, Hashiguchi T, Inomata D, Tanaka H, Isobe S. Development of a plant conveyance system using an AGV and a self-designed plant-handling device: A case study of DIY plant phenotyping. BREEDING SCIENCE 2022; 72:85-95. [PMID: 36045895 PMCID: PMC8987848 DOI: 10.1270/jsbbs.21070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 01/19/2022] [Indexed: 06/15/2023]
Abstract
Plant phenotyping technology has been actively developed in recent years, but the introduction of these technologies into the field of agronomic research has not progressed as expected, in part due to the need for flexibility and low cost. "DIY" (Do It Yourself) methodologies are an efficient way to overcome such obstacles. Devices with modular functionality are critical to DIY experimentation, allowing researchers flexibility of design. In this study, we developed a plant conveyance system using a commercial AGV (Automated Guided Vehicle) as a case study of DIY plant phenotyping. The convey module consists of two devices, a running device and a plant-handling device. The running device was developed based on a commercial AGV Kit. The plant-handling device, plant stands, and pot attachments were originally designed and fabricated by us and our associates. Software was also developed for connecting the devices and operating the system. The run route was set with magnetic tape, which can be easily changed or rerouted. Our plant delivery system was developed with low cost and having high flexibility, as a unit that can contribute to others' DIY' plant research efforts as well as our own. It is expected that the developed devices will contribute to diverse phenotype observations of plants in the greenhouse as well as to other important functions in plant breeding and agricultural production.
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Affiliation(s)
- Takanari Tanabata
- Department of Frontier Research and Development, Kazusa DNA Research Institute, 2-6-7 Kazusa-kamatari, Kisarazu, Chiba 292-0818, Japan
| | - Kunihiro Kodama
- Department of Frontier Research and Development, Kazusa DNA Research Institute, 2-6-7 Kazusa-kamatari, Kisarazu, Chiba 292-0818, Japan
| | - Takuyu Hashiguchi
- Faculty of Agriculture, University of Miyazaki, 1-1 Gakuenkibanadai-Nishi, Miyazaki 889-2192, Japan
| | | | - Hidenori Tanaka
- Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki, 1-1 Gakuenkibanadai-Nishi, Miyazaki 889-2192, Japan
| | - Sachiko Isobe
- Department of Frontier Research and Development, Kazusa DNA Research Institute, 2-6-7 Kazusa-kamatari, Kisarazu, Chiba 292-0818, Japan
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19
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Banerjee BP, Spangenberg G, Kant S. CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements. BIOSENSORS 2021; 12:bios12010016. [PMID: 35049643 PMCID: PMC8774002 DOI: 10.3390/bios12010016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/25/2021] [Accepted: 12/27/2021] [Indexed: 05/12/2023]
Abstract
The phenotypic characterization of crop genotypes is an essential, yet challenging, aspect of crop management and agriculture research. Digital sensing technologies are rapidly advancing plant phenotyping and speeding-up crop breeding outcomes. However, off-the-shelf sensors might not be fully applicable and suitable for agricultural research due to the diversity in crop species and specific needs during plant breeding selections. Customized sensing systems with specialized sensor hardware and software architecture provide a powerful and low-cost solution. This study designed and developed a fully integrated Raspberry Pi-based LiDAR sensor named CropBioMass (CBM), enabled by internet of things to provide a complete end-to-end pipeline. The CBM is a low-cost sensor, provides high-throughput seamless data collection in field, small data footprint, injection of data onto the remote server, and automated data processing. The phenotypic traits of crop fresh biomass, dry biomass, and plant height that were estimated by CBM data had high correlation with ground truth manual measurements in a wheat field trial. The CBM is readily applicable for high-throughput plant phenotyping, crop monitoring, and management for precision agricultural applications.
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Affiliation(s)
| | - German Spangenberg
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | - Surya Kant
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400, Australia;
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
- Correspondence: ; Tel.: +61-3-4344-3179
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20
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Potter JJ, Tan S, Penczykowski RM. Robotany: A portable, low‐cost platform for precise automated aerial imaging of field plots. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- James J. Potter
- Department of Mechanical Engineering and Materials Science Washington University in St. Louis St. Louis Missouri USA
- Tyson Research Center Washington University in St. Louis Eureka Missouri USA
| | - Sylvia Tan
- Department of Mechanical Engineering and Materials Science Washington University in St. Louis St. Louis Missouri USA
- Department of Mechanical Engineering Northwestern University Evanston Illinois USA
| | - Rachel M. Penczykowski
- Department of Biology Washington University in St. Louis St. Louis Missouri USA
- Tyson Research Center Washington University in St. Louis Eureka Missouri USA
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21
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Jolles JW. Broad‐scale applications of the Raspberry Pi: A review and guide for biologists. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13652] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Jolle W. Jolles
- Zukunftskolleg University of Konstanz Konstanz Germany
- Department of Collective Behaviour Max Planck Institute of Animal Behaviour Konstanz Germany
- Centre for Research on Ecology and Forestry Applications (CREAF) Barcelona Spain
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22
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Feldman A, Wang H, Fukano Y, Kato Y, Ninomiya S, Guo W. EasyDCP: An affordable, high‐throughput tool to measure plant phenotypic traits in 3D. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13645] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Alexander Feldman
- Institute for Sustainable Agro‐Ecosystem Services Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan
| | - Haozhou Wang
- Institute for Sustainable Agro‐Ecosystem Services Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan
| | - Yuya Fukano
- Institute for Sustainable Agro‐Ecosystem Services Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan
| | - Yoichiro Kato
- Institute for Sustainable Agro‐Ecosystem Services Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan
| | - Seishi Ninomiya
- Institute for Sustainable Agro‐Ecosystem Services Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan
- Plant Phenomics Research Center Nanjing Agricultural University Nanjing China
| | - Wei Guo
- Institute for Sustainable Agro‐Ecosystem Services Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan
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23
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Verbraeken L, Wuyts N, Mertens S, Cannoot B, Maleux K, Demuynck K, De Block J, Merchie J, Dhondt S, Bonaventure G, Crafts-Brandner S, Vogel J, Bruce W, Inzé D, Maere S, Nelissen H. Drought affects the rate and duration of organ growth but not inter-organ growth coordination. PLANT PHYSIOLOGY 2021; 186:1336-1353. [PMID: 33788927 PMCID: PMC8195526 DOI: 10.1093/plphys/kiab155] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 03/02/2021] [Indexed: 05/18/2023]
Abstract
Drought at flowering and grain filling greatly reduces maize (Zea mays) yield. Climate change is causing earlier and longer-lasting periods of drought, which affect the growth of multiple maize organs throughout development. To study how long periods of water deficit impact the dynamic nature of growth, and to determine how these relate to reproductive drought, we employed a high-throughput phenotyping platform featuring precise irrigation, imaging systems, and image-based biomass estimations. Prolonged drought resulted in a reduction of growth rate of individual organs-though an extension of growth duration partially compensated for this-culminating in lower biomass and delayed flowering. However, long periods of drought did not affect the highly organized succession of maximal growth rates of the distinct organs, i.e. leaves, stems, and ears. Two drought treatments negatively affected distinct seed yield components: Prolonged drought mainly reduced the number of spikelets, and drought during the reproductive period increased the anthesis-silking interval. The identification of these divergent biomass and yield components, which were affected by the shift in duration and intensity of drought, will facilitate trait-specific breeding toward future climate-resilient crops.
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Affiliation(s)
- Lennart Verbraeken
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Nathalie Wuyts
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Stien Mertens
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Bernard Cannoot
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Katrien Maleux
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Kirin Demuynck
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Jolien De Block
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Julie Merchie
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Stijn Dhondt
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | | | | | | | | | - Dirk Inzé
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Steven Maere
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Hilde Nelissen
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
- Author for communication:
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24
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Development of a Raspberry Pi-Based Sensor System for Automated In-Field Monitoring to Support Crop Breeding Programs. INVENTIONS 2021. [DOI: 10.3390/inventions6020042] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making.
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25
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Tonelli A, Mangia V, Candiani A, Pasquali F, Mangiaracina TJ, Grazioli A, Sozzi M, Gorni D, Bussolati S, Cucinotta A, Basini G, Selleri S. Sensing Optimum in the Raw: Leveraging the Raw-Data Imaging Capabilities of Raspberry Pi for Diagnostics Applications. SENSORS 2021; 21:s21103552. [PMID: 34065190 PMCID: PMC8160707 DOI: 10.3390/s21103552] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/10/2021] [Accepted: 05/16/2021] [Indexed: 12/12/2022]
Abstract
Single-board computers (SBCs) and microcontroller boards (MCBs) are extensively used nowadays as prototyping platforms to accomplish innovative tasks. Very recently, implementations of these devices for diagnostics applications are rapidly gaining ground for research and educational purposes. Among the available solutions, Raspberry Pi represents one of the most used SBCs. In the present work, two setups based on Raspberry Pi and its CMOS-based camera (a 3D-printed device and an adaptation of a commercial product named We-Lab) were investigated as diagnostic instruments. Different camera elaboration processes were investigated, showing how direct access to the 10-bit raw data acquired from the sensor before downstream imaging processes could be beneficial for photometric applications. The developed solution was successfully applied to the evaluation of the oxidative stress using two commercial kits (d-ROM Fast; PAT). We suggest the analysis of raw data applied to SBC and MCB platforms in order to improve results.
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Affiliation(s)
- Alessandro Tonelli
- DNAPhone S.R.L., Viale Mentana 150, 43121 Parma, Italy; (A.T.); (V.M.); (A.C.); (F.P.); (T.J.M.); (A.G.); (M.S.)
| | - Veronica Mangia
- DNAPhone S.R.L., Viale Mentana 150, 43121 Parma, Italy; (A.T.); (V.M.); (A.C.); (F.P.); (T.J.M.); (A.G.); (M.S.)
| | - Alessandro Candiani
- DNAPhone S.R.L., Viale Mentana 150, 43121 Parma, Italy; (A.T.); (V.M.); (A.C.); (F.P.); (T.J.M.); (A.G.); (M.S.)
| | - Francesco Pasquali
- DNAPhone S.R.L., Viale Mentana 150, 43121 Parma, Italy; (A.T.); (V.M.); (A.C.); (F.P.); (T.J.M.); (A.G.); (M.S.)
| | - Tiziana Jessica Mangiaracina
- DNAPhone S.R.L., Viale Mentana 150, 43121 Parma, Italy; (A.T.); (V.M.); (A.C.); (F.P.); (T.J.M.); (A.G.); (M.S.)
| | - Alessandro Grazioli
- DNAPhone S.R.L., Viale Mentana 150, 43121 Parma, Italy; (A.T.); (V.M.); (A.C.); (F.P.); (T.J.M.); (A.G.); (M.S.)
| | - Michele Sozzi
- DNAPhone S.R.L., Viale Mentana 150, 43121 Parma, Italy; (A.T.); (V.M.); (A.C.); (F.P.); (T.J.M.); (A.G.); (M.S.)
| | - Davide Gorni
- H&D S.R.L., Strada Langhirano 264/1a, 43124 Parma, Italy;
| | - Simona Bussolati
- Dipartimento di Scienze Medico-Veterinarie, Via del Taglio 10, 43126 Parma, Italy; (S.B.); (G.B.)
| | - Annamaria Cucinotta
- Dipartimento di Ingegneria e Architettura, University of Parma, Parco Area delle Scienze, 181/A, 43124 Parma, Italy;
| | - Giuseppina Basini
- Dipartimento di Scienze Medico-Veterinarie, Via del Taglio 10, 43126 Parma, Italy; (S.B.); (G.B.)
| | - Stefano Selleri
- Dipartimento di Ingegneria e Architettura, University of Parma, Parco Area delle Scienze, 181/A, 43124 Parma, Italy;
- Correspondence: ; Tel.: +39-052-190-5763
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Mochida K, Nishii R, Hirayama T. Decoding Plant-Environment Interactions That Influence Crop Agronomic Traits. PLANT & CELL PHYSIOLOGY 2020; 61:1408-1418. [PMID: 32392328 PMCID: PMC7434589 DOI: 10.1093/pcp/pcaa064] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 04/26/2020] [Indexed: 05/16/2023]
Abstract
To ensure food security in the face of increasing global demand due to population growth and progressive urbanization, it will be crucial to integrate emerging technologies in multiple disciplines to accelerate overall throughput of gene discovery and crop breeding. Plant agronomic traits often appear during the plants' later growth stages due to the cumulative effects of their lifetime interactions with the environment. Therefore, decoding plant-environment interactions by elucidating plants' temporal physiological responses to environmental changes throughout their lifespans will facilitate the identification of genetic and environmental factors, timing and pathways that influence complex end-point agronomic traits, such as yield. Here, we discuss the expected role of the life-course approach to monitoring plant and crop health status in improving crop productivity by enhancing the understanding of plant-environment interactions. We review recent advances in analytical technologies for monitoring health status in plants based on multi-omics analyses and strategies for integrating heterogeneous datasets from multiple omics areas to identify informative factors associated with traits of interest. In addition, we showcase emerging phenomics techniques that enable the noninvasive and continuous monitoring of plant growth by various means, including three-dimensional phenotyping, plant root phenotyping, implantable/injectable sensors and affordable phenotyping devices. Finally, we present an integrated review of analytical technologies and applications for monitoring plant growth, developed across disciplines, such as plant science, data science and sensors and Internet-of-things technologies, to improve plant productivity.
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Affiliation(s)
- Keiichi Mochida
- RIKEN Center for Sustainable Resource Science, Tsurumi-ku, Yokohama, Japan
- Kihara Institute for Biological Research, Yokohama City University, Totsuka-ku, Yokohama, Japan
- Graduate School of Nanobioscience, Yokohama City University, Kanazawa-ku, Yokohama, Japan
- Institute of Plant Science and Resources, Okayama University, Kurashiki, Japan
- Corresponding author: E-mail, ; Fax, +81-45-503-9609
| | - Ryuei Nishii
- School of Information and Data Sciences, Nagasaki University, Nagasaki, Japan
| | - Takashi Hirayama
- Institute of Plant Science and Resources, Okayama University, Kurashiki, Japan
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Tovar JC, Quillatupa C, Callen ST, Castillo SE, Pearson P, Shamin A, Schuhl H, Fahlgren N, Gehan MA. Heating quinoa shoots results in yield loss by inhibiting fruit production and delaying maturity. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 102:1058-1073. [PMID: 31971639 PMCID: PMC7318176 DOI: 10.1111/tpj.14699] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 01/09/2020] [Accepted: 01/13/2020] [Indexed: 05/13/2023]
Abstract
Increasing global temperatures and a growing world population create the need to develop crop varieties that provide higher yields in warmer climates. There is growing interest in expanding quinoa cultivation, because of the ability of quinoa to produce nutritious grain in poor soils, with little water and at high salinity. The main limitation to expanding quinoa cultivation, however, is the susceptibility of quinoa to temperatures above approximately 32°C. This study investigates the phenotypes, genes and mechanisms that may affect quinoa seed yield at high temperatures. Using a differential heating system where only roots or only shoots were heated, quinoa yield losses were attributed to shoot heating. Plants with heated shoots lost 60-85% yield as compared with control plants. Yield losses were the result of lower fruit production, which lowered the number of seeds produced per plant. Furthermore, plants with heated shoots had delayed maturity and greater non-reproductive shoot biomass, whereas plants with both heated roots and heated shoots produced higher yields from the panicles that had escaped the heat, compared with the control. This suggests that quinoa uses a type of avoidance strategy to survive heat. Gene expression analysis identified transcription factors differentially expressed in plants with heated shoots and low yield that had been previously associated with flower development and flower opening. Interestingly, in plants with heated shoots, flowers stayed closed during the day while the control flowers were open. Although a closed flower may protect the floral structures, this could also cause yield losses by limiting pollen dispersal, which is necessary to produce fruit in the mostly female flowers of quinoa.
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Affiliation(s)
- Jose C. Tovar
- Donald Danforth Plant Science CenterSt. LouisMO63132USA
| | | | - Steven T. Callen
- Donald Danforth Plant Science CenterSt. LouisMO63132USA
- Bayer US – Crop ScienceSt. LouisMO63141USA
| | | | - Paige Pearson
- Donald Danforth Plant Science CenterSt. LouisMO63132USA
- Bayer US – Crop ScienceSt. LouisMO63141USA
| | | | - Haley Schuhl
- Donald Danforth Plant Science CenterSt. LouisMO63132USA
| | - Noah Fahlgren
- Donald Danforth Plant Science CenterSt. LouisMO63132USA
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28
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NOMURA K, TAKADA A, KUNISHIGE H, OZAKI Y, OKAYASU T, YASUTAKE D, KITANO M. Long-term and Continuous Measurement of Canopy Photosynthesis and Growth of Spinach. ACTA ACUST UNITED AC 2020. [DOI: 10.2525/ecb.58.21] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Koichi NOMURA
- Graduate School of Bioresource and Bioenvironment Sciences, Kyushu University
| | - Akihiro TAKADA
- Graduate School of Bioresource and Bioenvironment Sciences, Kyushu University
| | - Hirosato KUNISHIGE
- Graduate School of Bioresource and Bioenvironment Sciences, Kyushu University
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29
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Samiei S, Rasti P, Ly Vu J, Buitink J, Rousseau D. Deep learning-based detection of seedling development. PLANT METHODS 2020; 16:103. [PMID: 32742300 PMCID: PMC7391498 DOI: 10.1186/s13007-020-00647-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 07/23/2020] [Indexed: 05/10/2023]
Abstract
BACKGROUND Monitoring the timing of seedling emergence and early development via high-throughput phenotyping with computer vision is a challenging topic of high interest in plant science. While most studies focus on the measurements of leaf area index or detection of specific events such as emergence, little attention has been put on the identification of kinetics of events of early seedling development on a seed to seed basis. RESULT Imaging systems screened the whole seedling growth process from the top view. Precise annotation of emergence out of the soil, cotyledon opening, and appearance of first leaf was conducted. This annotated data set served to train deep neural networks. Various strategies to incorporate in neural networks, the prior knowledge of the order of the developmental stages were investigated. Best results were obtained with a deep neural network followed with a long short term memory cell, which achieves more than 90% accuracy of correct detection. CONCLUSION This work provides a full pipeline of image processing and machine learning to classify three stages of plant growth plus soil on the different accessions of two species of red clover and alfalfa but which could easily be extended to other crops and other stages of development.
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Affiliation(s)
- Salma Samiei
- Laboratoire Angevin de Recherche en Ingénierie des Système (LARIS),UMR INRAe IRHS, Université d’Angers, Angers, France
| | - Pejman Rasti
- Laboratoire Angevin de Recherche en Ingénierie des Système (LARIS),UMR INRAe IRHS, Université d’Angers, Angers, France
- Department of Big Data and Data Science, École d’ingénieur Informatique et Environnement (ESAIP), Angers, France
| | - Joseph Ly Vu
- Institut de Recherche en Horticulture et Semences-UMR1345, Université d’Angers, INRAe, Institut Agro, SFR 4207 QuaSaV, Beaucouzé, France
| | - Julia Buitink
- Institut de Recherche en Horticulture et Semences-UMR1345, Université d’Angers, INRAe, Institut Agro, SFR 4207 QuaSaV, Beaucouzé, France
| | - David Rousseau
- Laboratoire Angevin de Recherche en Ingénierie des Système (LARIS),UMR INRAe IRHS, Université d’Angers, Angers, France
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30
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Grindstaff B, Mabry ME, Blischak PD, Quinn M, Chris Pires J. Affordable remote monitoring of plant growth in facilities using Raspberry Pi computers. APPLICATIONS IN PLANT SCIENCES 2019. [PMID: 31467803 DOI: 10.1101/586776] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
PREMISE Environmentally controlled facilities, such as growth chambers, are essential tools for experimental research. Automated, low-cost, remote-monitoring hardware can greatly improve both reproducibility and maintenance. METHODS AND RESULTS Using a Raspberry Pi computer, open-source software, environmental sensors, and a camera, we developed Growth Monitor pi (GMpi), a cost-effective system for monitoring growth chamber conditions. Coupled with our software, GMPi_Pack, our setup automates sensor readings, photography, and alerts when conditions fall out of range. CONCLUSIONS GMpi offers access to environmental data logging, improving reproducibility of experiments and reinforcing the stability of controlled environmental facilities. The device is also flexible and scalable, allowing researchers the ability to customize and expand GMpi for their own needs.
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Affiliation(s)
- Brandin Grindstaff
- Division of Biological Sciences and Bond Life Sciences Center University of Missouri Columbia Missouri 65211 USA
| | - Makenzie E Mabry
- Division of Biological Sciences and Bond Life Sciences Center University of Missouri Columbia Missouri 65211 USA
| | - Paul D Blischak
- Department of Ecology and Evolutionary Biology University of Arizona Tucson Arizona 85721 USA
| | - Micheal Quinn
- Division of Information Technology University of Missouri Columbia Missouri 65211 USA
| | - J Chris Pires
- Division of Biological Sciences and Bond Life Sciences Center University of Missouri Columbia Missouri 65211 USA
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31
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Grindstaff B, Mabry ME, Blischak PD, Quinn M, Chris Pires J. Affordable remote monitoring of plant growth in facilities using Raspberry Pi computers. APPLICATIONS IN PLANT SCIENCES 2019; 7:e11280. [PMID: 31467803 PMCID: PMC6711351 DOI: 10.1002/aps3.11280] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 07/03/2019] [Indexed: 05/11/2023]
Abstract
PREMISE Environmentally controlled facilities, such as growth chambers, are essential tools for experimental research. Automated, low-cost, remote-monitoring hardware can greatly improve both reproducibility and maintenance. METHODS AND RESULTS Using a Raspberry Pi computer, open-source software, environmental sensors, and a camera, we developed Growth Monitor pi (GMpi), a cost-effective system for monitoring growth chamber conditions. Coupled with our software, GMPi_Pack, our setup automates sensor readings, photography, and alerts when conditions fall out of range. CONCLUSIONS GMpi offers access to environmental data logging, improving reproducibility of experiments and reinforcing the stability of controlled environmental facilities. The device is also flexible and scalable, allowing researchers the ability to customize and expand GMpi for their own needs.
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Affiliation(s)
- Brandin Grindstaff
- Division of Biological Sciences and Bond Life Sciences CenterUniversity of MissouriColumbiaMissouri65211USA
| | - Makenzie E. Mabry
- Division of Biological Sciences and Bond Life Sciences CenterUniversity of MissouriColumbiaMissouri65211USA
| | - Paul D. Blischak
- Department of Ecology and Evolutionary BiologyUniversity of ArizonaTucsonArizona85721USA
| | - Micheal Quinn
- Division of Information TechnologyUniversity of MissouriColumbiaMissouri65211USA
| | - J. Chris Pires
- Division of Biological Sciences and Bond Life Sciences CenterUniversity of MissouriColumbiaMissouri65211USA
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32
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Hickey LT, N Hafeez A, Robinson H, Jackson SA, Leal-Bertioli SCM, Tester M, Gao C, Godwin ID, Hayes BJ, Wulff BBH. Breeding crops to feed 10 billion. Nat Biotechnol 2019; 37:744-754. [PMID: 31209375 DOI: 10.1038/s41587-019-0152-9] [Citation(s) in RCA: 322] [Impact Index Per Article: 64.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2018] [Accepted: 04/25/2019] [Indexed: 12/14/2022]
Abstract
Crop improvements can help us to meet the challenge of feeding a population of 10 billion, but can we breed better varieties fast enough? Technologies such as genotyping, marker-assisted selection, high-throughput phenotyping, genome editing, genomic selection and de novo domestication could be galvanized by using speed breeding to enable plant breeders to keep pace with a changing environment and ever-increasing human population.
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Affiliation(s)
- Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia.
| | | | | | - Scott A Jackson
- Center for Applied Genetic Technologies, Department of Crop and Soil Sciences, University of Georgia, Athens, GA, USA
| | - Soraya C M Leal-Bertioli
- Center for Applied Genetic Technologies, Department of Plant Pathology, University of Georgia, Athens, GA, USA
| | - Mark Tester
- King Abdullah University of Science and Technology (KAUST), Division of Biological and Environmental Sciences and Engineering, Thuwal, Saudi Arabia
| | - Caixia Gao
- State Key Laboratory of Plant Cell and Chromosome Engineering, Center for Genome Editing, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing, China
| | - Ian D Godwin
- School of Agriculture and Food Sciences, The University of Queensland, Brisbane, Queensland, Australia
| | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia
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Nagano S, Moriyuki S, Wakamori K, Mineno H, Fukuda H. Leaf-Movement-Based Growth Prediction Model Using Optical Flow Analysis and Machine Learning in Plant Factory. FRONTIERS IN PLANT SCIENCE 2019; 10:227. [PMID: 30967880 PMCID: PMC6439531 DOI: 10.3389/fpls.2019.00227] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 02/11/2019] [Indexed: 05/10/2023]
Abstract
Productivity stabilization is a critical issue facing plant factories. As such, researchers have been investigating growth prediction with the overall goal of improving productivity. The projected area of a plant (PA) is usually used for growth prediction, by which the growth of a plant is estimated by observing the overall approximate movement of the plant. To overcome this problem, this study focused on the time-series movement of plant leaves, using optical flow (OF) analysis to acquire this information for a lettuce. OF analysis is an image processing method that extracts the difference between two consecutive frames caused by the movement of the subject. Experiments were carried out at a commercial large-scale plant factory. By using a microcomputer with a camera module placed above the lettuce seedlings, images of 338 seedlings were taken every 20 min over 9 days (from the 6th to the 15th day after sowing). Then, the features of the leaf movement were extracted from the image by calculating the normal-vector in the OF analysis, and these features were applied to machine learning to predict the fresh weight of the lettuce at harvest time (38 days after sowing). The growth prediction model using the features extracted from the OF analysis was found to perform well with a correlation ratio of 0.743. Furthermore, this study also considered a phenotyping system that was capable of automatically analyzing a plant image, which would allow this growth prediction model to be widely used in commercial plant factories.
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Affiliation(s)
- Shogo Nagano
- Department of Mechanical Engineering, Graduate School of Engineering, Osaka Prefecture University, Sakai, Japan
- Japan Society for the Promotion of Science, Tokyo, Japan
| | - Shogo Moriyuki
- Department of Mechanical Engineering, Graduate School of Engineering, Osaka Prefecture University, Sakai, Japan
| | - Kazumasa Wakamori
- Graduate School of Integrated Science and Technology, Shizuoka University, Shizuoka, Japan
| | - Hiroshi Mineno
- Graduate School of Integrated Science and Technology, Shizuoka University, Shizuoka, Japan
- PRESTO, Japan Science and Technology Agency, Kawaguchi, Japan
| | - Hirokazu Fukuda
- Department of Mechanical Engineering, Graduate School of Engineering, Osaka Prefecture University, Sakai, Japan
- PRESTO, Japan Science and Technology Agency, Kawaguchi, Japan
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34
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Hoyer JS, Pruneda‐Paz JL, Breton G, Hassert MA, Holcomb EE, Fowler H, Bauer KM, Mreen J, Kay SA, Carrington JC. Functional dissection of the ARGONAUTE7 promoter. PLANT DIRECT 2019; 3:e00102. [PMID: 31245750 PMCID: PMC6508778 DOI: 10.1002/pld3.102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Revised: 10/29/2018] [Accepted: 11/09/2018] [Indexed: 05/27/2023]
Abstract
ARGONAUTES are the central effector proteins of RNA silencing which bind target transcripts in a small RNA-guided manner. Arabidopsis thaliana has 10 ARGONAUTE (AGO) genes, with specialized roles in RNA-directed DNA methylation, post-transcriptional gene silencing, and antiviral defense. To better understand specialization among AGO genes at the level of transcriptional regulation we tested a library of 1497 transcription factors for binding to the promoters of AGO1,AGO10, and AGO7 using yeast 1-hybrid assays. A ranked list of candidate DNA-binding TFs revealed binding of the AGO7 promoter by a number of proteins in two families: the miR156-regulated SPL family and the miR319-regulated TCP family, both of which have roles in developmental timing and leaf morphology. Possible functions for SPL and TCP binding are unclear: we showed that these binding sites are not required for the polar expression pattern of AGO7, nor for the function of AGO7 in leaf shape. Normal AGO7 transcription levels and function appear to depend instead on an adjacent 124-bp region. Progress in understanding the structure of this promoter may aid efforts to understand how the conserved AGO7-triggered TAS3 pathway functions in timing and polarity.
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Affiliation(s)
- J. Steen Hoyer
- Donald Danforth Plant Science CenterSt. LouisMissouri
- Computational and Systems Biology ProgramWashington UniversitySt. LouisMissouri
| | - Jose L. Pruneda‐Paz
- Division of Biological Sciences and Center for ChronobiologyUniversity of California San DiegoLa JollaCalifornia
| | - Ghislain Breton
- Division of Biological Sciences and Center for ChronobiologyUniversity of California San DiegoLa JollaCalifornia
- Department of Integrative Biology and PharmacologyMcGovern Medical SchoolHoustonTexas
| | | | | | - Halley Fowler
- Donald Danforth Plant Science CenterSt. LouisMissouri
| | | | - Jacob Mreen
- Donald Danforth Plant Science CenterSt. LouisMissouri
| | - Steve A. Kay
- Division of Biological Sciences and Center for ChronobiologyUniversity of California San DiegoLa JollaCalifornia
- Department of NeurologyUniversity of Southern CaliforniaLos AngelesCalifornia
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Morton MJL, Awlia M, Al‐Tamimi N, Saade S, Pailles Y, Negrão S, Tester M. Salt stress under the scalpel - dissecting the genetics of salt tolerance. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 97:148-163. [PMID: 30548719 PMCID: PMC6850516 DOI: 10.1111/tpj.14189] [Citation(s) in RCA: 123] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Revised: 11/28/2018] [Accepted: 11/30/2018] [Indexed: 05/08/2023]
Abstract
Salt stress limits the productivity of crops grown under saline conditions, leading to substantial losses of yield in saline soils and under brackish and saline irrigation. Salt tolerant crops could alleviate these losses while both increasing irrigation opportunities and reducing agricultural demands on dwindling freshwater resources. However, despite significant efforts, progress towards this goal has been limited, largely because of the genetic complexity of salt tolerance for agronomically important yield-related traits. Consequently, the focus is shifting to the study of traits that contribute to overall tolerance, thus breaking down salt tolerance into components that are more genetically tractable. Greater consideration of the plasticity of salt tolerance mechanisms throughout development and across environmental conditions furthers this dissection. The demand for more sophisticated and comprehensive methodologies is being met by parallel advances in high-throughput phenotyping and sequencing technologies that are enabling the multivariate characterisation of vast germplasm resources. Alongside steady improvements in statistical genetics models, forward genetics approaches for elucidating salt tolerance mechanisms are gaining momentum. Subsequent quantitative trait locus and gene validation has also become more accessible, most recently through advanced techniques in molecular biology and genomic analysis, facilitating the translation of findings to the field. Besides fuelling the improvement of established crop species, this progress also facilitates the domestication of naturally salt tolerant orphan crops. Taken together, these advances herald a promising era of discovery for research into the genetics of salt tolerance in plants.
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Affiliation(s)
- Mitchell J. L. Morton
- Division of Biological and Environmental Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Kingdom of Saudi Arabia
| | - Mariam Awlia
- Division of Biological and Environmental Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Kingdom of Saudi Arabia
| | - Nadia Al‐Tamimi
- Division of Biological and Environmental Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Kingdom of Saudi Arabia
| | - Stephanie Saade
- Division of Biological and Environmental Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Kingdom of Saudi Arabia
| | - Yveline Pailles
- Division of Biological and Environmental Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Kingdom of Saudi Arabia
| | - Sónia Negrão
- Division of Biological and Environmental Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Kingdom of Saudi Arabia
| | - Mark Tester
- Division of Biological and Environmental Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Kingdom of Saudi Arabia
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36
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Schneider D, Lopez LS, Li M, Crawford JD, Kirchhoff H, Kunz HH. Fluctuating light experiments and semi-automated plant phenotyping enabled by self-built growth racks and simple upgrades to the IMAGING-PAM. PLANT METHODS 2019; 15:156. [PMID: 31889980 PMCID: PMC6927185 DOI: 10.1186/s13007-019-0546-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 12/09/2019] [Indexed: 05/06/2023]
Abstract
BACKGROUND Over the last years, several plant science labs have started to employ fluctuating growth light conditions to simulate natural light regimes more closely. Many plant mutants reveal quantifiable effects under fluctuating light despite being indistinguishable from wild-type plants under standard constant light. Moreover, many subtle plant phenotypes become intensified and thus can be studied in more detail. This observation has caused a paradigm shift within the photosynthesis research community and an increasing number of scientists are interested in using fluctuating light growth conditions. However, high installation costs for commercial controllable LED setups as well as costly phenotyping equipment can make it hard for small academic groups to compete in this emerging field. RESULTS We show a simple do-it-yourself approach to enable fluctuating light growth experiments. Our results using previously published fluctuating light sensitive mutants, stn7 and pgr5, confirm that our low-cost setup yields similar results as top-prized commercial growth regimes. Moreover, we show how we increased the throughput of our Walz IMAGING-PAM, also found in many other departments around the world. We have designed a Python and R-based open source toolkit that allows for semi-automated sample segmentation and data analysis thereby reducing the processing bottleneck of large experimental datasets. We provide detailed instructions on how to build and functionally test each setup. CONCLUSIONS With material costs well below USD$1000, it is possible to setup a fluctuating light rack including a constant light control shelf for comparison. This allows more scientists to perform experiments closer to natural light conditions and contribute to an emerging research field. A small addition to the IMAGING-PAM hardware not only increases sample throughput but also enables larger-scale plant phenotyping with automated data analysis.
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Affiliation(s)
- Dominik Schneider
- Compact Plants Phenomics Center, Washington State University, PO Box 646340, Pullman, WA 99164-6340 USA
- Institute of Biological Chemistry, Washington State University, PO Box 646340, Pullman, WA 99164-6340 USA
| | - Laura S. Lopez
- Plant Physiology, School of Biological Sciences, Washington State University, PO Box 644236, Pullman, WA 99164-4236 USA
| | - Meng Li
- Institute of Biological Chemistry, Washington State University, PO Box 646340, Pullman, WA 99164-6340 USA
| | - Joseph D. Crawford
- Plant Physiology, School of Biological Sciences, Washington State University, PO Box 644236, Pullman, WA 99164-4236 USA
| | - Helmut Kirchhoff
- Institute of Biological Chemistry, Washington State University, PO Box 646340, Pullman, WA 99164-6340 USA
| | - Hans-Henning Kunz
- Plant Physiology, School of Biological Sciences, Washington State University, PO Box 644236, Pullman, WA 99164-4236 USA
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Berry JC, Fahlgren N, Pokorny AA, Bart RS, Veley KM. An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping. PeerJ 2018; 6:e5727. [PMID: 30310752 PMCID: PMC6174877 DOI: 10.7717/peerj.5727] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 09/10/2018] [Indexed: 12/11/2022] Open
Abstract
High-throughput phenotyping has emerged as a powerful method for studying plant biology. Large image-based datasets are generated and analyzed with automated image analysis pipelines. A major challenge associated with these analyses is variation in image quality that can inadvertently bias results. Images are made up of tuples of data called pixels, which consist of R, G, and B values, arranged in a grid. Many factors, for example image brightness, can influence the quality of the image that is captured. These factors alter the values of the pixels within images and consequently can bias the data and downstream analyses. Here, we provide an automated method to adjust an image-based dataset so that brightness, contrast, and color profile is standardized. The correction method is a collection of linear models that adjusts pixel tuples based on a reference panel of colors. We apply this technique to a set of images taken in a high-throughput imaging facility and successfully detect variance within the image dataset. In this case, variation resulted from temperature-dependent light intensity throughout the experiment. Using this correction method, we were able to standardize images throughout the dataset, and we show that this correction enhanced our ability to accurately quantify morphological measurements within each image. We implement this technique in a high-throughput pipeline available with this paper, and it is also implemented in PlantCV.
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Affiliation(s)
- Jeffrey C. Berry
- Donald Danforth Plant Science Center, Saint Louis, MO, United States of America
| | - Noah Fahlgren
- Donald Danforth Plant Science Center, Saint Louis, MO, United States of America
| | | | - Rebecca S. Bart
- Donald Danforth Plant Science Center, Saint Louis, MO, United States of America
| | - Kira M. Veley
- Donald Danforth Plant Science Center, Saint Louis, MO, United States of America
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38
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ABE-VIEW: Android Interface for Wireless Data Acquisition and Control. SENSORS 2018; 18:s18082647. [PMID: 30104474 PMCID: PMC6111993 DOI: 10.3390/s18082647] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 08/04/2018] [Accepted: 08/09/2018] [Indexed: 01/19/2023]
Abstract
Advances in scientific knowledge are increasingly supported by a growing community of developers freely sharing new hardware and software tools. In this spirit we have developed a free Android app, ABE-VIEW, that provides a flexible graphical user interface (GUI) populated entirely from a remote instrument by ascii-coded instructions communicated wirelessly over Bluetooth. Options include an interactive chart for plotting data in real time, up to 16 data fields, and virtual controls including buttons, numerical controls with user-defined range and resolution, and radio buttons which the user can use to send coded instructions back to the instrument. Data can be recorded into comma delimited files interactively at the user’s discretion. Our original objective of the project was to make data acquisition and control for undergraduate engineering labs more modular and affordable, but we have also found that the tool is highly useful for rapidly testing novel sensor systems for iterative improvement. Here we document the operation of the app and syntax for communicating with it. We also illustrate its application in undergraduate engineering labs on dynamic systems modeling, as well as for identifying the source of harmonic distortion affecting electrochemical impedance measurements at certain frequencies in a novel wireless potentiostat.
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Czedik‐Eysenberg A, Seitner S, Güldener U, Koemeda S, Jez J, Colombini M, Djamei A. The 'PhenoBox', a flexible, automated, open-source plant phenotyping solution. THE NEW PHYTOLOGIST 2018; 219:808-823. [PMID: 29621393 PMCID: PMC6485332 DOI: 10.1111/nph.15129] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 02/22/2018] [Indexed: 05/11/2023]
Abstract
There is a need for flexible and affordable plant phenotyping solutions for basic research and plant breeding. We demonstrate our open source plant imaging and processing solution ('PhenoBox'/'PhenoPipe') and provide construction plans, source code and documentation to rebuild the system. Use of the PhenoBox is exemplified by studying infection of the model grass Brachypodium distachyon by the head smut fungus Ustilago bromivora, comparing phenotypic responses of maize to infection with a solopathogenic Ustilago maydis (corn smut) strain and effector deletion strains, and studying salt stress response in Nicotiana benthamiana. In U. bromivora-infected grass, phenotypic differences between infected and uninfected plants were detectable weeks before qualitative head smut symptoms. Based on this, we could predict the infection outcome for individual plants with high accuracy. Using a PhenoPipe module for calculation of multi-dimensional distances from phenotyping data, we observe a time after infection-dependent impact of U. maydis effector deletion strains on phenotypic response in maize. The PhenoBox/PhenoPipe system is able to detect established salt stress responses in N. benthamiana. We have developed an affordable, automated, open source imaging and data processing solution that can be adapted to various phenotyping applications in plant biology and beyond.
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Affiliation(s)
- Angelika Czedik‐Eysenberg
- Gregor Mendel Institute (GMI)Austrian Academy of SciencesVienna BioCenter (VBC)Dr. Bohr‐Gasse 31030ViennaAustria
| | - Sebastian Seitner
- Gregor Mendel Institute (GMI)Austrian Academy of SciencesVienna BioCenter (VBC)Dr. Bohr‐Gasse 31030ViennaAustria
| | - Ulrich Güldener
- Department of Genome‐oriented BioinformaticsTechnische Universität MünchenWissenschaftszentrum WeihenstephanFreisingGermany
| | - Stefanie Koemeda
- Vienna Biocenter Core Facilities (VBCF)Dr. Bohr‐Gasse 31030ViennaAustria
| | - Jakub Jez
- Vienna Biocenter Core Facilities (VBCF)Dr. Bohr‐Gasse 31030ViennaAustria
| | - Martin Colombini
- Workshop, Research Institute of Molecular Pathology (IMP)Vienna BioCenter (VBC)Campus‐Vienna‐Biocenter 11030ViennaAustria
| | - Armin Djamei
- Gregor Mendel Institute (GMI)Austrian Academy of SciencesVienna BioCenter (VBC)Dr. Bohr‐Gasse 31030ViennaAustria
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Gitzendanner MA, Yang Y, Wickett NJ, McKain M, Beaulieu JM. Methods for exploring the plant tree of life. APPLICATIONS IN PLANT SCIENCES 2018; 6:e1039. [PMCID: PMC5895194 DOI: 10.1002/aps3.1039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Accepted: 03/15/2018] [Indexed: 05/24/2023]
Affiliation(s)
| | - Ya Yang
- Department of Plant and Microbial BiologyUniversity of MinnesotaSt. PaulMinnesota55108USA
| | - Norman J. Wickett
- Department of Plant ScienceChicago Botanic GardenGlencoeIllinois60022USA
- Plant Biology and ConservationNorthwestern UniversityEvanstonIllinois60208USA
| | - Michael McKain
- Department of Biological SciencesUniversity of AlabamaTuscaloosaAlabama35487USA
| | - Jeremy M. Beaulieu
- Department of Biological SciencesUniversity of ArkansasFayettevilleArkansas72701USA
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Gehan MA, Fahlgren N, Abbasi A, Berry JC, Callen ST, Chavez L, Doust AN, Feldman MJ, Gilbert KB, Hodge JG, Hoyer JS, Lin A, Liu S, Lizárraga C, Lorence A, Miller M, Platon E, Tessman M, Sax T. PlantCV v2: Image analysis software for high-throughput plant phenotyping. PeerJ 2017; 5:e4088. [PMID: 29209576 PMCID: PMC5713628 DOI: 10.7717/peerj.4088] [Citation(s) in RCA: 110] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Accepted: 11/03/2017] [Indexed: 12/11/2022] Open
Abstract
Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.
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Affiliation(s)
- Malia A. Gehan
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Noah Fahlgren
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Arash Abbasi
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Jeffrey C. Berry
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Steven T. Callen
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Current affiliation: Monsanto Company, St. Louis, MO, United States of America
| | - Leonardo Chavez
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Andrew N. Doust
- Department of Plant Biology, Ecology, and Evolution, Oklahoma State University, Stillwater, OK, United States of America
| | - Max J. Feldman
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Kerrigan B. Gilbert
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - John G. Hodge
- Department of Plant Biology, Ecology, and Evolution, Oklahoma State University, Stillwater, OK, United States of America
| | - J. Steen Hoyer
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Computational and Systems Biology Program, Washington University in St. Louis, St. Louis, MO, United States of America
| | - Andy Lin
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Current affiliation: Unidev, St. Louis, MO, United States of America
| | - Suxing Liu
- Arkansas Biosciences Institute, Arkansas State University, Jonesboro, AR, United States of America
- Current affiliation: Department of Plant Biology, University of Georgia, Athens, GA, United States of America
| | - César Lizárraga
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Current affiliation: CiBO Technologies, Cambridge, MA, United States of America
| | - Argelia Lorence
- Arkansas Biosciences Institute, Department of Chemistry and Physics, Arkansas State University, Jonesboro, AR, United States of America
| | - Michael Miller
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Current affiliation: Department of Agronomy and Horticulture, Center for Plant Science Innovation, Beadle Center for Biotechnology, University of Nebraska - Lincoln, Lincoln, NE, United States of America
| | | | - Monica Tessman
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Department of Plant Biology, Ecology, and Evolution, Oklahoma State University, Stillwater, OK, United States of America
| | - Tony Sax
- Missouri University of Science and Technology, Rolla, MO, United States of America
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