Data management, analysis and visualisation
Full research support, from project design through to data visualisation
The APPF offers so much more than the technology for data capture. Our multi-disciplinary team offers the full scope of data management, from statistically designed layouts at the outset through to visualisation tools on completion, all to maximise your results. We can support your research through:
- Consultation – on best practice protocols, project design and statistical layout.
- Analysis – of images, preparation of growth curves, and access to datasets and visualisations tools.
- Bioinformatics and biometry – by performing quality control processes on datasets, providing standard data analysis, provision of open source algorithms and access to analytical pipelines, and by assisting researchers to process and interpret complex data.
- Data management – including data storage and access to data sharing tools.
- Education and training – in plant phenomics, image analysis, bioinformatics, biometry and statistical design.
Open data sharing
APPF policy promotes open data sharing. All APPF is published online for reuse and developed algorithms are shared in open source environments.
Linking of phenomics information with the APPF’s modelling capabilities and TERN data, Bureau of Meteorology’s climatic data, and Geoscience Australia’s DataCube will provide forecasting products on the cloud for predicting agricultural productivity and the impact of various scenarios.
Unique software capabilities
Each node of the APPF also offers unique software capabilities:
The Phenomics Ontology Driven Data management (PODD) system delivers an Open source (GNU Affero GPL, v3) and free data management service to capture, manage, secure, annotate, distribute and publish raw and analysed data from the phenotyping projects ran by the Australian Plant Phenomics Facility. PODD also provides the ability to manage a repository of associated contextual information (metadata) based on standard ontologies (controlled vocabulary) to support data discovery and analysis services. Benefits include:
- Repository for many different types of experimental data used in phenomics research, with associated contextual metadata
- Researcher can discover and access relevant phenomics datasets
- System can support arbitrary ontologies so can be used for other discipline areas
For more information contact APPF (Canberra, CSIRO).
TraitCapture is a “seeds to traits” pipeline which allows users to track seed/genotype selection, set growth conditions, and analyse phenotypic variation for heritable components through to mapping causative loci via GWAS and QTL analysis. Web-based visualisation tools allow real-time graphing of environment data with associated plant growth in time-lapse. Cloud-enabled GWAS on plant growth variation can be performed during an experiment allowing for real time capturing of heritable traits and trait loci across environments. This feedback allows users to tune the environments, phenotyping protocols and image analysis to improve QTL detection. When QTL are identified, a user can resort plants based on alternative genotype classes to look for pleiotropic effects on growth, development, and physiology. Experiments enabled by TraitCapture include:
- Iterative QTL identification and tests of pleiotropy.
- Heritability of potential spectral indices using hyperspectral cameras.
- Spatial and temporal distribution of fluorescent pigments under environmental stress.
- Light and temperature interactions on transpiration using Infrared (IR) cameras.
- Genetic basis of photosynthetic activity and efficiency using chlorophyll fluorescence cameras.
- Integration of 2.5D and 3D quantification of plant growth with stereo imaging.
Researchers undertaking experiments at the APPF’s Adelaide node are able to access their images and analyse their data in real time on a daily basis using Zegami. Zegami is a web application which allows users to filter, sort and chart data from experiments and group that data with the corresponding images.
With daily access to the incoming preliminary data, researchers can enjoy greater control over their experiments, introduce early intervention and modifications to experimental protocol if required, run a preliminary analysis before having view of the full experiment and even monitor progress when not onsite.
Zegami is designed to help us share our data with all researchers. We use Zegami to link the experimental metadata, such as genotypes and treatments, with the image data. We then interface directly with our results database, process the data and make it available to the user via a private login on the web.
Along with open access to its cutting edge infrastructure, the Australian Plant Phenomics Facility promotes the sharing of data and knowledge, driving best practice, innovation, collaboration and education to advance progress in global plant research.
Experts, tools and suppliers
Find an expert
The APPF Australian plant phenotyping experts list contains details of Australian researchers with expertise and capacity in plant phenomics including their areas of specialisation, technology, interests and location.
Find tools and suppliers
The list below contains links to recommended suppliers of a range of phenotyping tools and services that may support your research needs.
Exploring genetic variation for salinity tolerance in chickpea using image-based phenotyping
Atieno J, Li Y, Langridge P, Dowling K, Brien CJ, Berger B, Varshney RK, Sutton T (May 2017). Scientific Reports, DOI:10.1038/s41598-017-01211-7
Intraspecific diversity of terpenes of Eucalyptus camaldulensis (Myrtaceae) at a continental scale
Bustos-Segura C, Dillon S, Keszei A, Foley WJ, Külheim C (May 2017). Australian Journal of Botany, DOI: org/10.1071/BT16183#sthash.iNS5rrWr.dpuf
A Comprehensive Image-based Phenomic Analysis Reveals the Complex Genetic Architecture of Shoot Growth Dynamics in Rice (Oryza sativa)
Campbell MT, Du Q, Liub K, Brien CJ, Berger B, Zhang C, Walia H (Jun 2017). The Plant Genome, DOI: 10.3835/plantgenome2016.07.0064
Rapid recovery gene downregulation during excess-light stress and recovery in Arabidopsis
Crisp PA, Ganguly D, Smith AB, Murray KD, Estavillo GM, Searle IR, Ford E, Bogdanović O, Lister R, Borevitz JO, Eichten SR, Pogson BJ (Jul 2017), The Plant Cell, DOI: org/10.1105/tpc.16.00828
Functional differences in transport properties of natural HKT1;1 variants influence shoot Na+ exclusion in grapevine rootstocks
Henderson SW, Dunlevy JD, Wu Y, Blackmore DH, Walker RR, Edwards EJ, Gilliham M, Walker AR (Nov 2017), New Phytologist, DOI:
The genome of Chenopodium quinoa
Jarvis DE, Ho YS, Lightfoot DJ, Schmöckel SM, Li B, Borm TJA, Ohyanagi H, Mineta K, Michell CT, Saber N, Kharbatia NM, Rupper RR, Sharp AR, Dally N, Boughton BA, Woo YH, Gao G, Schijlen EGWM, Guo X, Momin AA, Negrão S, Al-Babili S, Gehring C, Roessner U, Jung C, Murphy K, Arold ST, Gojobori T, van der Linden CG, van Loo EN, Jellen EN, Maughan PJ, Tester M (Feb 2017). Nature, DOI:10.1038/nature21370
A practical method using a network of fixed infrared sensors for estimating crop canopy conductance and evaporation rate
Jones HG, Hutchinson PA, May T, Jamali H, Deery DM (Oct 2017). Biosystems Engineering, DOI: org/10.1016/j.biosystemseng.2017.09.012
Growth curve registration for evaluating salinity tolerance in barley
Meng R, Saade S, Kurtek S, Berger B, Brien CJ, Pillen K, Tester M, Sun Y (Mar 2017). Plant Methods, DOI: 10.1186/s13007-017-0165-7
Detecting spikes of wheat plants using neural networks with Laws texture energy
Qiongyan L, Cai J, Berger B, Okamoto M, Miklavcic SJ (Oct 2017), Plant Methods, DIO: org/10.1186/s13007-017-0231-1
Transition from a maternal to external nitrogen source in maize seedlings
Sabermanesh K, Holtham LR, George J, Roessner U, Boughton BA, Heuer S, Tester M, Plett DC, Garnett TP (Apr 2017). Journal of Integrative Plant Biology, DOI: 10.1111/jipb.12525(2017). Journal of Integrative Plant Biology, DOI: 10.1111/jipb.12525
Variation in shoot tolerance mechanisms not related to ion toxicity in barley
Tilbrook J, Schilling RK, Berger B, Garcia AF, Trittermann C, Coventry S, Rabie H, Brien CJ, Nguyen M, Tester M and Roy SJ (Sep 2017), Functional Plant Biology, DIO: org/10.1071/FP17049
Publications citing the Australian Plant Phenomics Facility
An approach to detect branches and seedpods based on 3D image in low-cost plant phenotyping platform
Cao T, Panjvani K, Dinh A, Wahid K, Bhowmik P (Apr 2017). IEEE Xplore. DOI: 10.1109/CCECE.2017.7946593
rosettR: protocol and software for seedling area and growth analysis
Nath K, O’Donnell JP, Lu Y (Mar 2017). Plant Methods. DOI: 10.1186/s13007-017-0163-9
Chlorophyll fluorescence for high-throughput screening of plants during abiotic stress, aging, and genetic perturbation
Tomé F, Jansseune K, Saey B, Grundy J, Vandenbroucke K, Hannah MA and Redestig H (May 2017). Springer International Publishing. DOI: 10.1007/978-3-319-48873-8_12
Efficient in-field plant phenomics for row-crops with an autonomous ground vehicle
Underwood J, Wendel A, Schofield B, McMurray L, Kimber R (May 2017). Journal of Field Robotics. DOI: 10.1002/rob.21728
Simko I, Hayes RJ and Furbank RT (2016). Frontiers in Plant Science. 7:1985. DOI: 10.3389/fpls.2016.01985
Methodology for High-Throughput Field Phenotyping of Canopy Temperature Using Airborne Thermography
Deery DM, Rebetzke GJ, Jimenez-Berni JA, James RA, Condon AG, Bovill WD, Hutchinson P, Scarrow J, Davy R and Furbank RT (2016). Frontiers in Plant Science. 7:1808. DOI: 10.3389/fpls.2016.01808
Salinity tolerance loci revealed in rice using high-throughput non-invasive phenotyping
Nadia Al-Tamimi, Chris Brien, Helena Oakey, Bettina Berger, Stephanie Saade, Yung Shwen Ho, Sandra M. Schmöckel, Mark Tester & Sonia Negrao (2016). Nature Communications. DOI: 10.1038/ncomms13342
Diurnal solar energy conversion and photo-protection in rice canopies
Katherine Meacham, Xavier Sirault, W. Paul Quick, Susanne von Caemmerer, Robert Furbank (2016). Plant Physiology. DOI:10.1104/pp.16.01585
Methodology for high-throughput field phenotyping of canopy temperature using airborne thermography
David M. Deery, Greg J. Rebetzke, Jose A. Jimenez-Berni, Richard A. James, Anthony G. Condon, William D. Bovill, Paul Hutchinson, Jamie Scarrow, Robert Davy and Robert T. Furbank (2016). Frontiers in Plant Science. DOI: 10.3389/fpls.2016.01808
Drought-inducible expression of Hv-miR827 enhances drought tolerance in transgenic barley
Ferdous, J., Whitford, R., Nguyen, M. et al. (2016). Functional & Integrative Genomics. DOI:10.1007/s10142-016-0526-8
Quantifying the Onset and Progression of Plant Senescence by Color Image Analysis for High Throughput Applications
Jinhai Cai, Mamoru Okamoto, Judith Atieno, Tim Sutton, Yongle Li, Stanley J. Miklavcic (2016). PLOS. DOI: org/10.1371/journal.pone.0157102
Image Harvest: an open-source platform for high-throughput plant image processing and analysis
Avi C. Knecht, Malachy T. Campbell, Adam Caprez, David R. Swanson, Harkamal Walia (2016). Journal of Experimental Biology. DOI: 10.1093/jxb/erw176
A QTL on the short arm of wheat (Triticum aestivum L.) chromosome 3B affects the stability of grain weight in plants exposed to a brief heat shock early in grain filling
Shirdelmoghanloo, H., Taylor, J.D., Lohraseb, I., Rabie, H.S., Brien, C., Timmins, A., Martin, P., Mather, D.E., Emebiri, L. and Collins, N.C. (2016). BMC Plant Biology, 16: 1-15. DOI:10.1186/s12870-016-0784-6
Heat susceptibility of grain filling in wheat (Triticum aestivum L.) linked with rapid chlorophyll loss during a 3-day heat treatment
Shirdelmoghanloo, H., Lohraseb, I., Rabie, H.S., Brien, C., Parent, B. and Collins, N.C. (2016). Acta Physiologiae Plantarum, 38: 208. DOI:10.1007/s11738-016-2208-5
Genomic variation across landscapes: insights and applications
Bragg JG, Supple MA, Andrew RL, Borevitz JO (2015). New Phytologist. DOI: 10.1111/nph.13410
Reconsidering plant memory: Intersections between stress recovery, RNA turnover, and epigenetics
Crisp PA, Ganguly D, Eichten SR, Borevitz JO, Pogson BJ. (2016). Science Advances. 2016 Feb 19;2(2):e1501340. DOI: 10.1126/sciadv.1501340
Population and phylogenomic decomposition via genotyping-by-sequencing in Australian Pelargonium
Nicotra AB, Chong C, Bragg JG, Ong CR, Aitken NC, Chuah A, Lepschi B, Borevitz (2016). Molecular Ecology, May 2016, 25(9):2000-14. DOI: 10.1111/mec.13584
SensorDB: a virtual laboratory for the integration, visualization and analysis of varied biological sensor data
Salehi A, Jimenez-Berni J, Deery DM, Doug Palmer D, Holland E, Rozas-Larraondo P, Chapman SC, Georgakopoulos D, Furbank RT (Dec 2015). Plant Methods. DOI: org/10.1186/s13007-015-0097-z
Pyramiding greater early vigour and integrated transpiration efficiency in bread wheat; trade-offs and benefits
Wilson PB, Rebetzke GR, Condon AG (2015). Field Crops Research, Volume 183, November 01, 2015, Pages 102-110. DOI: 10.1016/j.fcr.2015.07.002
Of growing importance: combining greater early vigour and transpiration efficiency for wheat in variable rainfed environments
Wilson PB, Rebetzke GR, Condon AG (2015). Functional Plant Biology, 42(12) 1107-1115. DOI: 10.1071/FP15228 2.69
Examining the efficacy of a genotyping-by-sequencing technique for population genetic analysis of the mushroom Laccaria bicolor and evaluating whether a reference genome is necessary to assess homology
Wilson AW, Wickett NJ, Grabowski P, Fant J, Borevitz J, Mueller GM (2015). Mycologia, Volume 107, 2015 – Issue 1. DOI: org/10.3852/13-278
Improving photosynthesis and yield potential in cereal crops by targeted genetic manipulation: Prospects, progress and challenges
R.T. Furbank, W.P. Quick, X.R.R. Sirault (2015). Field Crops Research. DOI: 10.1016/j.fcr.2015.04.009
Detection of decay in fresh-cut lettuce using hyperspectral imaging and chlorophyll fluorescence imaging
I. Simko, J.A.J. Berni, R.T. Furbank (2015). Postharvest Biology and Technology. DOI: org/10.1016/j.postharvbio.2015.04.007
Feature matching in stereoimages encouraging uniform spatial distribution
X. Tan, C. Sun, X.R.R. Sirault, R.T. Furbank, T.D. Pham (2015). Pattern Recognition, Volume 48, Issue 8, August 2015, Pages 2530-2542. DOI: org/10.1016/j.patcog.2015.02.026
Genomic breeding for food, environment and livelihoods
J. Rivers, N. Warthmann, B.J. Pogson, J.O. Borevitz (2015). Food Security. DOI: 10.1007/s12571-015-0431-3
“Rolled-upness”: phenotyping leaf rolling in cereals using computer vision and functional data analysis approaches
Sirault XRR, Condon AG, Wood JT, Farquhar GD, Rebetzke GJ. (2015). Plant Methods. BioMed Central. DOI: org/10.1186/s13007-015-0095-1
Different NaCl-induced calcium signatures in the Arabidopsis thaliana ecotypes Col-0 and C24
Schmöckel, S. M., Garcia, A. F., Berger, B., Tester, M., Webb, A. A. R. & Roy, S. J. (2015). PLOS One, 10(2), 9 pages. DOI: org/10.1371/journal.pone.0117564
Study on spike detection of cereal plants
Qiongyan, L., Cai, J., Berger, B. & Miklavcic, S. (2015). In 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 (pp. 228-233). Institute of Electrical and Electronics Engineers Inc. DOI: 10.1109/ICARCV.2014.7064309
Integrating image-based phenomics and association analysis to dissect the genetic architecture of temporal salinity responses in rice
Campbell MT, Knecht AC, Berger B, Brien CJ, Wang D, Walia H (2015). Plant Physiology, June 2015, pp.00450. DOI: 10.1104/pp.15.00450
A model-based approach to recovering the structure of a plant from images
Ward B, Bastian J, van den Hengel A, Pooley D, Bari R, Berger B, Tester M (2015). arXiv:1503.03191v2 (Open access to e-prints in Physics, Mathematics, Computer Science, Quantitative Biology, Quantitative Finance and Statistics)
Utilization of a high-throughput shoot imaging system to examine the dynamic phenotypic responses of a C4 cereal crop plant to nitrogen and water deficiency over time
E.H. Neilson, A.M. Edwards, C.K. Blomstedt, B. Berger, B. Lindberg Møller and R.M. Gleadow (2015). Journal of Experimental Biology. DOI: 10.1093/jxb/eru526
Combining field performance with controlled environment plant imaging to identify the genetic control of growth and transpiration underlying yield response to water-deficit stress in wheat
Boris Parent, Fahimeh Shahinnia, Lance Maphosa, Bettina Berger, Huwaida Rabie, Ken Chalmers,Alex Kovalchuk, Peter Langridge and Delphine Fleury (2015). Journal of Experimental Botany. DOI: 10.1093/jxb/erv320
Comparison of Leaf Sheath Transcriptome Profiles with Physiological Traits of Bread Wheat Cultivars under Salinity Stress
Fuminori Takahashi, Joanne Tilbrook, Christine Trittermann, Bettina Berger, Stuart J. Roy, Motoaki Seki, Kazuo Shinozaki, Mark Tester (2015). PLOS One. DOI: 10.1371/journal.pone.0133322
Variation for N uptake system in maize: genotypic response to N supply
Garnett T, Plett D, Conn V, Conn S, Rabie H, Rafalksi A, Dhugga K, Tester M, Kaiser B. 2015. Frontiers in Plant Science, 09 November 2015. DOI: org/10.3389/fpls.2015.00936
Genetic approaches to enhancing nitrogen-use efficiency (NUE) in cereals: challenges and future directions
Garnett T, Plett D, Heuer S, Okamoto M. 2015. Functional Plant Biology 42(10): 921-941. DIO: doi.org/10.1071/FP15025
Flavonoids and auxin transport inhibitors rescue symbiotic nodulation in the Medicago truncatula cytokinin perception mutant cre1
Ng JLP, Hassan, S, Truong TT, Hocart CH, Laffont C, Frugier F, Mathesius U (2015). Plant Cell, 27: 2210-2226. DOI:
Genomic Diversity and Climate Adaptation in Brachypodium
Wilson PB, Streich JC, Borevitz JO (2015) Chapter in: Genetics and Genomics of Brachypodium. Ed: John Vogel, Springer International. DOI:
Partially dissecting the steady-state electron fluxes in Photosystem I in wild-type and pgr5 and ndh mutants of Arabidopsis
Kou, J., Takahashi, S., Fan, D. Y., Badger, M. R., & Chow, W. S. (2015). Frontiers in Plant Science, 17 September 2015. DOI: org/10.3389/fpls.2015.00758
PhenoMeter: a metabolome database search tool using statistical similarity matching of metabolic phenotypes for high-confidence detection of functional links
Carroll, A. J., Zhang, P., Whitehead, L., Kaines, S., Tcherkez, G., & Badger, M. R. (2015). Frontiers in Bioengineering and Biotechnology, 29 July 2015. DOI: org/10.3389/fbioe.2015.00106
Inhibiting plant microRNA activity: molecular SPONGEs, target MIMICs and STTMs all display variable efficacies against target microRNAs
Reichel M, Li Y, Li J and Millar AA (2015). Plant Biotechnology Journal, 13, 915-926. DOI:
Specificity of plant microRNA target MIMICs: Cross-targeting of miR159 and miR319
Reichel M and Millar AA (2015). Journal of Plant Physiology, 180, 45-48. DOI: org/10.1016/j.jplph.2015.03.010
Novel MtCEP1 peptides produced in vivo differentially regulate root development in Medicago truncatula
Nadiatul A. Mohd-Radzman1, Steve Binos2, Thy T. Truong3, Nijat Imin1, Michael Mariani, 2 and Michael A. Djordjevic1(2015). Journal of Experimental Botany, Volume 66, Issue 17, 1 August 2015, Pages 5289–5300. DOI: org/10.1093/jxb/erv008
Improving recombinant Rubisco biogenesis, plant photosynthesis and growth by coexpressing its ancillary RAF1 chaperone
Spencer M. Whitney, Rosemary Birch, Celine Kelso, Jennifer L. Beck, and Maxim V. Kapralov. PNAS 2015 112 (11) 3564-3569; published ahead of print March 2, 2015.DOI: 10.1073/pnas.1420536112
bHLH05 is an interaction partner of MYB51 and a novel regulator of glucosinolate biosynthesis in Arabidopsis
Frerigmann, H., Berger, B. & Gigolashvili, T. (2014). Plant Physiology, 166(1), 349-369. DOI: 10.1104/pp.114.240887
Expression of the Arabidopsis vacuolar H+-pyrophosphatase gene (AVP1) improves the shoot biomass of transgenic barley and increases grain yield in a saline field
Schilling, R. K., Marschner, P., Shavrukov, Y., Berger, B., Tester, M., Roy, S. J. & Plett, D. C. (2014). Plant Biotechnology Journal, 12(3), 378-386. DOI: 10.1111/pbi.12145
Review – Scaling of thermal images at different spatial resolution: The mixed pixel problem
HG Jones, XRR Sirault (2014). Agronomy, 2014, 4(3), 380-396. DOI: 10.3390/agronomy4030380
Image-based phenotyping for non-destructive screening of different salinity tolerance traits in rice
Aris Hairmansis, Bettina Berger, Mark Tester, Stuart John Roy (2014). The Rice Journal, 2014, 7:16. DOI: 10.1186/s12284-014-0016-3
High-Throughput Phenotyping to Detect Drought Tolerance QTL in Wild Barley Introgression Lines
Honsdorf N, March TJ, Berger B, Tester M, Pillen K (2014). PLoS ONE, 9(5): e97047. DOI: 10.1371/journal.pone.0097047
TraitCapture: Genomic and environment modelling of plant phenomic data
Brown TB, Cheng R, Sirault XRR, Rungrat T, Murray KD, Trtilek M, Furbank RT, Badger M, Pogson BJ, Borevitz JO (2014). Current Opinion in Plant Biology 2014, 18:73-79. DOI: org/10.1016/j.pbi.2014.02.002
Digital imaging approaches for phenotyping whole plant nitrogen and phosphorus response in Brachypodium distachyon
Poiré R, Chochois V, Sirault XRR, Vogel JP, Watt M, Furbank RT (2014). Journal of Integrative Plant Biology. DOI:
Transplastomic integration of a cyanobacterial bicarbonate transporter into tobacco chloroplasts
Pengelly J, Förster B, von Caemmerer S, Badger M, Price G, Whitney S (2014). Journal of Experimental Botany, Volume 65, Issue 12, 1 July 2014, pages 3071–3080. DOI: org/10.1093/jxb/eru156
Online oxygen kinetic isotope effects using membrane inlet mass spectrometry can differentiate between oxidases for mechanistic studies and calculation of their contributions to oxygen consumption in whole tissues
Cheah MH, Millar AH, Myers RC, Day DA, Roth J, Hillier W, Badger MR (2014). Analytical chemistry, 2014, 86 (10), pp 5171–5178. DOI: 10.1021/ac501086n
A novel P700 redox kinetics probe for rapid, non‐intrusive and whole‐tissue determination of photosystem II functionality, and the stoichiometry of the two photosystems in vivo
Jia H, Dwyer SA, Fan DY, Han Y, Badger MR, von Caemmerer S, Chow WS (2014). Physiol Plantarum, 152: 403–413. DOI:
Wheat variability in photosynthetic capacity and efficiency for increased yield potential
Viridiana Silva-Pérez1, John R. Evans1, Gemma Molero3, Tony Condon2, Robert Furbank2, Matthew Reynolds (2014). Proceedings of the IV International Wheat Yield Consortium, CIMMYT Mexico, page 145 of pdf or 154 of document page numbering.
Leaf hyperspectral reflectance spectra as a tool to measure photosynthetic characters in wheat
Viridiana Silva-Pérez1, John R. Evans1, Gemma Molero3, Tony Condon2, Robert Furbank2, Matthew Reynolds (2014). Proceedings of the IV International Wheat Yield Consortium, CIMMYT Mexico, page 154 of pdf or 163 of document page numbering.
A Sinorhizobium meliloti-specific N-acyl homoserine lactone quorum-sensing signal increases nodule numbers in Medicago truncatula independent of autoregulation
Veliz-Vallejos DF, van Noorden GE, Mengqi Y and Mathesius U (2014). Frontiers in Plant Science, 5: 551, 14 October 2014. DOI: org/10.3389/fpls.2014.00551
The 2HA line of Medicago truncatula has characteristics of an epigenetic mutant that is weakly ethylene insensitive
Kurdyukov S, Mathesius U, Nolan KE, Sheahan MB, Goffard N, Carroll BJ, Rose RJ (2014). BMC Plant Biology 2014, 14:174. DOI: org/10.1186/1471-2229-14-174
Expression of the Arabidopsis vacuolar H+-pyrophosphatase gene (AVP1) improves the shoot biomass of transgenic barley and increases grain yield in a saline field
Schilling RK, Marschner P, Shavrukov Y, Berger B, Tester M, Roy SJ, Plett DC (2013). Plant Biotechnol J, 12: 378–386. DOI:
Accounting for variation in designing greenhouse experiments with special reference to greenhouses containing plants on conveyer systems
Brien CJ, Berger B, Rabie H, Tester M (2013). Plant Methods 2013, 9:5. DOI: org/10.1186/1746-4811-9-5
Germanium as a tool to dissect boron toxicity effects in barley and wheat
Hayes JE, Pallotta M, Baumann U, Berger B, Langridge P, Sutton T (2013). Functional Plant Biology 40(6) 618-627. DOI: org/10.1071/FP12329
A holistic high-throughput screening framework for biofuel assessment that characterises variations in soluble sugar and cell wall composition in Sorghum bicolour
Martin AP, Palmer WM, Byrt CS, Furbank RT, Grof CPL (2013). Biotechnology for Biofuels 2013, 6:186. DOI: org/10.1186/1754-6834-6-186
PlantScan: A three-dimensional phenotyping platform for capturing the structural dynamic of plant development and growth
Sirault,X, Fripp, J, Paproki A, Guo J, Kuffner P, Daily H, Li R, Furbank R (2013). In ‘7th International Conference on Functional-Structural Plant Models. Saariselka, Finland’, 9-14 June 2013. (Eds R Sievanen, E Nikinmaa, C Godin, A Lintunen, P Nygren) pp. 45-48. ISBN 978-951-651-408-9
Resiliences to water deficit in a phenotyping platform and in the field: How related are they in maize?
Chapuis R, Delluc C, Debeuf R, Tardieu F, Welcker C (2011). European Journal of Agronomy, Volume 42, October 2012, pages 59-67. DOI: org/10.1016/j.eja.2011.12.006
A novel mesh processing based technique for 3D plant analysis
Paproki A, Sirault X, Berry S, Furbank R, Fripp J (2012). BMC Plant Biology 2012, 12:63. DOI: org/10.1186/1471-2229-12-63
Natural Genetic Variation for Growth and Development Revealed by High-Throughput Phenotyping in Arabidopsis thaliana
Zhang X, Hause Jr RJ and Borevitz JO (2012).
C4 Plants as biofuel feedstocks: Optimising biomass production and feedstock quality from a lignocellulosic perspective
Byrt CS, Grof CPL, Furbank, RT (2011). Journal of Integrative Plant Biology, 53, 120-135. DOI:
Phenomics – technologies to relieve the phenotyping bottleneck
Furbank RT, Tester M (2011). Trends in Plant Science, Volume 16, Issue 12, December 2011, Pages 635-644. DOI: org/10.1016/j.tplants.2011.09.005
Accurate inference of shoot biomass from high-throughput images of cereal plants
Golzarian MR, Frick RA, Rajendran K, Berger B, Roy S, Tester M, Lun DS (2011). Plant Methods, 2011, 7:2. DOI: org/10.1186/1746-4811-7-2
Raising yield potential of wheat. II. Increasing photosynthetic capacity and efficiency
Parry MAJ, Reynolds M, Salvucci ME, Raines C, Andralojc PJ, Zhu X, Price D, Condon AG and Furbank RT (2011). Journal of Experimental Botany, Volume 62, Issue 2, 1 January 2011, pages 453–467. DOI: org/10.1093/jxb/erq304
Functional analysis of corn husk photosynthesis
Pengelly JJL, Kwasny S, Bala S, Evans JR, Voznesenskaya EV, Koteyeva NK, Edwards GE, Furbank RT and von Caemmerer S. (2011). . DOI:
Raising yield potential of wheat. I. Overview of a consortium approach and breeding strategies
Reynolds M, Bonnett D, Chapman SC, Furbank RT, Manes Y, Mather DE and Parry MAJ (2011). Journal of Experimental Botany, Volume 62, Issue 2, 1 January 2011, pages 439–452. DOI: org/10.1093/jxb/erq311
A SOS3 homologue maps to HvNax4, a barley locus controlling an environmentally-sensitive Na+ exclusion trait
Rivandi J, Miyazaki J, Hrmova M, Pallotta M, Tester M, Collins NC (2010). Journal of Experimental Botany, Volume 62, Issue 3, 1 January 2011, pages 1201–1216. DOI: org/10.1093/jxb/erq346
Genetic analysis of abiotic stress tolerance in crops
Roy SJ, Tucker EJ, Tester M (2011). Current Opinion in Plant Biology, Volume 14, Issue 3, June 2011, Pages 232-239. DOI: org/10.1016/j.pbi.2011.03.002
High-throughput shoot imaging to study drought responses
Berger B, Parent B, Tester MA (2010). Journal of Experimental Botany 61: 3519-3528. DOI: org/10.1093/jxb/erq201
The MetabolomeExpress Project: enabling web-based processing, analysis and transparent dissemination of GC/MS metabolomics datasets
Carroll AJ, Badger MR, Millar AH (2010). BMC Bioinformatics, 11: 376. PDF
Sodium exclusion QLT associated with improved seedling growth in bread wheat under salinity stress
Genc Y, Oldach K, Verbyla AP, Lott G, Hassan M, Tester M, Wallwork H, McDonald GK (2010). Theoretical and Applied Genetics, 121: 877-894. DOI: 10.1007/s00122-010-1357-y
A water-centred framework to assess the effects of salinity on the growth and yield of wheat and barley
Harris BN, Sadras VO, Tester MA (2010). Plant and Soil. DOI: doi.org/10.1007/s11104-010-0489-9
PODD: an ontology-driven data repository for collaborative phenomics research
Li Y-F, Kennedy G, Davies F, Hunter J (2010). School ITEE, The University of Queensland. The Role of Digital Libraries in a Time of Global Change. ICADL 2010. Lecture Notes in Computer Science, vol 6102. Springer, Berlin, Heidelberg. DOI: org/10.1007/978-3-642-13654-2_22
New phenotyping methods for screening wheat and barley for beneficial responses to water deficit
Munns R, James RA, Sirault XRR, Furbank RT, Jones HG (2010). Journal of Experimental Botany, 61: 3499-3507. DOI: org/10.1093/jxb/erq199
Growth of the C4 dicot Flaveria bidentis: photosynthetic acclimation to low light through shifts in leaf anatomy and biochemistry
Pengelly JJL, Sirault XRR, Tazoe Y, Evans JR, Furbank RT, von Caemmerer S. (2010). Journal of Experimental Botany, 61: 4109-4122. DOI: org/10.1093/jxb/erq226
HvNax3 – a locus controlling shoot sodium exclusion derived from wild barley (Hordeum vulgare ssp. spontaneum)
Shavrukov Y, Gupta NK, Miyazaki J, Baho MN, Chalmers KJ, Tester M, Langridge P, Collins, NC (2010). Functional & Integrative Genomics 10: 277-291. DOI: org/10.1007/s10142-009-0153-8
Breeding technologies to increase crop production in a changing world
Tester MA, Langridge P (2010). Science, Vol. 327, Issue 5967, pp. 818-822. DOI: 10.1126/science.1183700
Finkel, E. (2009). Science 325, 380-381.
Plant phenomics: from gene to form and function.
Furbank, R.T., ed. (2009), Functional Plant Biology, Volume 36 Number 10 & 11, Special Issue.
C4 rice: a challenge for plant phenomics.
Furbank RT, von Caemmerer S, Sheehy J and Edwards G (2009), Functional Plant Biology, 2009. 36, 845–856. DOI: org/10.1071/FP09185
Quantifying the three main components of salinity tolerance in cereals.
Rajendran K, Tester MA, Roy SJ (2009), Plant, Cell and Environment 32:237-249. DOI:
A new screening method for osmotic component of salinity tolerance in cereals using infrared thermography.
Sirault XRR, James RA and Furbank RT, (2009), Functional Plant Biology. DOI: org/10.1071/FP09182
Resource for reference ontologies, plant genomics and phenomics
The Planteome database: An integrated resource for reference ontologies, plant genomics and phenomics
The Planteome is a unique resource for both basic plant biology researchers such as evolutionary or molecular biologists and geneticists, and also for plant breeders who are interested in selecting for various traits of interest. The novel aspect of the Planteome lies in the semantic strength of the integrated ontology network, which can be traversed computationally. Planteome allows plant scientists in various fields to identify traits of interest, and locate data, including germplasm, QTL and genes associated with a given trait, and can help in building hypotheses, confirming observations, data sharing and inter- and intra-specific comparisons.
The Planteome project provides a suite of reference and species-specific ontologies for plants and annotations to genes and phenotypes. Ontologies serve as common standards for semantic integration of a large and growing corpus of plant genomics, phenomics and genetics data. The reference ontologies include the Plant Ontology, Plant Trait Ontology and the Plant Experimental Conditions Ontology developed by the Planteome project, along with the Gene Ontology, Chemical Entities of Biological Interest, Phenotype and Attribute Ontology, and others.
The project also provides access to species-specific Crop Ontologies developed by various plant breeding and research communities from around the world. It provides integrated data on plant traits, phenotypes, and gene function and expression from 95 plant taxa, annotated with reference ontology terms. The Planteome project is also developing a plant gene annotation platform; Planteome Noctua, to facilitate community engagement. All the Planteome ontologies are publicly available and are maintained at the Planteome GitHub site for sharing, tracking revisions and new requests. The annotated data are freely accessible from the ontology browser and the data repository. Read more.
Learn more about plant phenotyping or utilise these tools to teach and nuture our next generation of research scientists
Education and teaching aids
Papers on phenotyping and related topics
The following papers will provide further information on phenotyping and related topics. If you wish to suggest topics and references please contact us.
Towards recommendations for metadata and data handling in plant phenotyping
Krajewski P, Chen DJ, Cwiek H, van Dijk ADJ, Fiorani F, Kersey P, Klukas C, Lange M, Markiewicz A, Nap JP, van Oeveren J, Pommier C, Scholz U, van Schriek M, Usadel B, Weise S (2015). Journal of Experimental Botany, 66, 5417-5427. DOI: org/10.1093/jxb/erv271
Agronomic data: advances in documentation and protocols for exchange and use
Hunt LA, White JW, Hoogenboom G. (2001). Agricultural Systems, 70, 477-492. DOI: org/10.1016/S0308-521X(01)00056-7
Field-based phenomics for plant genetics research
White JW, Andrade-Sanchez P, Gore MA, Bronson KF, Coffelt TA, Conley MM, Feldmann KA, French AN, Heun JT, Hunsaker DJ, Jenks MA, Kimball BA, Roth RL, Strand RJ, Thorp KR, Wall GW, Wang G (2012). Field Crops Research, 133, 101-112. DOI: org/10.1016/j.fcr.2012.04.003
Field high-throughput phenotyping: the new crop breeding frontier
Araus J.L. & Cairns J.E. (2014). Trends in Plant Science, 19, 52-61. DOI: org/10.1016/j.tplants.2013.09.008
Field high-throughput phenotyping: The new crop breeding frontier
Araus JL, Cairns JE (2014). Trends in Plant Science vol 19 issue 1, 52-61. DOI: org/10.1016/j.tplants.2013.09.008
A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects
Arvidsson S, Perez-Rodriguez P, Mueller-Roeber B. (2011). New Phytologist, 191, 895-907.
Integrating image-based phenomics and association analysis to dissect the genetic architecture of temporal salinity responses in rice
Campbell MT, Knecht AC, Berger B, Brien CJ, Wang D, Walia H (2015). Plant Physiology 168(4), 1476-1489. DOI: 10.1104/pp.15.00450
Dissecting spatiotemporal biomass accumulation in barley under different water regimes using high-throughput image analysis
Neumann K, Klukas C, Friedel S, Rischbeck P, Chen D, Entzian A, Stein N, Graner A, Kilian B (2015). Plant Cell and Environment, 38, 1980-1996.
Lights, camera, action: high-throughput plant phenotyping is ready for a close-up
Fahlgren N, Gehan MA, Baxter I (2015). Current Opinion in Plant Biology, 24, 93-99. DOI: org/10.1016/j.pbi.2015.02.006
Remote sensing of vegetation: principles, techniques, and applications
Jones HG, Vaughan RA (2010). Oxford University Press, New York.
A versatile phenotyping system and analytics platform reveals diverse temporal responses to water availability in setaria
Fahlgren N, Feldman M, Gehan Malia A, et al. (2015). Molecular Plant, 8, 1520-1535. DOI: org/10.1016/j.molp.2015.06.005
Integrated analysis platform: An open-source information system for high-throughput plant phenotyping
Klukas C, Chen D, Pape J-M (2014). Plant Physiology, 165, 506-518. DOI: org/10.1104/pp.113.233932
The iPlant collaborative: cyberinfrastructure for plant biology
Goff SA, Vaughn M, McKay S, et al. (2011). Frontiers in Plant Science, 2. DOI: org/10.3389/fpls.2011.00034
A semi-automatic system for high throughput phenotyping wheat cultivars in-field conditions: description and first results
Comar A, Burger P, de Solan B, Baret F, Daumard F, Hanocq J-F (2012). Functional Plant Biology, 39, 914-924. DOI: org/10.1071/FP12065
High-throughput non-destructive biomass determination during early plant development in maize under field conditions
Montes JM, Technow F, Dhillon BS, Mauch F, Melchinger AE (2011). Field Crops Research, 121, 268-273. DOI: org/10.1016/j.fcr.2010.12.017
Development and evaluation of a field-based high-throughput phenotyping platform
Andrade-Sanchez P, Gore MA, Heun JT, Thorp KR, Carmo-Silva AE, French AN, Salvucci ME, White JW (2014). Functional Plant Biology, 41, 68-79. DOI: org/10.1071/FP13126
BreedVision – a multi-sensor platform for non-destructive field-based phenotyping in plant breeding
Busemeyer L, Mentrup D, Möller K, Wunder E, Alheit K, Hahn V, Maurer H, Reif J, Würschum T, Müller J, Rahe F, Ruckelshausen A (2013). Sensors, 13, 2830. DOI: 10.3390/s130302830
Development of a field-based high-throughput mobile phenotyping platform
Barker J, Zhang N, Sharon J, Steeves R, Wang X, Wei Y, Poland J (2016). Comput. Electron. Agric., 122, 74-85. DOI: org/10.1016/j.compag.2016.01.017
Quantification of plant stress using remote sensing observations and crop models: the case of nitrogen management
Baret F, Houles V, Guerif M (Jan 2007). Journal of Experimental Botany 58(4): 869-880, DOI: 10.1093/jxb/erl231
Yield-trait performance landscapes: from theory to application in breeding maize for drought tolerance
Messina CD, Podlich D, Dong ZS, Samples M, Cooper M (Nov 2010), Journal of Experimental Botany 62(3): 855-868. DOI: 10.1093/jxb/erq329
Future scenarios for plant phenotyping
Fiorani F, Schurr U (Feb 2013). Annual Review of Plant Biology 64: 267-291, DOI: 10.1146/annurev-arplant-050312-120137
Phenomics – technologies to relieve the phenotyping bottleneck
Furbank RT, Tester M (Nov 2011). Trends in Plant Science, 16, 635-644.10, DOI: 1016/j.tplants.2011.09.005
Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle
Berni JAJ, Zarco-Tejada PJ, Suarez L, Fereres E (2009). IEEE Transactions on Geoscience and Remote Sensing, 47, 722-738. DOI: 10.1109/TGRS.2008.2010457
Unmanned aerial systems for photogrammetry and remote sensing: A review
Colomina I, Molina P (2014). Isprs Journal of Photogrammetry and Remote Sensing, 92, 79-97. DOI: org/10.1016/j.isprsjprs.2014.02.013
Helping students learn about plant phenomics
Phenomics is an area of biology concerned with the measurement of phenomes — the physical and biochemical traits of organisms — as they change in response to genetic mutation and environmental influences. The phenome being all the possible phenotypes of an organism and the phenotype being the observable characteristics of an organism.
Captured phenomics data enables the more rapid discovery of molecular markers and faster germplasm development, aimed at improving crop yields including the tolerance of major crops and other agriculturally important plants to biotic and abiotic stresses such as drought, salinity and a broad spectrum of plant diseases.
Plant phenomics specifically was defined by Furbank and Tester (2011) as:
“Plant phenomics is the study of plant growth, performance and composition. Forward phenomics uses phenotyping tools to ‘sieve’ collections of germplasm for valuable traits. The sieve or screen could be high-throughput and fully automated and low resolution, followed by higher-resolution, lower-throughput measurements. Screens might include abiotic or biotic stress challenges and must be reproducible and of physiological relevance. Reverse phenomics is the detailed dissection of traits shown to be of value to reveal mechanistic understanding and allow exploitation of this mechanism in new approaches. This can involve reduction of a physiological trait to biochemical or biophysical processes and ultimately a gene or genes.”
Furbank RT & Tester M (2011) Phenomics – technologies to relieve the phenotyping bottleneck. Trends in Plant Science, 16, 635-644.
Who undertakes phenotyping?
A whole range of people carry out phenotyping, even if they don’t call it that. Plant breeders, farmers, growers, agronomists, viticulturists, plant scientists, ecologists and environmental scientists all do phenotyping in various forms.
The recent interest in phenomics has been in part due to the rapid development of the other -omics technologies: genomics, metabolomics, transcriptomics, and proteomics. The cost of these technologies has decreased enough to allow routine use, which has in turn led to a “phenotyping bottleneck”, a need to be able to better understand gene function and plant response to environment.
However, phenomics has been around for a long time and is fundamental to plant science. There are very few papers published by plant scientists that don’t incorporate some phenomics (e.g. plant biomass measurements). Precision agriculture is heavily dependent on measuring plant performance. Growers are increasingly using new technologies to measure plant growth and yield. Plant breeders are dependent on good phenomics in measuring yield and quality traits in new varieties.
Modern phenomics is simply using new technologies to do what was done previously with greater accuracy or with greater ease, and to measure aspects of plant growth and physiology that previously were not possible or were not possible with high throughput and precision.
According to the Food and Agriculture Organization of the United Nations (FAO), an expected population increase to 9 billion by 2050 will require our food production to double (1). As one of the most food secure nations in the world, Australia will need to play a major role in contributing to the food production demand.
Malnutrition and undernutrition as well as overweight and obesity impose high economic and social costs on countries at all income levels (2). Whilst the human and social consequences of poor nutrition are immeasurable, the costs of undernutrition and micronutrient deficiencies are estimated at 2-3 percent of global GDP, equivalent to US$1.4-2.1 trillion per year. The economic costs of overweight and obesity were estimated to be about US$1.4 trillion in 2010 (3).
Crop production is aimed at increasing the quality and quantity of yield. This is a major challenge in the face of climate change, declining arable land and biotic and abiotic plant stresses. In addition, the overuse of fertilisers is causing environmental pollution.
Australia is one of the most food secure nations. Approximately 60% of our food production is exported to other countries. The food production export value in 2011/12 was $30.5 billion (4).
1 High level exert forum “How to feed the world 2050”, Rome, October 2009 http://www.fao.org/wsfs/forum2050/en/
2 FAO (Food and Agriculture Organization of the United Nations), “The State of Food and Agriculture”, 2013, p. ix
3 FAO (Food and Agriculture Organization of the United Nations), “The State of Food and Agriculture”, 2013, p. ix
4 Australian Government, Department of Agriculture, Food and Fisheries, “Australian Food Statistics 2011-12, p.2
Plant phenomics is a science that has the power to transform our lives. By exploring how the genetic makeup of an organism determines its appearance, function and performance, phenomics can help us tackle these pressing challenges.
With a rapidly growing world population, a transformational advance in grain production must occur to increase yield by 50-60% to meet projected global food demand. Groundwater sources are declining around the world, increasing soil salinity and causing losses in crop production and grazing land in many countries.
These global production issues are particularly pertinent to Australia which faces long periods of heat, drought and increasing salinity, undermining farm productivity. Increasing the yield in crops, particularly in these marginal environmental conditions, using novel approaches that exploit robotics, machine learning, computer vision and genetics technologies will significantly increase global food quality and production, and reduce environmental degradation.
Australia is in an outstanding position in plant science. Australian agriculture is amongst the most innovative in the world and has tremendous growth opportunities, and Australian farmers are highly qualified and eager to adopt novel systems for efficient and sustainable farming. This is an exciting time for plant phenomics and plant science.
The Plant Phenomics Teacher Resource booklet and Powerpoint presentation
The Plant Phenomics Teacher Resource provides background knowledge about plant phenomics research and technology for upper secondary school teachers. The resource describes current Australian plant phenomics research and the technology behind the research, in the form of short background ‘briefing notes’ with accompanying images. We hope it helps you create interest among your students in this new and important field of science.
Download the resources here: