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 data is published online for reuse and developed algorithms are shared in open source environments.
Linking of phenomics information with the APPF’s modelling capabilities, 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.
Our statistical workhorse is ASReml-R, the R-language implementation of the ASReml statistical software package (proprietary). R users can access it as the asreml package, but will require a software licence.
The Australian Plant Phenomics Facility supplements asreml with three R packages written by Dr Chris Brien (APPF Adelaide node):
- asremlPlus – extra functionality for asreml
- dae – software for designing and analysing experiments
- imageData – software for analysing data produced by the Lemna-Tec system
These 3 packages can be downloaded freely from CRAN or from Chris’s website.
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 phenotyping projects run at 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
To transform phenotyping data into information, the APPF has developed a collaborative e-infrastructure platform called phenoSMART®. This platform allows the user to easily extract information and value from the data collected using phenotyping tools. The architecture of the platform also serves as a base to allow computational tools developed by other research groups across Australia to be made available to others and/or the agro-business sector.
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.
Photosynthetic variation and responsiveness to CO2 in a widespread riparian tree
Dillon S, Quentin A, Ivković M, Furbank RT, Pinkard E (Jan 2018). PLOS One 13 (1), DOI: org/10.1371/journal.pone.0189635
High throughput determination of plant height, ground cover and above-ground biomass in wheat with LiDAR
Jimenez-Berni J, Deery D, Rozas-Larraondo P, Condon A, Rebetzke G, James R, Bovill W, Furbank R, Sirault X (Feb 2018). Frontiers in Plant Science 9:237, DOI: 10.3389/fpls.2018.00237
Rice Functional Genomics Research: Past Decade and Future
Li Y, Xiao J, Chen L, Huang X, Cheng Z, Han B, Zhang Q, Wu C (Feb 2018). Molecular Plant, DOI: org/10.1016/j.molp.2018.01.007
On the utilization of novel spectral laser scanning for three-dimensional classification of vegetation elements
Li Z, Schaefer M, Strahler A, Schaaf C, Jupp D (Feb 2018).
The discovery of the virulence gene ToxA in the wheat and barley pathogen Bipolaris sorokiniana
McDonald MC, Ahren D, Simpfendorfer S, Milgate A, Solomon PS, (Feb 2018), Molecular Plant Pathology 19 (2): 432–439, DOI:
Genomic diversity guides conservation strategies among rare terrestrial orchid species when taxonomy remains uncertain
Ahren CW, Supple MA, Aitken NC, Cantrill DJ, Borevitz JO, James EA (Mar 2017), Annals of Botany, Volume 119, Issue 8, 1 June 2017, Pages 1267–1277. DOI: org/10.1093/aob/mcx022
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
Chloroplast function and ion regulation in plants growing on saline soils: Lessons from halophytes
Bose J, Munns R, Shabala S, Gilliham M, Pogson BJ, Tyerman SD (Jun 2017), Journal of Experimental Botany, Volume 68, Issue 12, 1 June 2017, Pages 3129–3143, DOI: org/10.1093/jxb/erx142
Ecovr: Visualizing real ecosystems and big data in virtual reality
Brown TB, Koch A, Wolba Z, Janson A, Kookana A, Liu Y Stobo I, Qiu Z, Urwin T, Borevitz J (2017), SIGGRAPH Conference 2017, Los Angeles
The sensitivity of photosynthesis to O2 and CO2 concentration identifies strong Rubisco control above the thermal optimum
Busch FA, Sage R F (Feb 2017), New Phytologist 213: 1036–1051, DOI:
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
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 (Jun 2017), IEEE Xplore, DOI: 10.1109/CCECE.2017.7946593
The tomato / gene for Fusarium wilt resistance encodes an atypical leucine-rich repeat receptor-like protein whose function is nevertheless dependent on SOBIR1 and SERK3/BAK1
Catanzariti A-M, Do HTT, Bru P, de Sain M, Thatcher LF, Rep M, Jones DA (Mar 2017), The Plant Journal 89, 6: 1195-1209, DOI: 10.1111/tpj.13458
Novel resampling improves statistical power for multiple-trait QTL mapping
Cheng R, Doerge RW, Borevitz J (Mar 2017), , DOI: 10.1534/g3.116.037531
Functional Genomics-guided discovery of a light activated phytotoxin in the wheat pathogen Parastagonospora nodorum via pathway activation
Chooi Y-H, Zhang G, Hu J, Muria-Gonzalez MJ, Tran P, Pettitt A, Maier A, Barrow RA, Solomon PS ( May 2017), Environmental Microbiology, 19 (5): 1975-1986, DOI: 10.1111/1462-2920.13711
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
Relationship between hydraulic and stomatal conductance and its regulation by root and leaf aquaporins under progressive water stress and recovery and exogenous application of ABA in Vitis vinifera L. ‘Syrah’
Dayer S, Tyerman SD, Garnett T, Pagay V. (Dec 2017), Acta Horticulturae 1188, 227-234, DOI: 10.17660/ActaHortic.2017.1188.29
Changes in the chloroplastic CO2 concentration explain much of the observed Kok effect: A model
Farquar GD, Busch FA, (Apr 2017), New Phytologist 214, 2: 570-584, DOI: 10.1111/nph.14512
Differential expression of microRNAs and potential targets under drought stress in barley
(Jan 2017), Plant, Cell & Environment, DOI:
Drought-inducible expression of Hv-miR827 enhances drought tolerance in transgenic barley
Ferdous J, Whitford R, Nguyen M et al. (May 2017), Functional & Integrative Genomics 17:2-3 pp 279-292, DOI: org/10.1007/s10142-016-0526-8
Water and temperature stress define the optimal flowering period for wheat in south-eastern Australia
Flohr BM, Hunt JR, Kirkegaard JA, Evans JR (Aug 2017), Field Crops Research 209:108-119, DOI: org/10.1016/j.fcr.2017.04.012
A MEM1-like motif directs mesophyll cell-specific expression of the gene encoding the C4 carbonic anhydrase in Flaveria
Gowik U, Schulze S, Saladie M, Rolland V, Tanz SK, Westhoff P, Ludwig M (Jan 2017), Journal of Experimental Botany 68(2): 311-320, DOI: org/10.1093/jxb/erw475
Carbon dioxide and water transport through plant aquaporins: CO2 and water transport through plant aquaporins
Groszmann M, Osborn HL, and Evans JR, (Jun 2017), Plant, Cell & Environment 40(6) pp 938-961, DOI: 10.1111/pce.12844
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:
Cellulose synthesis and cell expansion are regulated by different mechanisms in growing Arabidopsis hypocotyls
Ivakov A, Flis A, Apelt F, Funfgeld F, Scherer U, Stitt M, Kragler F, Vissenberg K, Persson S, Suslov D (Jun 2017), The Plant Cell , DOI: 10.1105/tpc.16.00782
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
Development of the first consensus genetic map of intermediate wheatgrass (Thinopyrum intermedium) using genotyping-by-sequencing
Kantarski T, Larson S, Zhang X, DeHaan L, Borevitz J, Anderson J, Poland J, (Jan 2017), Theoretical and Applied Genetics 130(1) pp 137-150, DOI: org/10.1007/s00122-016-2799-7
Ethylene signalling is important for isoflavonoid meditated resistance to Rhizoctonia solani in Medicago truncatula
Liu Y, Hassan S, Kidd BN, Garg G, Mathesius U, Singh KB, Anderson JP (Sep 2017), Molecular Plant-Microbe Interactions 30 (9): 691-700, DOI: 10.1094/MPMI-03-17-0057-R
Crops in Silico: Generating virtual crops using an integrative and multi-scale modelling platform
Marshall-Colon A, Long SP, Allen DK, Allen G, Beard DA, Benes B, Von Caemmerer S, Christensen AJ, Cox DJ, Hart JC, Hirst PM, Kannan K, Katz DS, Lynch J, Millar AJ, et al. (May 2017), Frontiers in Plant Science Vol 8 pg 786, DOI: 10.3389/fpls.2017.00786
Diurnal solar energy conversion and photoprotection in rice canopies
Meacham K, Sirault X, Quick WP, von Caemmerer S, Furbank R (Jan 2017), Plant Physiology 173 (1):495–508, DOI: org/10.1104/pp.16.01585
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
Leaf photosynthetic parameters related to biomass accumulation in a global rice diversity survey
Mingnan Q, Zheng G, Hamdani S, Essemine J, Song Q, Wang H, Chu C, Sirault X, Zhu X (Sep 2017), Plant Physiology 175 (1) 248–258, DOI:
kWIP: The k-mer weighted inner product, a de novo estimator of genetic similarity
Murray KD, Webers C, Ong CS, Borevitz J, Warthmann N (Sep 2017), PLoS Computational Biology 13(9), DOI: 10.1371/journal.pcbi.1005727
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
Photosynthesis: ancient, essential, complex, diverse … and in need of improvement in a changing world
Niinemets Ü, Berry JA, von Caemmerer S, Ort DR, Parry MAJ, Poorter H (Jan 2017), New Phytologist 213(1): 43-47, DOI: 10.1111/nph.14307
Variation in leaf respiration rates at night correlates with carbohydrate and amino acid supply
O’Leary BM, Lee CP, Atkin OK, Cheng R, Brown TB, Millar AH (Aug 2017), Plant Physiology 174: 2261-2273, DOI: org/10.1104/pp.17.00610
Effects of reduced carbonic anhydrase activity on CO2 assimilation rates in Setaria viridis: a transgenic analysis
Osborn HL, Alonso-Cantabrana H, Sharwood RE, Covshoff S, Evans JR, Furbank RT, von Caemmerer S (Jan 2017), Journal of Experimental Botany 68(2) pp 299-310, DOI: org/10.1093/jxb/erw357
A chloroplast retrograde signal, 3’-phosphoadenosine 5’-phosphate, acts as a secondary messenger in abscisic acid signaling in stomatal closure and germination
Pornsiriwong W, Estavillo GM, Chan KX, Tee EE, Ganguly D, Crisp PA, Phua SY, Zhao C, Qiu J, Park J, Yong MT, Nisar N, Yadav AK, Schwessinger B, Rathjen J, Cazzonelli CI, Wilson PB, Gilliham M, Chen Z-H, Pogson BJ (Mar 2017), eLife 2017;6:e23361, DOI: 10.7554/eLife.23361
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
The combination of gas-phase fluorophore technology and automation to enable high-throughput analysis of plant respiration
Scafaro AP, Negrini ACA, O’Leary B, Millar AH, Ahmed Rashid FA, Hayes L, Fan Y, Zhang Y, Chochois V, Badger MR, Atkin OK (Mar 2017), Plant Methods 13:16, DOI: 10.1186/s13007-017-0169-3
Biochemical Model of C3 photosynthesis applied to wheat at different temperatures
Silva-Perez V, Furbank RT, Condon AG, Evans JR (Aug 2017), Plant, Cell & Environment, 40: 1552–1564, DOI:
Phenomic approaches and tools for phytopathologists
Simko I, Jimenez-Berni JA, Sirault XRR (Jan 2017), Phytopathology 107 (1):6–17, DOI: org/10.1094/PHYTO-02-16-0082-RVW
Have we finally opened the door to understanding Septoria tritici blotch disease in wheat?
Solomon PS (Apr 2017), New Phytologist 214(2): 493-495, DOI: 10.1111/nph.14502
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
Measuring CO2 and HCO3− permeabilities of isolated chloroplasts using a MIMS-18O approach
Tolleter D, Chochois V, Poiré R, Price GD, Badger MR (Jun 2017), Journal of Experimental Botany, Volume 68, Issue 14, 1 June 2017, pages 3915–3924, DOI: org/10.1093/jxb/erx188
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
Convergence of mitochondrial and chloroplastic ANAC017/PAP-dependent retrograde signalling pathways and suppression of programmed cell death
van Aken O, Pogson BJ (May 2017), Cell Death and Differentiation, 24(6): 955-960, DOI: 10.1038/cdd.2017.68
C4 photosynthesis: 50 years of discovery and innovation
von Caemmerer S, Ghannoum O, Furbank R, (Jan 2017), Journal of Experimental Botany volume 68, issue 2, 1 January 2017, pages 97–102. DOI: 10.1093/jxb/erw491
The Transcription factor MYB29 is a regulator of ALTERNATIVE OXIDASE 1
Zhang X, Vandepoele K, Radomiljac JD, Chan KX, Pogson BJ, et al. (Feb 2017), Plant Physiology , DOI: 10.1104/pp.16.01494
Multiphase experiments in practice: A look back
Brien CJ (Dec 2017), Australian & New Zealand Journal of Statistics 59: 327–352, DOI:10.1111/anzs.12221
Nitrate uptake and its regulation in relation to improving nitrogen use efficiency in cereals
Plett DCHoltham LR,Okamoto M, Garnett TP (Aug 2017), Seminars in Cell & Developmental Biology, DOI: org/10.1016/j.semcdb.2017.08.027
Salinity tolerance loci revealed in rice using high-throughput non-invasive phenotyping
Al-Tamimi N, Brien C, Oakey H, Berger B, Saade S, Ho YS, Schmöckel SM, Tester M, Negrao S (Nov 2016), Nature Communications 7, DOI: 10.1038/ncomms13342
Using phenocams to monitor our changing Earth: toward a global phenocam network
Brown TB, Hultine KR, Steltzer H, Denny EG, Denslow MW, Granados J, Henderson S, Moore D, Nagai S, SanClements M, Sánchez-Azofeifa A, Sonnentag O, Tazik D, Richardson AD (Mar 2016), Frontiers in Ecology and the Environment 14:2 pp 84-93, DOI: org/10.1002/fee.1222
Quantifying the onset and progression of plant senescence by color image analysis for high throughput applications
Cai J, Okamoto M, Atieno J, Sutton T, Li Y, Miklavcic SJ (Jun 2016), PLOS, DOI: org/10.1371/journal.pone.0157102
Reconsidering plant memory: Intersections between stress recovery, RNA turnover, and epigenetics
Crisp PA, Ganguly D, Eichten SR, Borevitz JO, Pogson BJ (Feb 2016), Science Advances 19;2(2):e1501340, DOI: 10.1126/sciadv.1501340
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, Furbank RT (Dec 2016), Frontiers in Plant Science 7:1808, DOI: 10.3389/fpls.2016.01808
DNA methylation profiles of diverse Brachypodium distachyon align with underlying genetic diversity
Eichten SR, Stuart T, Srivastava A, Lister R, Borevitz JO (Sep 2016), Genome Research 26(11):1520-1531, DOI:
Image Harvest: An open-source platform for high-throughput plant image processing and analysis
Knecht AC, Campbell MT, Caprez A, Swanson DR, Walia H (May 2016), Journal of Experimental Biology 67(11):3587–3599, DOI: 10.1093/jxb/erw176
Diurnal solar energy conversion and photo-protection in rice canopies
Meacham K, Sirault X, Quick WP, von Caemmerer S, Furbank R (Nov 2016), Plant Physiology, DOI:10.1104/pp.16.01585
Reviews and syntheses: Australian vegetation phenology: new insights from satellite remote sensing and digital repeat photography
Moore CE, Brown T, Keenan TF, Duursma RA, van Dijk AIJM, Beringer J, Culvenor D, Evans B, Huete A, Hutley LB, Maier S, Restrepo-Coupe N, Sonnentag O, Specht A, Taylor JR, van Gorsel E, Liddell MJ (Sep 2016), Biogeosciences 13, 5085-5102, DOI: org/10.5194/bg-13-5085-2016
3D scanning system for automatic high-resolution plant phenotyping
Nguyen CV, Fripp J, Lovell DR, Furbank R, Kuffner P, Daily H, Sirault X (Dec 2016), 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) 30 Nov-2 Dec 2016, QLD Australia, DOI: 10.1109/DICTA.2016.7796984
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 (May 2016), Molecular Ecology 25(9):2000-14, DOI: 10.1111/mec.13584
Using phenomic analysis of photosynthetic function for abiotic stress response gene discovery
Rungrat T, Awlia M, Brown T, Cheng R, Sirault X, Fajkus J, Trtilek M, Furbank B, Badger M, Tester M, Pogson BJ, Borevitz JO, Wilson P (Sep 2016), The Arabidopsis Book 14: e0185. 2016, DOI: org/10.1199/tab.0185
Temperature responses of Rubisco from Paniceae grasses provide opportunities for improving C3 photosynthesis
Sharwood RE, Ghannoum O, Kapralov MV, Gunn LH, Whitney SM (Nov 2016), Nature Plants 2: 16186, DOI:10.1038/nplants.2016.186
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 HS, Brien C, Parent B, Collins NC (Aug 2016), Acta Physiologiae Plantarum 38: 208, DOI: 10.1007/s11738-016-2208-5
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 JD, Lohraseb I, Rabie HS, Brien C, Timmins A, Martin P, Mather DE, Emebiri L, Collins NC (Apr 2016), BMC Plant Biology 16:100, DOI:10.1186/s12870-016-0784-6
Non-destructive phenotyping of lettuce plants in early stages of development with optical sensors
Simko I, Hayes RJ and Furbank RT (Dec 2016), Frontiers in Plant Science 7:1985, DOI: 10.3389/fpls.2016.01985
Genomic variation across landscapes: insights and applications
Bragg JG, Supple MA, Andrew RL, Borevitz JO (Sep 2015), New Phytologist 207:4 pp 953-967, DOI: 10.1111/nph.13410
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 (Aug 2015), Plant Physiology 168(4), DOI: 10.1104/pp.15.00450
PhenoMeter: a metabolome database search tool using statistical similarity matching of metabolic phenotypes for high-confidence detection of functional links
Carroll AJ, Zhang P, Whitehead L, Kaines S, Tcherkez G, Badger MR (Jul 2015), Frontiers in Bioengineering and Biotechnology, 29 July 2015, DOI: org/10.3389/fbioe.2015.00106
Improving photosynthesis and yield potential in cereal crops by targeted genetic manipulation: Prospects, progress and challenges
Furbank RT, Quick WP, Sirault XRR (Oct 2015), Field Crops Research vol 182 pp 19-29, DOI: 10.1016/j.fcr.2015.04.009
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 (Nov 2015), Frontiers in Plant Science, 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 (Aug 2015), Functional Plant Biology 42(10): 921-941, DIO: doi.org/10.1071/FP15025
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 DY, Badger MR, Chow WS (Sep 2015), Frontiers in Plant Science, DOI: org/10.3389/fpls.2015.00758
Novel MtCEP1 peptides produced in vivo differentially regulate root development in Medicago truncatula
Mohd-Radzman NA, Binos S, Truong TT, Imin N, Mariani M, Djordjevic MA (Aug 2015), Journal of Experimental Botany 66(17) pp 5289–5300, DOI: org/10.1093/jxb/erv008
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
Neilson EH, Edwards AM, Blomstedt CK, Berger B, Lindberg Møller B, Gleadow RM (Apr 2015), Journal of Experimental Biology 66(7) pp 1817-1832, DOI: 10.1093/jxb/eru526
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 (Aug 2015), Plant Cell 27: 2210-2226, DOI:
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
Parent B, Shahinnia F, Maphosa L, Berger B, Rabie H, Chalmers K, Kovalchuk A, Langridge P, Fleury D (Sep 2015), Journal of Experimental Botany 66(18) pp 5481-5492, DOI: 10.1093/jxb/erv320
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 (Sep 2015), Plant Biotechnology Journal 13(7) pp 915-926,
Specificity of plant microRNA target MIMICs: Cross-targeting of miR159 and miR319
Reichel M, Millar AA (May 2015), Journal of Plant Physiology vol 180 pp 45-48, DOI: org/10.1016/j.jplph.2015.03.010
Genomic breeding for food, environment and livelihoods
Rivers J, Warthmann N, Pogson BJ, Borevitz JO (Apr 2015), Food Security 7(2) pp 375-382, DOI: 10.1007/s12571-015-0431-3
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 11:53, DOI: org/10.1186/s13007-015-0097-z
Different NaCl-induced calcium signatures in the Arabidopsis thaliana ecotypes Col-0 and C24
Schmöckel SM, Garcia AF, Berger B, Tester M, Webb AAR, Roy SJ (Feb 2015), PLOS One, 10(2), 9 pages, DOI: org/10.1371/journal.pone.0117564
Detection of decay in fresh-cut lettuce using hyperspectral imaging and chlorophyll fluorescence imaging
Simko I, Berni JAJ, Furbank RT (Aug 2015), Postharvest Biology and Technology vol 106 pp 44-52, DOI: org/10.1016/j.postharvbio.2015.04.007
“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 (Nov 2015), Plant Methods 11:52, DOI: org/10.1186/s13007-015-0095-1
Comparison of Leaf Sheath Transcriptome Profiles with Physiological Traits of Bread Wheat Cultivars under Salinity Stress
Takahashi F, Tilbrook J, Trittermann C, Berger B, Roy SJ, Seki M, Shinozaki K, Tester M (Aug 2015), PLOS One, DOI: 10.1371/journal.pone.0133322
Feature matching in stereoimages encouraging uniform spatial distribution
Tan X, Sun C, Sirault XRR , Furbank RT, Pham TD (Aug 2015), Pattern Recognition 48(8) pp 2530-2542, DOI: org/10.1016/j.patcog.2015.02.026
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 (Mar 2015), arXiv:1503.03191v2 (Open access to e-prints in Physics, Mathematics, Computer Science, Quantitative Biology, Quantitative Finance and Statistics)
Improving recombinant Rubisco biogenesis, plant photosynthesis and growth by coexpressing its ancillary RAF1 chaperone
Whitney SM, Birch R, Kelso C, Beck JL, Kapralov MV (Mar 2015), PNAS 2015 112 (11) 3564-3569, DOI: 10.1073/pnas.1420536112
Of growing importance: combining greater early vigour and transpiration efficiency for wheat in variable rainfed environments
Wilson PB, Rebetzke GR, Condon AG (Nov 2015), Functional Plant Biology 42(12) 1107-1115, DOI: 10.1071/FP15228 2.69
Pyramiding greater early vigour and integrated transpiration efficiency in bread wheat; trade-offs and benefits
Wilson PB, Rebetzke GR, Condon AG (Nov 2015), Field Crops Research, Volume 183, November 01, 2015, Pages 102-110, DOI: 10.1016/j.fcr.2015.07.002
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:
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 vol 107 (1), DOI: org/10.3852/13-278
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 (Apr 2014), Current Opinion in Plant Biology 18 pp 73-79, DOI: org/10.1016/j.pbi.2014.02.002
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 (May 2014), Analytical Chemistry 86(10) pp 5171–5178, DOI: 10.1021/ac501086n
Proximal remote sensing buggies and potential applications for field-based phenotyping
Deery D, Jimenez-Berni J, Jones H, Sirault X, Furbank R (Jul 2014), Agronomy 4(3) pp 349-379, DOI: 10.3390/agronomy4030349
An assessment of near surface CO2 leakage detection techniques under Australian conditions
Feitz A, Jenkins C, Schacht U, McGrath A, Berko H, Schroder I, Noble R, et al. (2014), Energy Procedia 63 pp 3891-3906, DOI: org/10.1016/j.egypro.2014.11.419
bHLH05 is an interaction partner of MYB51 and a novel regulator of glucosinolate biosynthesis in Arabidopsis
Frerigman, H, Berger B, Gigolashvili T (Sep 2014), Plant Physiology 166(1), 349-369, DOI: 10.1104/pp.114.240887
Image-based phenotyping for non-destructive screening of different salinity tolerance traits in rice
Hairmansis A, Berger B, Tester M, Roy SJ (Aug 2014), Rice 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 (May 2014), PLoS ONE 9(5): e97047, DOI: 10.1371/journal.pone.0097047
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 (Nov 2014), Physiologia Plantarum 152(3) pp 403–413, DOI:
Review – Scaling of thermal images at different spatial resolution: The mixed pixel problem
Jones HG, Sirault XRR (Apr 2014), Agronomy 2014, 4(3), 380-396, DOI: 10.3390/agronomy4030380
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 (Jun 2014), BMC Plant Biology 14:174, DOI: org/10.1186/1471-2229-14-174
Transplastomic integration of a cyanobacterial bicarbonate transporter into tobacco chloroplasts
Pengelly J, Förster B, von Caemmerer S, Badger M, Price G, Whitney S (July 2014), Journal of Experimental Botany 65(12) pp 3071–3080, DOI: org/10.1093/jxb/eru156
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 (Aug 2014), Journal of Integrative Plant Biology 56(8) pp 781-796, DOI:
Study on spike detection of cereal plants
Qiongyan L, Cai J, Berger B, Miklavcic S (Dec 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
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 (Apr 2014), Plant Biotechnology Journal 12(3), 378-386, DOI: 10.1111/pbi.12145
Leaf hyperspectral reflectance spectra as a tool to measure photosynthetic characters in wheat
Silva-Pérez V, Evans JR, Molero G, Condon T, Furbank R, Reynolds M (Mar 2014), Proceedings of the IV International Wheat Yield Consortium, CIMMYT Mexico, pp 154 of pdf or pp 163 of document page numbering
Wheat variability in photosynthetic capacity and efficiency for increased yield potential
Silva-Pérez V, Evans JR, Molero G, Condon T, Furbank R, Reynolds M (Mar 2014), Proceedings of the IV International Wheat Yield Consortium, CIMMYT Mexico, pp 145 of pdf or pp 154 of document page numbering
Stereo matching using cost volume watershed and region merging
Tan X, Sun C, Sirault X, Furbank R, Pham TD (Nov 2014), Signal Processing: Image Communication 29 (10) pp 1232–44, DOI: org/10.1016/j.image.2014.06.002
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 (Oct 2014), Frontiers in Plant Science vol 5 pp 551, DOI: org/10.3389/fpls.2014.00551
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
3D plant modelling via hyperspectral imaging
Liang J, A Zia, J Zhou, Sirault X (Dec 2013), IEEE International Conference on Computer Vision Workshops 2013 (ICCVW), 172–77, ISBN 978-1-4799-3022-7, DOI: 10.1109/ICCVW.2013.29
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
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:
PlantScann™: 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 (Jun 2013), Proceedings of the 7th International Conference on Functional-Structural Plant Models, Saariselka, Finland, 9-14 June 2013, 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 (Oct 2012), European Journal of Agronomy vol 42, pp 59-67, DOI: org/10.1016/j.eja.2011.12.006
Infrared thermography in plant phenotyping for salinity tolerance
James RA, Sirault XRR (2012), In: Shabala S., Cuin T. (eds) Plant Salt Tolerance, Methods in Molecular Biology (Methods and Protocols), vol 913. Humana Press, Totowa, NJ, DOI: org/10.1007/978-1-61779-986-0_11
A novel mesh processing based technique for 3D plant analysis
Paproki A, Sirault X, Berry S, Furbank R, Fripp J (May 2012), BMC Plant Biology 12:63, DOI: org/10.1186/1471-2229-12-63
Down-regulation of Glucan, Water-Dikinase activity in wheat endosperm increases vegetative biomass and yield
Ral JP, Bowerman AF, Li Z, Sirault X, Furbank R, Pritchard JR, Bloemsma M, Cavanagh CR, Howitt CA, Morell MK (Sep 2012), Plant Biotechnology Journal 10(7) pp 871-882, DOI: 10.1111/j.1467-7652.2012.00711.x
Cross image inference scheme for stereo matching
Tan X, Sun C, Sirault X, Furbank R, Pham TD (Nov 2012), Computer Vision – ACCV 2012, Lecture Notes in Computer Science, vol 7727 pp 217-230, Springer, Berlin, Heidelberg, DOI: org/10.1007/978-3-642-37447-0_17
Tree structural watershed for stereo matching
Tan X, Sun C, Sirault X, Furbank R, Pham TD (Nov 2012), Proceedings of the 27th Conference on Image and Vision Computing New Zealand (IVCNZ 2012) pp 340-345, DOI: 10.1145/2425836.2425903
Natural genetic variation for growth and development revealed by high-throughput phenotyping in Arabidopsis thaliana
Zhang X, Hause RJ, Borevitz JO (Jan 2012),
C4 Plants as biofuel feedstocks: Optimising biomass production and feedstock quality from a lignocellulosic perspective
Byrt CS, Grof CPL, Furbank, RT (Jan 2011), Journal of Integrative Plant Biology 53/120-135, DOI:
Phenomics – technologies to relieve the phenotyping bottleneck
Furbank RT, Tester M (Dec 2011), Trends in Plant Science 16/12 pp 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 (Feb 2011), Plant Methods 7:2, DOI: org/10.1186/1746-4811-7-2
Automated 3D segmentation and analysis of cotton plants
Paproki A, Fripp J, Salvado O, Sirault X, Berry S, Furbank R (Dec 2011), International Conference on Digital Image Computing: Techniques and Applications, Noosa, QLD, pp 555–560, DOI: 10.1109/DICTA.2011.99
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 (Jan 2011), Journal of Experimental Botany 62/2, pp 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 (Jun 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 (Jan 2011), Journal of Experimental Botany 62/2, pp 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 (Jan 2011), Journal of Experimental Botany 62/3, pp 1201–1216, DOI: org/10.1093/jxb/erq346
Genetic analysis of abiotic stress tolerance in crops
Roy SJ, Tucker EJ, Tester M (Jun 2011), Current Opinion in Plant Biology 14/3, pp 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 (Aug 2010), Journal of Experimental Botany 61/13: 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 (Jul 2010), BMC Bioinformatics 11: 376, DOI: org/10.1186/1471-2105-11-376
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 (Sep 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 (Nov 2010), Plant and Soil volume 336, Issue 1–2, pp 377–389, 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, eds (Nov 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 (Aug 2010), Journal of Experimental Botany 61/13: 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 (Sep 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 (May 2010), Functional & Integrative Genomics 10/2: 277-291, DOI: org/10.1007/s10142-009-0153-8
Breeding technologies to increase crop production in a changing world
Tester MA, Langridge P (Feb 2010), Science vol. 327, issue 5967, pp 818-822, DOI: 10.1126/science.1183700
With ‘phenomics’ plant scientists hope to shift breeding into overdrive
Finkel E (Jul 2009), Science vol 325, issue 5939, pp 380-381, DOI: 10.1126/science.325_380
Plant phenomics: from gene to form and function
Furbank, RT, ed. (Nov 2009), Functional Plant Biology 36(11) v – vi (forward), DOI: org/10.1071/FPv36n11_FO
C4 rice: a challenge for plant phenomics
Furbank RT, von Caemmerer S, Sheehy J and Edwards G (Nov 2009), Functional Plant Biology 36(11) 845-856, DOI: org/10.1071/FP09185
Quantifying the three main components of salinity tolerance in cereals
Rajendran K, Tester MA, Roy SJ (Mar 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 (Nov 2009), Functional Plant Biology 36(11) 970-977, 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 Planteome project is an international collaborative effort and is supported by primary funding (IOS:1340112 award) from the National Science Foundation of USA.
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
TEDxKAUST Talk by Professor Mark Tester
Professor Tester elegantly articulates the current problem of depleting fresh water resources and increasing food demand. His solution to solving food and water security issues is to unlock the salt-tolerant capabilities of various agricultural crops.
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
Global learning: Online plant science courses
Learn from the best around the world with online plant science courses:
- Plant Breeding (Wageningen University & Research)
Are you looking for more theoretical background on plant breeding? This online course includes five modules, both basic, more complex breeding and selection methods, new technological developments, and underlying biological concepts. Distance learning offers you a flexible learning process and the possibility to compose your own course. It is ideal for professionals and enables them to study the lecture material at their own pace and place. This distance learning course has been successfully followed by a broad audience from more than twenty different countries and are well-rated.
Plant Pathology and Entomology (Wageningen University & Research in cooperation with the Royal Netherlands Society of Plant Pathology (KNPV) and the Foundation Willie Commelin Scholten for Phytopathology (WCS))
Are you looking for more theoretical background on plant pathology and entomology? This online course includes modules on phytopathology, nematology, entomology and virology. Distance learning offers you a flexible learning process and the possibility to compose your own course. It is ideal for professionals and enables them to study the lecture material at their own pace and place.
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. Watch this short story to learn more about the challenges of developing plant varieties that can better cope with drought and salinity.
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: