P IRC Program Update · Deep Learning Based Estimation of Wheat Emergence and Biomass from Field...
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P 2 IRC Program Update Dr. Andrew Sharpe Canola Innovation Day Dec 6 th 2018
Transcript of P IRC Program Update · Deep Learning Based Estimation of Wheat Emergence and Biomass from Field...
P2IRC Program Update
Dr. Andrew SharpeCanola Innovation Day Dec 6th 2018
• Capitalize on strengths at UofS
• Original – nationally and globally
• Breakthrough Impact
• Aligned with Govt. priorities
• Ability to leverage financially
(partners)
Announced: December 4th 2014Application deadline: March 2nd 2015Funding to be awarded: Up to $350 million Awards announced: July 2015 $37.5 M awarded
Proposal: Designing Crops for Global Food Security
Plant Phenotyping and Imaging Research Centre (P2IRC)Establish a new centre which assembles conventional imaging (macro and micro), photon, neutron, synchrotron imaging, and mass spectroscopy into a unified data platform, which permits association genetics to be applied on a massive scale genotype to phenotype.
The Vision
Theme Structure of the P2IRC ‐ Phase I2. Image acquisition technologies1. Phenometrics 3. Computational Informatics
Crop Genomics and Bioinformatics
4. Societal and Developing World Impact
Digital Phenotyping
Association Genetics
Plant PedologicalPhenotypes
Synchrotron Imaging
Betatron laser imaging
Neutron beam imaging
MALDI MS imaging
Sensor Technology
Data management
Distributed HP computing
Data acquisition / analysis
Pheno‐informatics
Theme Structure of the P2IRC ‐ Phase I2. Image acquisition technologies1. Phenometrics 3. Computational Informatics
Crop Genomics and Bioinformatics
4. Societal and Developing World Impact
Digital Phenotyping
Association Genetics
Plant PedologicalPhenotypes
Synchrotron Imaging
Betatron laser imaging
Neutron beam imaging
MALDI MS imaging
Sensor Technology
Data management
Distributed HP computing
Data acquisition / analysis
Pheno‐informatics
GENOME SEQUENCES (Theme 3)
ASSOCIATIVE DATA ANALYSIS (Theme 3)
GERMPLASM
NEW IMAGING TECHNOLOGIES (Theme 2) ROOT / SOIL PHENOTYPE
(Theme 3)
HIGH THROUGHPUT PHENOTYPING
(Theme 3)
Phenometrics
GENOME SEQUENCES (Theme 3)
ASSOCIATIVE DATA ANALYSIS (Theme 3)
GERMPLASM
NEW IMAGING TECHNOLOGIES (Theme 2) ROOT / SOIL PHENOTYPE
(Theme 3)
HIGH THROUGHPUT PHENOTYPING
(Theme 3)
Phenometrics
The Genome
Appels, R., et al. 2018. Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science, 361(6403).
1st Gen1st Gen2nd Gen
1st Gen
3rd Gen
Optical map (scaffolding)
(Yuan C et al, 2017, Trends in Biotech, 35:6)
Evolution of Sequencing technologies
Oxford Nanopore Technology SequencingPros:• Accessible to most labs• Low cost• High throughput• Long sequence reads• Direct DNA/RNA sequencing• Direct CpG methylation dataCons: • High error rate (2‐15%)
‐ 2048 (512x4) pores per flowcell‐ 10 Gb data
MinIONIsobel Parkin, AAFC
www.nanoporetech.com
Brassica nigra genomes (N100 short read assembly vs. long read assembly)
Brassica nigra chromosome B6 (N100 short read assembly vs. long read assembly)
Exploring Synteny – Theme 3 Bioinformatics
Exploring Synteny – Theme 3 Bioinformatics
PI: Carl Gutwin – Dept. Computer ScienceTry out the tools:GSB: usask‐gsb.netlify.comSynVisio: synvisio.github.ioOther systems online soon
Contact us if you have data you would like to visualize!
ASSOCIATIVE DATA ANALYSIS (Theme 3)
GERMPLASM
NEW IMAGING TECHNOLOGIES (Theme 2) ROOT / SOIL PHENOTYPE
(Theme 3)
HIGH THROUGHPUT PHENOTYPING
(Theme 3)
GENOME SEQUENCES (Theme 3)Phenometrics
ReferenceLine
AAFC Nested Association Mapping (NAM) Population
• Training:• NAM Founder lines and Reference line
• Checks:• Other AAFC breeding lines• Representative B. juncea and B. carinata
• Validation:• NAM RILs• NAM test hybrids• Commercial hybrids
…
50 Diverse Founder Lines
NAM Funding• SaskCanola, ACPC, Manitoba Canola Producers• Agricultural Development Fund• Seed Industry Consortia Members
NAM Co‐PIs• Isobel Parkin (Lead)• Steve Robinson• Sally Vail
Image Acquisition Tools
CameraOnAStick (COASt)
DRAGANFLY COMMANDERDRAGANFLY X4 ‐ P
Image Acquisition Tools
Rededge (Multispectral)
Sony 5100 (Modified GBNIR)
Flir Vue Pro R (Thermal)
Ground Truth Data• Emergence and Stand Counts• Weekly Biomass• Cholophyll (SPAD)• NDVI (Greenseeker)• Weekly Growth Stage• Flower Counts: Weekly July to Mid‐August• Height: Vegetative, Reproductive, Canopy• Light Interception (LI‐COR Line Quantum Sensor)• Pod Counts and Canopy Metrics• Lodging Metrics• Seed Yield
Manually counting flower numbers in the field.
Steve Ryu
Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks (Ubbens and Stavness, 2017)
Ubbens JR and Stavness I (2017) Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks. Front. Plant Sci. 8:1190. doi: 10.3389/fpls.2017.01190
Deep Learning Based Estimation of Wheat Emergence and Biomass from Field Images (Aich et al. 2018)
Aich et al. (2018) DeepWheat: Estimating Phenotypic Traits from Crop Images with Deep Learning. arXiv:1710.00241v2
Automated Lodging Detection
Aug 6
July 14
Sept 8
Sajit Rajapaksa, Mark Eramian, Hema Duddu, Menglu Wang, Steve Shirtliffe, Seungbum Ryu, Anique Josuttes, Ti Zhang, Sally Vail, Curtis Pozniak and Isobel Parkin. Classification of plot lodging with graylevel co‐occurrence matrix. Submitted to WACV 2018.
Classification of crop lodging with grey level co‐occurrence matrix. Rajapaksa et al. 2018.
S. Rajapaksa et al., "Classification of Crop Lodging with Gray Level Co‐occurrence Matrix," 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, 2018, pp. 251‐258. doi:10.1109/WACV.2018.00034
Looking Forward: Ground‐based Phenotyping and Physiological Breeding
• Theme 1.2: Ground‐Based Imaging• Scott Noble, Tyrone Keep, David Pastl, Keith Halcro, Azar Khorsandi
• Raju Soolanayakanahally
Sprayer Phenotype Acquisition and Measurement Machine (SPAMM)
Phenotype Acquisition and Measurement Machine (PAMM)
P2IRC – Phase II and the Next Four Years (2019‐23)
• International Science Advisory Committee (ISAC) and Industry Advisory Committee (IAC)‐ Recommendation to mobilize the program and Theme achievements into a number of cross‐cutting, outcome‐oriented R & D projects (‘Flagships’)
‐ Exploit the strong technology base and utilize the expertise to resolve some major bottlenecks in breeding or agronomy
‐ Go from technology conception to useful and adoptable technology and build capabilities that are unique or leading edge
• Four challenging “Flagships” identified• Integrated workplans with common goals to provide outcomes that are needed by breeders, farmers or industry
• Focusing efforts with a commitment to an overarching high priority goal to develop climate change resilient crops
• New Program Lead – Andrew Sharpe taken over from Maurice Moloney
P2IRC Flagship Program – Designing Climate Change Resilient Crops
2019‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐2023
F3AI as a breeding tool
F2Mobilizing Root‐Soil‐Microbiomeinteractions
F1Digital
breeding for climate resiliency
F4Precision agronomy for yield
sustainability
• Phase 1 platforms• Elite breeding germplasm• New genetic diversity
• New climate resilient germplasm
• AI software tools• Advanced precision
agriculture platforms
www.p2irc.usask.ca
Theme 1.2 Aerial Imaging: Ti Zhang, Hema S. Duddu, Menglu Wang, Seungbum Ryu, Rosalind Bueckert, Xulin Guo, Steve Shirtliffe
AAFC‐SRDC Aerial Imaging:Kim Hodge, Cam Kenny, Yogendra
Khedikar, Evan Derdall, Raju Soolanayakanahally, Branimir Gjetvaj
and Brad Hope
Theme 3.2 Data Acquisition and Analysis: Ian Stavness, Kevin Stanley, Mark Eramian, William van der Kamp
AAFC Canola Breeding Field Crew: PI – Sally VailBrad Hope, Paul Prodahl, Ryan Vetter, Christopher Headley, Caroline Brown, other Brassica breeding programs, Brassica physiology program, many FSWEP and Coop students; Melfort (Glenn Moskal), Scott (Greg Ford); Previous group members (Murray Lewis, Hossein Zakeri)
Acknowledgments
www.p2irc.usask.ca
