- Phenome2019 Roll Call
- Phenome 2019 Plenary Talks
- Phenome 2019 Collaborative Notes
- Link to notes from General Session I
- Technology Session
- Word Cloud: Phenome 2019
- Phenome 2019 Featured Sponsor: Noble Research Institute
- Phenome 2019 Featured Sponsor: Qubit Phenomics
- Phenome 2019 Featured Sponsor: Phenospex
- Phenome 2019: Digital Phenotyping Workshop
- Discover Phenome 2019: Workshops and Field Trips
- Phenome 2019 Workshop: Genomes-to-Fields Initiative
- Phenome 2019 Roundtable Discussion: NSF ERC for Materials for Agriculture Resource Imaging Analytics at High Resolution (MARIAH)
- Phenome 2019 for Computer Scientists
- Phenome 2019 Featured Speaker: Erin Sparks
- Phenome 2019 Featured Speaker: Magdalena Julkowska
- Phenome 2019 Featured Speaker: Siobhan Braybrook
- Phenome 2019 Featured Speaker: Sierra Young
Phenome 2019 Collaborative Notes
A compilation of all the sessions
Melba Crawford, PhD - Purdue University
GB is the work horse because geometry based measurements
Having a good GPS system makes it easy for plot extraction
Plant height, crop density, panicle counting, plant structure in sorghum
LIDAR from UAV doesn’t get good penetration- ground LIDAR does better.
Her group also uses APSIM analytic models
Aim to not include l
Increasingly high-res positioning systems are game changers for research plots where row level is critical
Latent features whenever possible to maintain explainability outside of any models one builds
Lidar provides greater detail for plant structure. Rovers used for greater penetration
New modeling strategy that incorporates time and weather is called Recurrent Neural Network (RNN)
Requires full matrix of data for each time point
Do we use detailed rover datasets to develop robust models which you then predict via more broadly applicable UAV measurements?
Challenge and opportunity is to develop cross trained scientists that are able to talk to each other and work together
Brook T. Moyers, PhD – University of Massachusetts Boston
Of rice and men: using complex genetic populations in crop improvement
We are running out of water and rice is water-sensitive
She used the MAGIC rice population for her research developed at IRRI (1,200 lines total); she used a subset of 415-538 on her experiments
Augmented Design: where you do not replicate the lines (except checks)
Heritability of several traits dramatically reduced when incorporating kinship matrix in the MAGIC population, why? Lots of G by E effect and transgressive segregants.
Lots of GxE in trait values requiring large-scale experiments with multiple environments and appropriate replication to find candidate loci
Regina Baucom, PhD – University of Michigan
Can character displacement drive the evolution of root traits?
Why study root traits? Important for nutrient acquisition, host microbes, staple crops
How have they been studied? Ecologist → healthy diverse plant community; plant scientists → genes underlying particular architectures
Need more work looking at evolutionary forces leading to root traits
Theoretically, no 2 species can compete for the same limiting resources and co-exist
Character displacement--trait differences between otherwise similar species evolve due to competition for similar resources
Is there the potential for character displacement for root traits???
Ipomoea purpurea competing vs Ipomoea hederacea was the model of choice
Plant-plant competition is an agent of selection on roots (there was no above-ground competition)
Joshua Peschel - Iowa State University
Maize Stalk-Localized Actuation through Physical Presence of an Environmental Robot
Cool robot named Ariel
They tested no robot, robot and robot+slapper
Plant mechanical properties changes as a result of human/robot interaction to plants-a secondary effect of high throughput phenotyping
Mitchell Feldmann - University of California, Davis
Pedigree-Informed Genome-to-Phenome Mapping of Morphometric Traits in Strawberry
New talk title- “Quantization of Strawberry Fruit..
Alejandro Vergara - International Center for Tropical Agriculture (CIAT)
Unmanned aerial vehicle based remote sensing for monitoring Cassava (Manihot esculenta Crantz) growth and development
Afef Marzougui - Washington State University
Phenotyping Aphanomyces Root Rot Resistance in Pea using Volatile Organic Compounds
VOCs are emitted by plants in response to many environmental factors, has genetic components
Resistant pea cultivar release 3 VOCs after 15 dai
Ian Braun - Iowa State University
Computational methodology to enable direct computation on phenotypic descriptions
Using computable language-based descriptions of phenotypes from the literature to find novel candidate genes across and among species
Tara Enders - University of Minnesota
Relationships between the cold-responsive transcriptome and phenome in maize seedlings
Characterized variation in maize seedlings under cold stress
Whole plant phenotyping with RGB and hyperspectral cameras along with RNASeq data
Brooke Bruning - Australian Plant Phenomics Facility
Hyperspectral distribution maps to predict the concentration and spatial distribution of nitrogen and water content in wheat
Water and nitrogen can be predicted using hyperspectral phenotyping methods
Malcolm Bennett, PhD - University of Nottingham
US-UK DEEPER TEAM, SHAWN KEPPLER AND NATALIE DE LEON
Uncovering the hidden half of plant growth and development using root phenotyping
Root Robot excavation and imaging: harrow to dig out stalks used at Penn State. Developed 3D imaging using a multi-angle system. Huge variation in crown root angle. Deep roots showed increased NUE under low N.
Root changes the porosity around it. The soil is an important part. Thistle re-engineering the surrounding rhizosphere.
Shaping 3D Root System Architecture. Roots stop branching when they go through an air space.
New facility: Hounsfield robotic CT scanning of roots (named after the guy who invented CT) doing “4D” imaging includes time. Challenge is software. Reconstructing root phenotypes.
Nadia Shakoor, PhD - Donald Danforth Plant Science Center
TERRA-REF Year Four: De-risking the world’s largest agricultural robot for reference-grade crop phenotyping data.
David LeBauer - University of Arizona College of Agriculture and Life Sciences
TERRA REF’s Open Software, Data, and Computing for High Throughput Phenomics
George Kantor, PhD - Carnegie Mellon University - Robotics Institute
Robotics and AI Platforms for Rapid In-Field Phenotyping
1.Mobility: Autonomous vehicles, 2. perception and 3. deep neural networks.
Use in vineyards. A utility vehicle with camera and flash works night and day. Good image for a computer not for people. Automatically count all the grapes. Do early-season yield prediction and predict harvest. Build layered maps for sorghum and maize.
Molly Hanlon, PhD - Penn State
Using x-ray fluorescence technology to estimate root depth without coring or digging
Maria Newcomb, PhD - UA Maricopa Agriculture Center
Automated Chlorophyll Fluorescence Imaging from a Field Phenotyping Platform
Cody Champion - New Mexico Institute of Mining and Technology
Multivariate Curve Resolution and Other Methods Applied to the Terra-ref Hyperspectral Dataset
Magdalena M. Julkowska, PhD - KAUST
MVApp - Lessons learned from streamlining data curation and analysis
MVApp YouTube Channel
They have tutorials
People can get code at GitHub
They welcome collaborators
No more Excel! :)
Anjali Iyer-Pascuzzi, PhD – Purdue University
New tools for image based quantification of disease traits in crops
Ralstonia solanacearum Tomato bacterial wilt bacteria and slime form biofilm that works like plug.
3D imaging of plants on a turntable (4 images of each plant at 45 degree angles)
Collaborates with machine learning expert at Purdue
Katherine Meacham-Hensold – University of Illinois at Urbana- Champaign
High-throughput field phenotyping using hyperspectral imaging reveals genetic modifications to photosynthetic capacity
Handheld devices and cameras for hyperspectral imaging
Part of the RIPE project funded by Gates Foundation
Hyperspectral cameras are Pika II and Pika NIR mounted on a push car
Partial Least Square Regression(PLSR )analysis of tobacco predicting VCmax and Jmax with pretty good accuracy in single leaves.
Look at poster # 207
Steven L. Anderson, II - Texas A&M University
Utilizing structure from motion point clouds to estimate maize (Zea mays L.) height within a field-based breeding program
Poster 201 Did some grain yield vs ht measurements.
Karina Medina Jimenez - Arkansas University
High throughput kernel phenotyping of corn hybrids grown in Arkansas
Kernel size and shape measurements. Used 120 k of each hybrid from G2F
Charles Pignon - Institute for Genomic Biology
Accelerating stomatal movement under fluctuating light
Slower response of stomata to change in light (5 min) compares to PS ( 2 sec). By making stomata perform faster water could be saved. Good amount of genetic variation for the trait, speed of stomatal conductance change. Were able to find candidate genes.
Tessa Durham Brooks, PhD - Doane University
Image analysis interventions improve computational self-efficacy and inform career paths of natural science majors in the DIVAS project
Mitchell Eithun, BA – Michigan State University
Isolating juvenile phyllotactic patterns embedded within eight years of secondary growth (sweet cherry, Prunus avium L.)
Dirk Hays - Texas A&M University
SELECTION OF HIGH CARBON SEQUESTRATION ROW CROPS USING GPR
50 MT of roots/ha from perennial sorghum. Rhizomes and fine roots using GPR
Amy Tabb, PhD - USDA-ARS-AFRS
Autonomously generating shape estimates of thin plant parts across scales
Randy Clark, PhD - Corteva Agriscience
Phenotyping and modeling of plant responses to environmental stresses to guide crop breeding
Integrating phenomics in commercial crop development
Phenomics comes toward the end in breeding program
Crop growth models capture traits that results in better yield
Crop Growth Model Whole Genomic Prediction model. “All comes back to phenotyping in the end”. Need to know physiology and data to generate priors for the model.
Used huge transparent chambers (glass?) in the field to measure transpiration response to vapor pressure deficit throughout the day in maize
AQUAMAX ( a maize hybrid for drought prone regions) displays higher silk elongation rate under water deficit- more silk results to more pollination and therefore more yield.
Seth C. Murray, PhD - Texas A&M University
Towards predictive phenomics in selection, grain yield using unmanned aerial systems in maize
Genomes to Fields and bench-top near infrared reflectance spectroscopy data sets
Goal is to improve plants. Focus on prediction and discovery in plant breeding program.
Poster 213 phenomic prediction from NIRS whole kernel spectral yield. NGRDI time series.
20 flight dates on G2F set in Texas irr and dryland 8 flight dates, 3 flight dates pretty good predictions. Juvenile, flowering and grain fill images adequate. Checks had different spectral patterns. Soon available in Cyverse.
Jordan Ubbens - University of Saskatchewan
Latent Space Association Analysis: Towards GWAS Directly from Images
Natalia de Leon - University of Wisconsin-Madison
The Genomes to Fields GxE project: Progress and Perspective
- Viewing yield potential in the lense of NCGA corn contest winners (~540bu/acre) vs US average (~176bu/acre). There is a huge delta which could be improved still.
- Contest winners like all plants to emerge within a deviation of 4-12 hours.
- GEMS data format
- What alleles are important? New alleles?
- Germplasm enhancement of maize (GEM) project
- Access to ex PVP lines to incorporate alleles. Can tell what’s different but not necessarily what is useful.
- Phenotyping kernel size shape to characterize E effect on genetic lines (Edgar Spalding’s team)
Ian Stavness, PhD - University of Saskatchewan
Computational Tools for Plant Phenotyping from Outdoor Images
Plot Vision developed for field imaging
Sierra Young, PhD - North Carolina State University, Biological and Agricultural Engineering
Advancements and Challenges in Technology and Data Management Practices of Field-Based, High-Throughput Phenotyping
- There is a tech gap between the research phenomics community and traditional plant breeders
- Although tech is improving by being cheaper and a bit more standard, it is still a hurdle to get into
- View as a systems approach
- Human-machine interface
- This is not just ‘user friendliness’, but what actions are available given a person's level of expertise
- Human controller vs human monitor
- System (field) to data collection
- Platforms to date are for very specific usecases and are not ‘one size fits all’
- Do we even want that???
- Should have less focus on a generic system, but have the outputs from data capture be more standard for downstream steps
- Data transfer outside of system
- Temporal and spatial add fun and complexity
- Acquisition to processing is not trivial
- Human-data interaction currently is a dealbreaker for widespread use
- Human-machine interface
- I don’t want to be swapping SD cards from cameras…
- Decision support for human
- The science and an art
- If we go with black box, there needs to be trust in it
- Level of trust is highly dependent on the desired level of interaction and transparency in the system
- These are social science questions in part
- It is less focusing on generic boxes (steps), but the interfaces between them
- We need to include user(s) in the design more and more
- Focus on going from R&D across applied phenomics
Ethan Stewart - Cornell
The application of a deep learning approach for quantitative disease phenotyping in UAV images
NCLB resulted in lost value of ~ $ 1.9 billion/yr working on detection system
Convolutional neural network. Number and size of lesions.
Amazon’s Mechanical Turk was used to generate training data
Daniel J. Robertson, PhD, PE - University of Idaho
Deep Phenotyping for Genome to Phenome Mapping of Stalk Lodging Resistance
5-20 % yield lost; corn, wheat and rice due to stalk lodging
Stalk lodging is a complex problem and requires transdisciplinary teams working together
Received 6 mil NSF EPSCOR Track II (wow!)
Working on DNA->RNA->Protein->Metabolome->Phenome using a series of differential equations.
Implemented in a Bayesian statistical framework.
Stalk lodging depends on 3 things: 1. Anchor to soil, 2.load and 3.bending strength
Build a computational model to describe the trait.
A ‘room full of experts’ can help describe it. However a room full of experts is also very Expensive…
Zheng Xu - University of Nebraska-Lincoln
Alternative Deep Learning Approaches in Plant Phenotyping Based On Hyperspectral Images
Sen Subramanian - South Dakota State University
Quantitative 3D Imaging of cell level auxin and cytokinin response ratios in legume roots and nodules
Carmela Rosario Guadagno - University of Wyoming
Hydration dynamics surrounding photosystems explain high-throughput chlorophyll a fluorescence phenotyping
Jeff Gustin - University of Florida
VIGOR: A Controlled Environment-based Machine Vision Assay of Seedling Emergence from Soil
Seeds germinated in 168 cells or tubes filled with sand or soil monitored emergence with the camera. Used camera mounted at one side. Added a red cap to each cell measured movement of red cap movement. Traps seedling in cell.
Doing QTL analysis finding corr between seed wt and germ.
Kai-Wei Yang, PhD Student - Purdue University
Prediction of Sorghum (Sorghum bicolor) Performance through Remote Sensing and Crop Modeling
Barry Martin - Flagship Pioneering
Automated phenotyping and analysis of germination and early seedling growth using cloud computing
Margaret Staton - University of Tennessee
TreeSnap, a Mobile Device App and Website Enabling Tree Phenotyping by Citizen Scientists
Is a citizen-science mobile app partnered with restoration tree breeding programs
Has cool user interface with examples, information tabs
Jitter GPS to prevent bad actors from observed public data
Dani Martinez, PhD - Cornell University
Blackbird: A next-gen imaging system for high-throughput laboratory phenotyping.
VitisGen 2 Project in grapevine focused on powdery mildew resistance
Blackbird-00- next gen prototype of the HT imaging system
William S. van der Kamp - University of Saskatechewan
Plot Vision: Image Analysis for Plant Breeders
Why we need Plot Vision? Breeders have lots of plots and images of plots and need to process these faster with relative ease.
Christopher N. Topp, PhD - Donald Danforth Plant Science Center
Multiscale X-ray imaging, computer vision, and virtual reality data display provide a new paradigm for plant developmental biology research, teaching, and outreach
Oliver Scholz - Fraunhofer Development Center X-Ray Technology
Improving a Professor - Creating a field-based phenotyping robot based on a remote controlled sensing platform.
Sanaz Jarolmasjed - Donald Danforth Plant Science Center
Hyperspectral image analysis tools in PlantCV framework
Zbigniew Kolber - Soliense Inc.
Application of Light Induced Fluorescence Transient (LIFT) Technique in Plant Phenomics
Patrick Hudson - University of New Mexico
Passive electrical properties as reliable predictors for key aspects of plant water relations
Water potential and relative water content- two measures of water status in plants
Microneedle electrical impedance spectroscopy
Stefan Gerth - Fraunhofer Development Center X-Ray Technology
CTProcessing.net - A 3D volume processing pipeline for automated analysis of Computer Tomography measurements
Scalable, adaptable, and linkable
Pawel Krajewski - Institute of Plant Genetics, Polish Academy of Sciences
MIAPPE: current developments and applications
Deciding what information to ask everyone to include in annotating phenotypic datasets.
Using pre-defined ontologies for most variables.
More info on http://miappe.org
David Pont, PhD - Scion, New Zealand Forest Research Institute
Phenotyping for Forestry - Seeing the Forest AND the Trees
Forests often planted on the land no one else wants, so lots of issues with poor soils, steep hillsides.
Hard to process images when on side of the hill is in dark shadow and the other quite bright.
Two levels phenotyping- area based and tree based
Argelia Lorence, PhD - Arkansas State University
MicroCT scanning reveals drought-induced changes in cell size and morphology in Arabidopsis seeds
When water-limited below 50%, only you see symptoms in seeds
100 to 12.5 percent changed seed volume and surface area, cell size
All major and minor nutrients changed except Na in well watered vs water-limited seeds
@DMC_lab developed small plant imaging platform
Effects of water-limitation transgenerational
Erin Sparks, PhD - University of Delaware
Robotic phenotyping at the root-soil interface
Older the plants get when logding occurs, then it affects yield
Maize brace roots proposed to promote lodging resistance
Indeed brace roots significantly contribute to stalk flexural stiffness
Ángel Ferrero-Serrano, PhD – Penn State University
Phenotypic and genome-wide association of natural variation with the local environment of Arabidopsis
in silico GWAS using 1,001 resequenced arabidopsis lines, 131 exp. measured traits mined from previously published GWAS, and 204 geoclimatic variables from the locations the accessions were collected from.
CLIMtools website with the datasets and tools for association: http://www.personal.psu.edu/sm...
AGG3 is a heterotrimmeric G-protein involved in cold response.
A lot of this talk seemed to come from this paper published last month. Free-to-read link from the speaker’s twitter feed: http://rdcu.be/bgNXQ
Ferrero-Serrano Á, Assmann SM. 2019 Phenotypic and genome-wide association with the local environment of Arabidopsis. Nature Ecology and Evolution. PMID:30643246, doi: 10.1038/s41559-018-0754-5
INDUSTRY SHOWCASE AND CAREER OPPORTUNITIES
1. Kelly from Bayer Crop Sciences
She is a breeder
How can data analytics and phenomics help in crop development?
Liang Dong, PhD - Iowa State University
Miniature sensors for digital agriculture
· Sensors with micro technology
· Crop sensors: In-Planta Sensor for in-situ measurement of Nitrogen with tiny needles
· Microfluidic ion-selective soil sensor: In-field measurement of N in soil
· Measurement water transport from roots to leaves with two or more moisture sensors
· Leaf thickness measurement with capacitance and mechanical stress changes in sensor
· Miniature printed soil water potential sensor
Courtney Murren, PhD - College of Charleston
unPAK-ing the Arabidopsis fitness phenome: large scale mutant screen of roots, shoots and fruits with companion CURE (Course based Undergraduate Research Experiences)
Lance Cadle-Davidson, PhD - Grape Genetics Research Unit - USDA, ARS
Computer vision for high-throughput phenotyping of powdery mildew resistance
Sara Tirado - University of Minnesota
Image-based Phenotypic Platform for Monitoring Maize Growth to Estimate End-season Productivity
· DJI p4
· Image stiching and pipeline
· Stand counts and plant rotations measured at VE-V3
· Measure flowering
· Throughout canopy closure, plant height, color
· Data over 4-row plots
· Drone vs hand measured height 0.96 r2
o RMSE at 24cm (given hand vs hand is ~10cm)
o Overall correlation is largely driven by specific dates (individual dates more like 0.41)
o Almost no correlation at a given time point, even with hand vs hand
§ Not much variation to drive any correlation anyhow
· How can we better predict height at a time point?
· Hand height ~ algo height + density + planting date + genotype
· Can find times of height dip = blowdown storm and lodging
o Field level genotypic effect of lodging observable
· Temporal measurements can help inform stuff
Reisha D. Peters - University of Saskatechewan
Characterization of leaf surface phenotypes based on light interaction
Margaret Frank - Cornell University
Exploring Graft-Incompatibility with Pepmato and Tomepper
Brent E. Ewars, PhD - University of Wyoming
Coming out of the dark: night time stomatal conductance phenotyping to help improve plant growth and response to drought
Duke Pauli, PhD - The University of Arizona
Desert Phenomics: Understanding Crop Hydraulics for the Climate of the Future
Kevin Silverstein - University of Minnesota
G.E.M.S: A platform to enable data-driven agricultural innovation
Jennifer C. Quebedeaux – University of Illinois at Urbana-Champaign
Manipulating stomatal patterning to improve water use efficiency in wheat
Tyler Dowd, PhD - Donald Danforth Plant Science Center
A Greenhouse Mesocosm System for Integrated Environmental Sensing, Root Phenotyping, and New Sensor Development
Ramona L. Walls - University of Arizona
A generalized workflow for integrating biodiversity trait data using ontologies
Noah Fahlgren - Donald Danforth Plant Science Center
Machine Learning Methods in PlantCV for Leaf Tracking and More
Arthur R. Woll, PhD - Cornell University
Quantitative elemental imaging using 3D confocal x-ray fluorescence microscopy at the micron scale
Changying Li - UGA
3D plant organ mapping under field conditions
Siobhan A. Braybrook, PhD - UCLA
Quantifying diverse cell shapes: morphometrics and udulomics
Boyan Peshlov - Climate Corporation
From images to insights: Large scale crop health evaluation using high resolution imagery and machine learning
Jnaneshwar Das, PhD - Arizona State University
Robots in the Wild: Collaborative Exploration and Mapping
Sruti Das Choudhury – University of Nebraska-Lincoln
Deep Learning for Early Detection and Temporal Propagation of Drought Stress based on Hyperspectral Imagery: Dataset, Algorithm and Analysis