Hilton Tucson El Conquistador Resort

Tucson, Arizona

Tuesday, February 13, 2018

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Event

2:00 PM 6:00 PM Phenome Digital Phenotyping Workshop, Day 1 (pre-registration, required) Sponsored by:

Wednesday, February 14, 2018

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Event

8:00 AM 4:30 PM Phenome Digital Phenotyping Workshop, Day 2 (pre-registration, required) Sponsored by:

8:30 AM 3:00 PM Field Trip: Bridgestone Americas: Guayule Research Farm (pre-registration, required)

8:30 AM 3:00 PM Field Trip: Maricopa Agricultural Center (pre-registration, required)

3:00 PM 6:00 PM Registration Open 3:00 PM 6:00 PM Speaker Ready Room Open 3:00 PM 6:00 PM Poster Set-up 4:00 PM 6:30 PM NAPPN General Assembly (pre-registration,

required) 6:00 PM 7:30 PM Welcome Reception

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Thursday, February 15, 2018

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Event

7:30 AM 8:30 AM Breakfast Sponsored by:

7:30 AM 8:30 AM Poster Set-Up 7:30 AM 5:00 PM Registration Open 7:30 AM 6:30 PM Speaker Ready Room Open 8:15 AM 12:30 PM General Session I

Chair: Ivan Baxter; Donald Danforth Plant Science Center 8:15 AM – 8:30AM Chris Topp, Committee Chair, Donald Danforth Plant Science Center Phenome 2018 Welcome Remarks 8:30 AM – 9:00 AM Roland Pieruschka, PhD University of California, Davis Plant phenotyping: overcoming the bottleneck by integrated approaches 9:00 AM – 9:20 AM Wolfgang Busch, PhD Salk Institute for Biological Studies From Phenotypes to Mechanisms: Approaching Root Growth Control Using Systems Genetics 9:20 AM – 9:40 AM Pedro Andrade-Sanchez, PhD University of Arizona Integrating sensor technology and ground platforms: Case studies in proximal sensing and field phenomics in desert environments 9:40 AM – 9:50 AM Todd De Zwaan, PhD LamnaTec Fusion of multi-sensor imagery and machine learning for inspecting and grading of agricultural products 9:50 AM – 10:00 AM David Hanson, University of New Mexico Rapid gas exchange in the phenomic era 10:00 AM – 10:30: Coffee Break

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10:30 AM – 10:34 AM Tyson L. Swetnam, PhD BIO5 Institute, University of Arizona Portable, scalable, high throughput geospatial analyses with Singularity containers on cloud and high performance computing. 10:34AM – 10:38 AM Farzad Hosseinali, Texas A&M University Quantifying Nanoscale Biomechanical Properties of the Plant Cuticular Waxes 10:38 AM – 10:42 AM Juniper Kiss, Aberystwyth University Phylogenetic signal in subgenus Rubus (bramble, blackberry) leaflet shape using geometric morphometrics 10:42 AM – 10:46 AM Gokhan Hacisalihoglu, PhD Florida A&M University Novel Machine Vision Phenotyping of Maize NAM Plants Reveals Modulation Effect by Priming Depending on the Cold Temperature 10:46 AM – 10:50 AM Jaderson Armanhi, University of Campinas A real-time, non-invasive, low-cost monitoring system for plant phenotyping under stress 10:50 AM – 10:54 AM Seyed Vahid Mirnezami, Iowa State University High throughput monitoring anthesis progression of field-grown maize plants

11:00 AM – 11:30 AM David Houle, Florida State University Dimensionality: Curse or blessing?

11:30 AM – 11:50 AM Reza Ehsani, UC-Merced Sensor Systems for Monitoring Horticultural Crops: Challenges and Opportunities

11:50 AM – 12:10 PM Philip Miller, Sandia National Labs Microneedles as wearable sensors for monitoring plant stress

12:10 PM – 12:30 PM Xiaoyuan Yang, PhD The Climate Corporation Challenges and opportunities of using satellite imagery to derive insights for Precision Agriculture applications

10:00 AM 5:00PM Exhibits Open 10:00 AM 10:30 AM Coffee Break

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10:00 AM 10:30 AM Poster Set-Up 12:30 PM 1:30 PM Lunch 1:30 PM 2:30 PM Poster Session I 2:30 PM 5:30 PM Concurrent I: Robotics

Chair: Joshua Peschel, Iowa State University 2:30 PM – 2:50 PM Brittany Duncan, PhD University of Nebraska, Lincoln Human-Robot Interaction for High Performing Teams in Field Applications 2:50 PM – 3:10 PM Sierra Young, University of Illinois Design and Evaluation of a Field-Based High-Throughput Phenotyping Robot for Energy Sorghum 3:10PM – 3:30 PM Sanjeev Koppal, PhD University of Florida Small Vision Sensors for Phenomics

3:30 PM – 3:50 PM Malia A. Gehan, PhD Donald Danforth Plant Science Center PlantCV Tools for Hyperspectral Imaging of Abiotic Stress 3:50 PM – 4:20 PM Coffee Break

4:20 PM - 4:40 PM Amy Tabb, PhD USDA-ARS-AFRS Phenotyping tree shape in the field using computer vision and robotics 4:40 PM - 5:00 PM Daniel Sabo, PhD Georgia Tech Research Institute Electrical Capacitance Tomography (ECT) to Monitor Root Health and Development and Possible Application in Phenotyping 5:00 PM - 5:20 PM Erin Sparks, University of Delaware Bracing for Impact: The role of aerial roots in plant stability

2:30 PM 5:30 PM Concurrent II: New Sensors

Chair: Jennifer Clarke, University of Nebraska–Lincoln 2:30 PM - 2:50 PM James Janni, PhD DuPont Pioneer Spatial and spectral data for improved hyperspectral phenotyping

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2:50 PM - 3:10 PM Tara Enders, University of Minnesota Computer vision and hyperspectral approaches to document temperature stress responses in maize seedlings 3:10 PM - 3:30 PM Kaitlyn Read, University of New Mexico Tissue specific electrical impedance as a potential screening tool 3:30 PM - 3:50 PM Andrew Leakey, PhD University of Illinois at Urbana-Champaign Phenomics of stomata and water use efficiency in C4 species 3:50 PM – 4:20 PM Coffee Break 4:20 PM – 4:40 PM Nadia Shakoor, PhD Donald Danforth Plant Science Center Phenomics at Scale: Driving Advances in Plant Breeding with Insights from Diverse Sensor Platforms 4:40 PM - 5:00 PM Florie Gosseau, LIPM, Universite de Toulouse, INRA, CNRS, Castanet-Tolosan, France Heliaphen, an outdoor high-throughput phenotyping platform designed to integrate genetics and crop modeling 5:00 PM - 5:20 PM Travis Gray University of Saskatchewan Beyond Orthomosaics: Multi-Image Spectral Analysis of Agricultural UAV Imagery

3:50 PM 4:20 PM Coffee Break 5:30 PM 7:10 PM Technology Session

Chair: Ivan Baxter; Donald Danforth Plant Science Center 5:30PM - 5:40 PM William Salter School of Life and Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney Tackling the physiological phenotyping bottleneck with low-cost, enhanced-throughput, do-it-yourself gas exchange and ceptometry 5:40 PM - 5:50 PM Jasenka French, PhD Cibo Technologies Application of Crop Phenotyping to Computation Agronomy at CiBO

5:50 PM - 6:00 PM Zheng Xu, PhD University of Nebraska-Lincoln CT image-based Segmentation and Reconstruction of Root Systems by Machine Learning and Computational Methods 6:00 PM - 6:10 PM Grégoire Hummel, PhD, CEO Phenospex B.V. PlantEye F500: combine 3D and multispectral information in one sensor 6:10 PM - 6:20 PM Larry York, PhD Noble Research Institute RhizoVision-Crown: An open hardware and software phenotyping platform for root crowns using a backlight, a machine vision camera, and a new C++ image analysis program 6:20 PM - 6:30 PM Blake Joyce, PhD CyVerse, BIO5 Institute, University of Arizona Image Analysis using CyVerse 6:30 PM - 6:40 PM Oliver Scholz Fraunhofer Development Center X-Ray Technology Phenotyping for Plant Breeding using 3D Sensors and a Generic 3D Leaf Model 6:40 PM - 6:50 PM James Bunce PP Systems High Throughput Photosynthesis Characterization of C3 Plants 6:50 PM - 7:00 PM Eric Rogers, Doctor of Philosophy Hi Fidelity Genetics In situ phenotyping of root system architecture 7:00 PM - 7:10 PM Bruce Schnicker The Climate Corporation Leveraging Sensors, Probes and Drones to Enable Data Driven Decisions for Growers

Friday, February 16, 2018

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Event

7:00 AM 8:30 AM Breakfast 7:30 AM 4:00 PM Registration Open 7:30 AM 5:00 PM Speaker Ready Room Open

8:30 AM 12:30 PM General Session II Chair: Sally Mackenzie, Penn State University 8:30 AM - 9:00 AM Rick Zedde Wageningen University & Research Automation and robotics for high-throughput phenotyping and precision horticulture and agriculture 9:00 AM - 9:20 AM Diane Rowland, Doctor of Philosophy University of Florida Integrating truly transdisciplinary approaches in forming a novel pipeline between questions and solutions addressing crop stress. 9:20 AM - 9:40 AM Michael Selvaraj, PHD International Center for Tropical Agriculture CIAT Phenomics Platform: Aiming at improving Eco-efficiency of crops in the changing global climate 9:40 AM - 10:00 AM Ross Sozzani, PhD NCSU Quantitative imaging and dynamic models of plant stem cells 10:00 AM – 10:30: Coffee Break 10:30 AM - 10:34 AM Fuqi Liao, MA The Noble Research Institute Plant Root Quantitative Analysis 10:34 AM - 10:38 AM Sara Tirado University of Minnesota Field Based Phenotypic Platform for Characterizing Maize Growth and Development 10:38 AM - 10:42 AM Donghee Hoh, MSU-DOE Plant Research Laboratory The genetic and mechanistic bases of photosynthetic cold tolerance in legume, cowpea (vigna unguiculata (l.) walp.) via high throughput environmental phenotyping 10:42 AM - 10:46 AM Sabrina Elias University of Dhaka and University of Nebraska Lincoln Deciphering the association of phenome and gene expression postulating salt tolerance mechanism in a rice landrace, Horkuch

10:46 AM - 10:50 AM Ian Braun Iowa State University Computational Classification of Phenologs across Biological Diversity 10:50 AM - 10:54 AM Cory Hirsch, PhD University of Minnesota Machine vision phenotyping platform for seedling growth and morphology 10:54 AM - 10:58 AM Kyle Parmley Iowa State University Machine learning approaches in Soybean Phenomics: Predicting Seed Yield, Oil and Protein in Contrasting Production Systems 11:00 AM - 11:30 AM Sindhuja Sankaran, PhD Washington State University Advances in sensing for high-throughput in-field and postharvest crop phenotyping 11:30 AM - 11:50 AM Daniel Runcie, PhD University of California Davis A Bayesian approach to quantitative genetics for high-dimensional traits 11:50 AM - 12:10 PM Saket Navlakha, PhD The Salk Institute for Biological Studies Network design principles of plant shoot architectures 12:10 PM - 12:30 PM Mao Li, PhD Donald Danforth Plant Science Center Using mathematics to dissect and quantify the plant form, above and belowground

10:00 AM 10:30 AM Coffee Break 10:00 AM 5:00 PM Exhibits Open 12:30 PM 1:30 PM Lunch 1:30 PM 2:30 PM Poster Session II 2:30 PM 5:30 PM Concurrent III: Integrating Phenotypes

Through Modeling Chair: Carolyn Lawrence-Dill, Iowa State University 2:30 PM - 2:50 PM Brian Bailey, PhD University of California, Davis Coupling terrestrial LiDAR measurements of tree architecture with high-resolution biophysical models to provide insights into plant-environment interactions

2:50 PM - 3:10PM Caitlin Moore University of Illinois Linking solar induced fluorescence with photosynthetic variability in crops at the leaf and plot scales. 3:10 PM - 3:30 PM Suxing Liu University of Georgia Put the carbon back into the soil: 3D root phenotyping for improved carbon sequestration 3:30 PM - 3:50 PM Noah Fahlgren Donald Danforth Plant Science Center A modular, community-driven framework for developing high-throughput plant phenotyping tools 3:50 PM – 4:20 PM: Coffee Break 4:20 PM - 4:40 PM Guillaume Lobet, PhD Forschungszentrum Juelich Non-linear plant phenotyping pipelines: how can structural models and machine learning can help us analyse large plant image datasets 4:40 PM - 5:00 PM Walid Sadok University of Minnesota Gravimetric phenotyping of canopy conductance in wheat and maize reveals novel mechanisms, traits and genetic loci involved in drought tolerance in the field 5:00 PM - 5:10 PM Brent Ewers University of Wyoming Use of biophysical first principles to select plant traits and the instruments and analyses to measure and explain them 5:10 PM - 5:20 PM Christer Jansson Pacific Northwest national Laboratory Genome-to-Phenome Mapping by Metabotyping in Brachypodium distachyon: Exploring Genotypic Diversity for Biomass Accumulation and Shoot-Root Allometry

2:30 PM 5:30 PM Concurrent IV: Crop Biology Chair: Nathan Springer, University of Minnesota 2:30 PM - 2:50 PM Candice Hirsch, PhD University of Minnesota Insights into the genotype-by-environment interaction enabled through phenomics

2:50 PM - 3:10 PM Kamaldeep Virdi, PhD University of Minnesota Genetic control of soybean (Glycine max L. Merr.) shoot architecture 3:10 PM - 3:30 PM Steven Shirtliffe, PhD Department of Plant Sciences, University of Saskatchewan Field Phenotyping of Grain Crops Response to Agronomic Inputs 3:30 PM - 3:50 PM Abdullah A Jaradat USDA/ARS & University of Minnesota Forward Phenomics of oat Panicles 3:50 PM – 4:20 PM: Coffee Break 4:20 PM - 4:40 PM Alison Thompson, PhD USDA-ARS Data fusion with light detection and ranging and images to map and count bolls in upland cotton 4:40 PM - 5:00 PM Katy Rainey Purdue University UAS Phenotyping in Soybean Breeding and Phenomic Inference 5:00 PM - 5:20 PM Menachem Moshelion The Hebrew University of Jerusalem Whole-plant stress performance analysis: A new tool for functional phenotyping

3:50 PM 4:20 PM Coffee Break 5:30 PM 7:00PM Poster Session III and Reception

Saturday, February 17, 2018

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Event

7:30 AM 8:30 AM Breakfast 7:30 AM 5:00PM Speaker Ready Room Open

8:00 AM 3:30 PM Registration Open

8:30 AM 10:45 AM Poster Removal

8:30 AM 12:30 PM General Session III Chair: Nathan Springer, University of Minnesota 8:30 AM - 9:00 AM Tiina Roose, MSc, DPhil (PhD) University of Southampton Multiscale modelling of plant-soil interaction

9:00 AM - 9:20 AM Robert Guralnick University of Florida The Plant Phenology Ontology: A new informatics resource for large-scale integration of plant phenology data 9:20 AM - 9:40 AM Carolyn Rasmussen, PhD University of California, Riverside Division plane orientation in plant cells 9:40 AM - 10:00 AM Maria Salas-Fernandez, PhD Iowa State University Automated plant architectural trait extraction from a field-based high-throughput phenotyping platform 10:00 AM – 10:30 AM: Coffee Break 10:30 AM - 11:00 AM Sotirios Tsaftaris, MSC, PhD University of Edinburgh Machine learning in plant phenotyping: will it relieve the bottleneck? 11:00 AM - 11:20 AM Alexander Bucksch, PhD University of Georgia The shape of plants to come: in situ computation and field math 11:20 AM - 11:40 AM Michael Malone, PhD Climate Corporation Measurements that matter: Ensuring quality and traceability of data for agricultural insights 11:40 AM - 12:00 PM Stefan Gerth Fraunhofer EZRT Root phenotyping using X-ray technology: Automation of data segmentation for 4D analysis 12:00 PM - 12:20 PM Hong Cui, PhD University of Arizona From text blobs to computable data: challenges in mining phenotypical data from text

10:00 AM 10:30 AM Coffee Break 10:00 AM 3:30 PM Exhibits Open 12:30 PM 2:30 PM Lunch 2:30 PM 5:30 PM Concurrent V: Microclimate effects on plant

phenotypes

Chair: Chris Topp, Donald Danforth Plant Science Center

2:30 PM - 2:50 PM Therese LaRue Stanford University Uncovering the genetic basis for natural variation of root system dynamics in Arabidopsis 2:50:00 PM - 3:10:00 PM Miki Fujita, PhD RIKEN CSRS Evaluation of Plant Environmental Stress Response using “RIPPS”, an Automated Phenotyping System 3:10 PM - 3:30 PM Bettina Berger Australian Plant Phenomics Facility - University of Adelaide High-throughput 3D analysis of barley shoots reveals novel QTL involved in leaf growth under salt 3:30 PM - 3:50 PM Max Feldman Donald Danforth Plant Science Center The trait components that constitute whole plant water use efficiency are defined by unique, environmentally responsive genetic signatures in the model C4 grass Setaria 3:50 PM – 4:20: Coffee Break

4:20 PM - 4:40 PM Jian Jin, PhD Purdue University Purdue's New Automatic Phenotyping Greenhouse with Micro-climates Removed 4:40 PM - 5:00 PM Rony Wallach Prof. Hebrew University of Jerusalem Should Soil Water Availability considered in plant phenotyping for abiotic-tolerance, and how? 5:00 PM - 5:20 PM Nathan Miller University of Wisconsin-Madison A Machine-Vision Seedling Emergence Assay

2:30 PM 5:30 PM Concurrent VI: Graduate training in phenomics: an interdisciplinary adventure Chair: Carolyn Lawrence-Dill, Iowa State University 2:30 PM - 2:50 PM Jordan Ubbens, MSc University of Saskatchewan An Introduction to Deep Learning in Plant Phenotyping Without Agonizing Pain 2:50 PM - 3:10 PM Eric Lyons, PhD University of Arizona Teaching students to use supercomputers for phenomics

3:10 PM - 3:30 PM Carolyn Lawrence-Dill, Iowa State University P3, the Predictive Plant Phenomics Graduate NSF Research Traineeship (NRT) at Iowa State University 3:30 PM - 3:50 PM Argelia Lorence, PhD Arkansas State University Developing the Pipeline of Plant Phenomics Experts at the Wheat and Rice Center for Heat Resilience 3:50 PM – 4:20 PM: Coffee Break 4:20 PM - 4:40 PM Natalie Henkhaus, PhD American Society of Plant Biologists Reinventing Postgraduate Training in the Plant Sciences through Modularity, Customization, and Distributed Mentorship 4:40 PM - 5:00 PM Ramona Walls University of Arizona Help! My data is a out of control! Novel services for management of distributed phenotypic data 5:00 PM – 5:20 PM Bobby Brauer, Matt McCown, Jenna Hoffman Monsanto Who is Phenome 2018? Our journey delivering the digital phenotyping revolution through a combined focus on technology and people

3:50 PM 4:20 PM Coffee Break

7:00 PM 10:00PM Closing Party (wristband required) Sponsored by:

Speaker Abstracts

General Session I Thursday, February 15, 2018 | 8:15 AM – 12:30 PM

Plant phenotyping: overcoming the bottleneck by integrated approaches

Roland Pieruschka, PhD, Forschungszenrum Jülich

Forschungszentrum, Jülich, Germany

Quantitative analysis of structure and function of plants has become a major bottleneck

in research and applied use of plants. Approaches targeting relevant traits are needed to

quantitatively address key processes and understand the dynamic interactions between

genetic constitution, molecular and biochemical processes with physiological responses

leading to the development of phenotypes.

In this presentation, I will use case studies to demonstrate how plant phenotyping

infrastructure can be used to address relevant biological questions for accurate

measurement of biomass, structure and functional properties of plants across different

scales and developmental stages. For instance, I will present the use of automated systems

for the cultivation and imaging of model and crop species and demonstrate phenotyping

pipelines across scales under controlled and filed conditions. In the second part of the

presentation I would illustrate the role of plant phenotyping networks, summarize the

recent activities such as the EU funded project EPPN2020 that enables European

scientists to access plant phenotyping facilities across Europe and, the ESFRI listed

project EMPHASIS that aims at long- term sustainable development of the plant

phenotyping infrastructure in Europe. Finally, the International Plant Phenotyping

Network, a non-profit association integrates the plant phenotyping community as a global

communication hub.

From Phenotypes to Mechanisms: Approaching Root Growth Control Using

Systems Genetics

Wolfgang Busch, PhD, Salk Institute for Biological Studies

Salk Institute for Biological Studies

What is the basis for the profound variation of phenotypes and within a single species?

Uncovering the relevant genetic variants and the molecular mechanisms which these

variants affect, would have tremendous implications for a large number of application

ranging from breeding to precision medicine. Using the root system of the model plant

Arabidopsis thaliana, we have approached this problem using a systems genetics

approach that integrates high throughput phenotyping, genome wide association

mapping and functional genomic approaches. We have discovered multiple novel

mechanisms that underlie the natural variation of root traits and have explored the

relation of these gene variants and root traits with climate and soil parameters. Among

the most outstanding mechanisms is a signaling module of Leucine-Rich-Receptor-Like-

Kinases in which natural genetic variation determines root growth responses to low iron

levels. Interestingly, these genes are also involved in defense responses. Overall, our

work demonstrates that systems genetics approaches harnessing existing natural genetic

variation, phenomics as well as modern post-genome-era approaches, allow us to

understand genetic and molecular mechanisms that underlie phenotypic variation and

most likely contribute to local adaption.

Integrating sensor technology and ground platforms: Case studies in

proximal sensing and field phenomics in desert environments

Pedro Andrade-Sanchez, PhD, University of Arizona

University of Arizona

This presentation will provide a brief review of ground-based sensor platforms used for

plant trait characterization, with particular emphasis on systems applied to research

under the dry desert conditions of the US Southwest and irrigated agriculture. A

description of approaches for continuous monitoring of sensor platform position,

measurements of plant spectral and thermal response along with plant geometry will be

included in this talk. Plant canopy height characterization will be presented in more detail

as a case study. Canopy height can be interpreted as one axis of canopy volume, therefore

interpretation of electronic displacement sensor signals is an efficient way to characterize

plant geometry. Canopy height in sorghum is important because of many factors,

maximum plant height usually shows strong relations with net productivity. In cereals,

rapid changes in canopy height are potential indicators of panicle initiation and onset of

grain filling, although this association likely varies with photoperiod and genetics. Since

canopy height determines the working distance between sensors and the crop surface,

accurate measurement of heights is also of value for proximal sensing. Mechanical

actuation is an integral component of sensor platforms that allow adjustments in vertical

frame position. This way, the sensor height may be raised as the crop grows to maintain

a fixed working distance, and height data may be used as covariates in analyses of

proximal sensing datasets. Plant height also affects crop management, especially in

relation to lodging and mechanical harvest.

Fusion of multi-sensor imagery and machine learning for inspecting and

grading of agricultural products

Todd De Zwaan, PhD, LemnaTec Corporation

Solmaz Hajmohammadi – LemnaTec

Substantial improvements in plant breeding and crop management to feed a projected

population of 9-billion is one of this century’s grand global challenges. Early and accurate

detection and diagnosis of plant diseases, even before specific symptoms become visible

are a key factor in crop yield. This can be achieved by the development of high-resolution

systems equipped with multiple sensors measuring beyond the visible light spectrum.

This is a data intensive approach and demands analytical methods that can cope with the

resolution, size and complexity of the signals from these sensors. This approach also

needs high-throughput capabilities to measure more complex phenotypic information at

higher volumes in production environments.

In recent years, impressive results have been achieved in image detection and

classification that extended the market of computer vision applications in agriculture.

However, any nontrivial machine learning algorithm needs a high-quality dataset. A

result of the ever increasing development of sensors for rapid and non-destructive

assessment of plants is the ability to fuse the output of these sensors to create higher order

datasets. Multi-sensor fusion aims to integrate data collected at different temporal,

spectral and spatial scales to deliver more knowledge content than could be achieved by

each sensor independently.

Phenotyping occurs at laboratory, greenhouse, and field scales. Therefore, the demand

for platforms in each of these settings that have multi-sensor capabilities is high.

LemnaTec is addressing this demand with software and hardware systems that assess

phenotypes of plants and their organs from millimeter to meter scale in laboratory,

greenhouse and field settings. This talk will focus on the sensor fusion methodology in

different platforms using 2D and 3D datasets, and highlight applications of machine

learning tools for segmentation and quality monitoring using hyperspectral imaging.

Rapid gas exchange in the phenomic era

David Hanson, University of New Mexico

Joseph Stinziano – University of Western Ontario

Phenotyping for photosynthetic gas exchange parameters is limiting our ability to select

plants for enhanced photosynthetic carbon gain and to assess plant function in current

and future natural environments. This is due, in part, to the time required to generate

estimates of the maximum rate of ribulose-1,5-bisphosphate carboxylase oxygenase

(Rubisco) carboxylation, the maximal rate of electron transport, and Rubisco activation,

from the response of photosynthesis to the CO2 concentration inside leaf air spaces. To

relieve this bottleneck, we developed a method for rapid photosynthetic carbon

assimilation CO2 responses utilizing non-steady-state measurements of gas exchange.

Using high temporal resolution measurements under rapidly changing CO2

concentrations, we can collect traditional gas exchange parameters in around 2 minutes.

This is a small fraction of the time previously required for even the most advanced gas

exchange instrumentation. We present how we have applied this method to diurnal

changes in physiology as well as responses to light, CO2, and temperature.

Portable, scalable, high throughput geospatial analyses with Singularity

containers on cloud and high performance computing.

Tyson Swetnam, PhD, BIO5 Institute, University of Arizona

Reproducible science with geographic information systems (GIS) on cloud, high

throughput computing (HTC), and high performance computing (HPC) requires portable,

scalable, workflows as part of the Research Object. Here we present a method for running

free and open-source software for GIS; i.e. Geospatial Data Abstraction Library (GDAL),

Geographic Resources Analysis Support System (GRASS), and System for Automated

Geoscientific Analyses (SAGA), in tandem with a workflow management system,

Makeflow, on cloud and HPC using Singularity containers. Our example workflow

involves the computation of daily and monthly sum solar irradiation using an OpenMP

version of the GRASS r.sun algorithm. A single virtual machine (VM) masters the

workflow, with remote workers connected over Internet2 started on cloud, HTC, and/or

HPC platforms, all using the same Singularity container. The workflow is currently

deployed on the OpenTopography.org cyberinfrastructure, where users can select any

location on the terrestrial earth surface using national or global digital elevation model

(DEM) data to calculate global irradiation and daily hours of sunlight. Our workflow links

with OpenTopography via the Opal2 toolkit for wrapping this particular scientific

application as a Web service from a XSEDE Jetstream VM. The workers are launched on

demand on XSEDE Comet HPC and Open Science Grid HTC. Importantly, because the

workflow is containerized with Singularity, it can be re-deployed on any combination of

local desktop, cloud, or HTC / HPC by simply pulling the code from our GitHub repository

and following a few basic setup instructions. Containerized workflows such as ours that

take an open science approach, as part of the Research Object, will allow for future

reproducible geospatial science on cyberinfrastructure.

Quantifying Nanoscale Biomechanical Properties of the Plant Cuticular

Waxes

Farzad Hosseinali, Texas A&M University, Biological & Agricultural Engineering

Department

The potential applications of Atomic Force Microscope (AFM) in quantifying the

biomechanical properties of plants tissue and membranes, such as the cuticle of tomato

fruits, have been introduced before. However, previous studies on the application of the

AFM in the surface characterization of cotton fiber were mainly focused on the AFM

capabilities in producing high-resolution topography images of either fiber surface or its

cross–section. In fact, cotton fiber cells are covered with a thin cuticular

membrane. The cuticle is mostly made of lipids, alcohols, and fatty acids (collectively

called ‘cotton wax’). The waxy layer can be 10 to 300 nm thick and

imparts hydrophobicity to the fiber surface. The main objective of this study was to

characterize and compare the surface nanomechanical properties of cotton fibers using

various modes of the AFM. Surface topography and friction images of the fibers were

obtained with conventional contact mode. The nanomechanical property images, such

as adhesion and deformation, were obtained with Bruker’s newly developed

high-speed force-volume technique, PeakForce QNM®. The differences in nanoscale

friction, adhesion, and deformation signals can be attributed to fiber surface

hydrophobicity and stiffness, which in turn depend on fatty acids’ hydrocarbon

chain length, film viscosity, and the waxy layer thickness.

Phylogenetic signal in subgenus Rubus (bramble, blackberry) leaflet shape

using geometric morphometrics

Juniper Kiss, Aberystwyth University

Plant phenotypic plasticity and different ways of genetic recombination during clonal and

sexual reproduction make the identification of some plant species difficult. Although DNA

barcoding has revolutionised species identification, polyploidy, hybridisation and

apomixis pose challenges to this process. Subgenus Rubus (brambles, blackberries) is one

of the most taxonomically challenging groups of dicots and their morphology based

classification has not been entirely consistent with their molecular phylogeny. The

definition of bramble species is controversial and is often reliant on leaf and leaflet

characters. Here, we combined geometric morphometrics with molecular analysis.A total

of 230 leaves from 115 specimens were imaged from different environments (woodland,

sandy beach, saltmarsh, grassland) in the UK. We conducted a three-loci molecular

analysis using ITS(internal transcribed spacer) region of nrDNA and two cpDNA regions,

maturase K (matK) and trnL–trnF for 23 representative leaf samples. We analysed the

shape of five-foliate and three-foliate leaves using landmark-based image analysis. Using

Principal Component Analysis (PCA) and Canonical Variate Analysis (CVA), the leaflet

shapes clustered according to the different environments. Discrimination Analysis (DA)

also confirmed that most of the group mean shapes were highly significantly different (P

< 0.001) at different locations, while it was more obscure when analysed for differences

in between bramble series. Using squared-change parsimony, the molecular phylogeny of

the haplotypes was projected into the leaflet morphospace. Permutation tests suggested

the phylogenetic signal in leaflet arrangement morphology to be statistically significant

(P < 0.05). These results suggest that each haplotype has different shapes in different

environments, while the overall shape differences of haplotypes could be explained by

their phylogeny. We suggest a statistically robust approach to combine morphometric

analysis with molecular data to understand the variability of leaflet shape which could

affect the morphology-based classification of Rubus.

Novel Machine Vision Phenotyping of Maize NAM Plants Reveals

Modulation Effect by Priming Depending on the Cold Temperature

Gokhan Hacisalihoglu, PhD, Florida A&M University

Seedling emergence is an important factor for yield, particularly under challenging

planting conditions. In the US corn belt, maize is planted in early spring, as soon as soil

temperatures are permissive to germination. At that time, temperatures often drop below

normal, which can delay or even kill the seedling. Seed pre-treatments have been shown

to improve germination in cold conditions in crops such as rice and cabbage, but are

largely unpublished in maize. To assess the effects of pre-treatments on early maize cold

tolerance, twenty-seven inbred parents of maize Nested Association Mapping (NAM)

population were primed using a synthetic solid matrix and then tested for cold tolerance

using a soil-based emergence assay. Primed kernels were incubated at 10°C for 5 days,

and then transferred to 24°C for emergence. DSLR cameras were used to capture images

every 30 min to obtain emergence profiles of each seedling. Emergence time was

determined from the time-lapsed images and multiple measures including final

emergence percentage, time to 50% emergence, and emergence rate were extracted for

each genotype. The cold treatment reduced total emergence of several genotypes.

However, priming pre-treatment protected the sensitive genotypes allowing nearly full

emergence. We also used single-kernel near infrared reflectance spectroscopy to

determine seed density, weight, oil, protein, and starch for the kernels prior to planting.

By combining kernel characteristics and emergence time, we found small, but highly

significant correlations between the kernel and early seedling performance.

A real-time, non-invasive, low-cost monitoring system for plant

phenotyping under stress

Jaderson Armanhi, University of Campinas

Phenotypic data are essential to understanding plant responses to environmental

changes. Conventional instruments to assess plant physiological status are often invasive

or destructive, such as pressure chambers and tensiometers, or designed to provide a

single data point, such as the infrared gas analyzer (IRGA) and the porometer. Although

these methods are reliable, they do not provide continuous monitoring of plant response

to environmental stresses, which might result in losses of relevant information regarding

the true physiological status and plant adaptation mechanisms. Real-time phenotyping

technologies are usually costly, and most platforms are restricted to phenotyping

facilities. Therefore, the development of low-cost phenotyping options is exceptionally

convenient for small-scale studies and experimental setups under growth chamber and

greenhouse conditions. Here we propose a simple and real-time monitoring system for

the remote study of plant physiology using low-cost and easy-to-handle electronic

components. Our system provides the constant monitor of leaf temperature, vapor

pressure deficit (VPD), soil moisture, water loss, as well as the air temperature, relative

humidity and light intensity. An integrated RGB camera was used to record plant

response over time and a modified camera was used to capture near-infrared images for

NDVI measurements. All sensors and cameras are connected to a microcontroller

Raspberry Pi that receives and processes signals and images through custom and

automated scripts. Real-time data are sent to an online server that plots graphs and

creates time-lapse movies on a webpage. Sensors and methods are currently being

validated in experiments designed to evaluate drought stress response in maize. By

providing temporal high-resolution data and imaging, our small-scale system has the

potential to bring valuable information on plant phenotyping in a low-cost manner.

High throughput monitoring anthesis progression of field-grown maize

plants

Seyed Mirnezami, Graduate Research Assistant, Iowa State University

The tassel is the male organ of the maize plant. Sufficient pollen production is crucial for

the production of hybrid seed. Good seed set requires both sufficient daily production of

pollen, but also pollen shed on enough days to ensure a good “nick” with the receptivity

of female inbreds. Traditional approaches for phenotyping anthesis progression are time-

consuming, subjective, and labor intensive and are thus impractical for phenotyping large

populations in multiple environments. In this work, we utilize a high throughput

phenotyping approach that is based on extracting time-lapse information of anthesis

progress from digital cameras. The major challenge is identifying the region of the interest

(i.e. the location of tassels in the imaging window) in the acquired images. Camera drift,

different types of weather, including fog, rain, clouds, and sun and additionally, occlusion

of tassels by other tassels or leaves complicated this problem. We discuss various

approaches and associated challenges for object detection and localization under noisy

conditions. We illustrate a promising deep-learning approach to tassel recognition and

localization that is based on Region with Convolutional Neural Network (R-CNN). It is

able to reliably identify a diverse set of tassel morphologies. We subsequently extract

time-dependent tassel traits from these localized images.

Dimensionality: Curse or blessing?

David Houle, Florida State University

The ability to acquire phenome-level data is wonderful – hundred dimensional vectors of

data! Our limited human brains, however, think in just three or possibly four,

dimensions, and we like our stories simple. All that raises the question: Once you have

phenomic data, what do you do with it? Many biologists still study phenome level effects

as a very large set of one-to-one mappings between each trait and the experiment or SNP,

depending on the application. Part of the reason that biologists are often stuck in this

mode is the claimed “curse of dimensionality,” where measuring more and more on the

same number of subjects is supposed to become less and less useful. I suggest a taking a

geometric approach where the object of study is not the effects of the SNP or experiment

on each of the hundreds of phenotypes we might measure, but the total length of the effect

vector, and its direction in phenotype space. This mode of thinking follows naturally from

a fully multivariate analysis of the phenotype. A second frontier is the incorporation of

multivariate predictors, for example of a genome-full of polymorphism, or a history of

environmental influences on the phenome itself. For such applications, we need to

introduce biologically-motivated structure to the analysis, for example using

regularization. By choosing the analysis to match our questions, we can escape the curse

of dimensionality, and indeed turn dimensionality to a blessing.

Sensor Systems for Monitoring Horticultural Crops: Challenges and

Opportunities

Reza Ehsani, UC Merced

UC-Merced

Collecting site- or plant-specific data under field conditions has been a challenging and

costly task for researchers and growers. Growers need data for efficient use of crop inputs

and more efficient control of pests and diseases. Scientists need these data for selecting

the best plant in their breeding program or evaluating the effectiveness of different field

practices and treatments. Canopy size and density, growth rate, early detection of pests

and diseases, rootstock and tree age, root size, root density, yield estimation, and yield

monitoring are examples of data that can be used in a plant production system. In recent

years, there has been a significant progress in the area of Unmanned Aerial Vehicles

(UAVs), multi-band and hyperspectral cameras, wireless sensors, and low-power low-cost

electronic components, and data processing units. These advances resulted in better

sensor systems. This presentation will provide an overview of the sensor system

technologies for tree crops that are commercially available or being developed.

Microneedles as wearable sensors for monitoring plant stress

Philip Miller, Sandia National Labs

Kaitlyn Read – UNM; Dave Hanson – UNM; Ronen Polsky – Sandia National Labs;

Patrick Hudson – UNM

Microneedles are microscale devices primarily used for minimally invasive drug delivery

in humans however our group has utilized them as wearable electrochemical sensors for

health monitoring applicaitons. Recently, we've adapted these sensors for plant stress

monitoring in sorghum and shown that these devices are well tolerated and easily

interfaced with several plant tissue types (leaves, root crowns, and stalks) with little

immune reponse from the plant. Three sensors systems are being developed utilizing our

electromolding method. In this technique, metal microneedles are made from

electroplating into predefined molds that are made from replicating structures fabricated

with two-photon polymerization utilizing laser direct write. With this method arrays of

both hollow and solid microneedles are possible. Hollow microneedles are being adapted

for turgor pressure sensors and solid microneedles for impedance probes and as

metabolite sensors. Initial results show impedance measurements can tract plant drought

stress and recovery via monitoring impedances at low frequencies (0.1-10kHz) between

microneedles in sorghum leaves and a probe in the soil. Microneedle metabolite sensors

have been designed to detect glucose and arrays of microneede sensors allow for

multiplexed detection for spatial mapping. A portable system for remote data logging of

impedance, metabolites, and turgor pressure has begun greenhouse testing and initial

results indicate the system is capable of measuring impedance and environmental

conditions autonomously.

Challenges and opportunities of using satellite imagery to derive insights

for Precision Agriculture applications

Xiaoyuan Yang, PhD, The Climate Corporation

Driving efficiency in agricultural production depends on a number of parameters that are

highly variable in space and time. Digital tools for precision agriculture, targeted at

tailoring subfield decisions, require timely, accurate and scalable input to generate

insights. Remote sensing technology is a viable solution to achieve this goal. Various

public and private satellite platforms become available with the capability of acquiring

global observation daily or tracking field history for the past 30 years. Customized narrow

spectral bands can be designed for specific applications such as understanding nitrogen

or water limitations to yield. In addition, the unique value of active remote sensing, e.g.

radar and lidar, are being adopted. The Climate Corporation delivers farmer facing field

health imagery in Climate FieldViewTM. Here we will review the challenges and

opportunities of using satellite imagery to derive agronomic insights. Sensor calibration,

preprocessing, cloud/shadow detection, and atmospheric correction etc. are necessary to

provide high quality image input at scale. Selecting proper combinations of spatial,

temporal and spectral resolution to address a specific problem is critical as tradeoffs often

exist in image sources. Combining imagery with weather, soil, and farm management

practices data, image based information layers can be produced applying advanced data

science algorithms. These layers encapsulate the information for decision making, such

as crop monitoring, scouting, stress detection, management zoning and yield prediction.

Moreover, scalable storage/computation platform is in need for running analytics on

enormous amount of image data.

Concurrent I: Robotics Thursday, February 15, 2018 | 2:30 PM – 5:30 PM

Human-Robot Interaction for High Performing Teams in Field Applications

Brittany Duncan, PhD, University of Nebraska, Lincoln

University of Nebraska

This talk will discuss the role of human-robot interaction in field-based robot

deployments and be focused on three individual research areas: integration of robots into

high performing teams, improved teleoperation, and necessary autonomy for improved

team performance. Specific research questions that will be addressed include: 1) What

role does the use of aerial vehicles play in shared decision making with high performing

and potentially distributed teams? 2) How can interfaces and interactions amplify the

current reach of the end users? and 3) What adaptations are necessary within the

autonomy to augment user perceptions in field-based environments? This discussion will

be of interest to researchers and practitioners in agriculture and robotics communities,

as well as those in the fields of human factors, artificial intelligence, and the social

sciences.

Design and Evaluation of a Field-Based High-Throughput Phenotyping

Robot for Energy Sorghum

Sierra Young, Iowa State University

University of Illinois

This article describes the design and field evaluation of a low-cost, high-throughput

phenotyping robot for energy sorghum. High-throughput phenotyping approaches have

been used in isolated growth chambers or greenhouses, but there is a growing need for

field-based, precision agriculture techniques to measure large quantities of plants at high

spatial and temporal resolutions throughout a growing season. A low-cost, tracked mobile

robot was developed to collect phenotypic data for individual plants and tested on two

separate energy sorghum fields in Central Illinois during summer 2016. Stereo imaging

techniques determined plant height, and a depth sensor measured stem width near the

base of the plant. A data capture rate of one acre, bi-weekly, was demonstrated for

platform robustness consistent for various environmental conditions and crop yield

modeling needs, and formative human-robot interaction observations were made during

the field trials to address usability. This work is of interest to researchers and practitioners

advancing the field of plant breeding because it demonstrates a new phenotyping

platform that can measure individual plant architecture traits accurately (absolute

measurement error at 15% for plant height and 13% for stem width) over large areas at a

sub-daily frequency.

Small Vision Sensors for Phenomics

Sanjeev Koppal, PhD, University of Florida

University of Florida

Biological vision performs amazing visual tasks with negligible power consumption.

Insect eyes for example, allow for optical flow, obstacle avoidance, target tracking,

navigation and even object recognition using micro-watts of power. If robotic drones had

this kind of low power vision, we could imagine massive impact on agriculture and

phenomics. However, despite the fantastic strides in computer vision in recent years,

delivering such high-performance and real-time capability, within tiny power budgets, is

still a distant dream. The reason is that core computer vision algorithms usually follow a

predictable pattern: large amounts of high-resolution imagery and video are combined

with massive amounts of computation. While this achieves spectacular results in many

domains, a new approach is required for the coming wave of next generation miniature

devices. These are micro and nano-scale devices, with feature sizes less than 1mm, that

will soon impact fields as diverse as geographic and environment sensing, agricultural

control and monitoring, energy usage and crop health. This talk is about our work in

solving the core problems that will enable computer vision on miniature platforms.

Allowing these small devices to reliably sense their surroundings has the potential for a

major transformation in phenomics and related fields.

PlantCV Tools for Hyperspectral Imaging of Abiotic Stress

Malia Gehan, PhD, Donald Danforth Plant Science Center

To tackle the challenge of producing more food and fuel with fewer inputs a variety of

strategies to improve and sustain crop yields will need to be explored. These strategies

may include: mining natural variation of wild crop relatives to breed crops that require

less water; increasing crop temperature tolerance to expand the geographical range in

which they grow; and altering the architecture of crops so they can maintain productivity

while being grown more densely. These research objectives can be achieved with a variety

of methodologies, but they will require both high-throughput DNA sequencing and

phenotyping technologies. A major bottleneck in plant science is the ability to efficiently

and non-destructively quantify plant traits (phenotypes) through time. PlantCV

(http://plantcv.danforthcenter.org/) is an open-source and open development suite of

image processing and analysis tools that could initially analyze images from visible, near-

infrared, and fluorescent cameras. Here we present new PlantCV analysis tools associated

with the development of a hyperspectral and 3D imaging platform aimed at the

identification of early abiotic stress response.

Phenotyping tree shape in the field using computer vision and robotics

Amy Tabb, PhD, USDA-ARS-AFRS

United States Department of Agriculture – Agricultural Research Service

Phenotyping of tree shape is a challenging problem, not least of which because the

traditional metrics of tree shape – height, width, branch number, branch angle, branch

diameter, and branch length, may not be particularly characteristic of the structural

differences that are evident to humans between phenotypes. We describe ongoing work

to develop a robot vision system that captures the above metrics of fruit tree shape

autonomously and accurately, as well as complete tree reconstructions for use in novel

shape descriptors. I will demonstrate how the system operates in field settings, and

describe its constraints and possible applicability to other species.

Electrical Capacitance Tomography (ECT) to Monitor Root Health and

Development and Possible Application in Phenotyping

Daniel Sabo, PhD, Georgia Tech Research Institute

Ga Tech Research Inst

It is becoming increasingly important in plant phenotyping to have an understanding of

root development due to its importance to the health, development, and production

quality of plants. For breeders, it is important to develop cultivars with desired rooting

traits that contribute to resource use efficiency and improved yield. On the other hand,

information about root health and development would provide needed insight into plant

development and water/nutrient requirements. This means there is a universal need for

a new, nondestructive, and in situ method that monitors root health and development.

Electrical capacitance tomography (ECT) is a nondestructive technique that allows for

this desired root monitoring. We have shown that relative ECT measurements are able to

provide information for root development, insight into speed of root growth, and the

ability to distinguish healthy developing roots from stunted and dying roots,

nondestructively. ECT was also used for presymptomatic detection in bell pepper plants

and various stress effects on the roots. ECT has the ability to provide much needed

information on root health, speed of root growth, stress effects on rooting properties, and

root mass development, making it a desirable sensing technique for plant phenotyping.

Bracing for Impact: The role of aerial roots in plant stability

Erin Sparks, University of Delaware

Damage to plants that prevents them from staying upright, called lodging, can have

a significant impact on cereal crop yield. While there is a large emphasis on

reducing lodging, we understand little about how plants achieve stability. In maize

plants, aerial roots that emerge from the stem above the soil, called brace roots,

are proposed to play an important role in structural stability. Yet how brace roots

develop, integrate environmental cues and contribute to plant stability remains a

poorly understood area of plant biology. Research in our lab focuses on questions

regarding the development and function of maize brace roots. Specifically, we are

taking a structural engineering approach to define the contribution of brace roots

to plant stability. From structural engineering, we know that there are two key

features to building stable structures: the arrangement of the building materials

and the mechanical properties of the building materials. To extrapolate these

features into plants, we have developed a field-based crawling robot for brace root

phenotyping to define the arrangement of building materials. In addition, we are

subjecting brace roots to tension and compression testing to define the mechanical

properties of the building materials. This information is being integrated into structural

engineering models to determine the contribution of brace roots to plant stability. These

experiments are among the first to define the diversity of brace root architecture and

mechanical properties in maize, which is critical to understanding the significance of

these specialized roots in plant stability.

Concurrent II: New Sensors Thursday, February 11, 2017 | 2:30 PM – 5:30 PM

Spatial and spectral data for improved hyperspectral phenotyping

James Janni, PhD, DuPont Pioneer

DuPont Pioneer

Phenotypes have been characterized using both the spectral and spatial information

provided by Pioneer's automated hyperspectral imaging towers. A stressed phenotype

and leaf nitrogen estimations will be used to demonstrate the sensitivity for high

throughput plant characterization. Spatial variation of spectral response will be explored

for increased precision. Spectral indices and the inversion of the PROSPECT model will

be included.

Computer vision and hyperspectral approaches to document temperature

stress responses in maize seedlings

Tara Enders, University of Minnesota

Susan St Dennis – University of Minnesota; Nathan Miller – University of Wisconsin;

Liz Sampson – University of Minnesota; Edgar Spalding – University of Wisconsin;

Nathan Springer – University of Minnesota; Cory Hirsch – University of Minnesota

Yields of maize may be reduced substantially within the next century due to global climate

change. Understanding how maize varieties respond to temperature extremes will be

instrumental in developing varieties that can withstand future abiotic stresses while still

producing high yield. We are documenting the variation of morphological traits, color,

and hyperspectral signals in maize seedlings in response to abiotic stresses in multiple

maize genotypes over time. Morphological measurements, such as plant height, width,

and area, can help characterize the impact of stresses on growth rates. Color data from

RGB images allows for quantification of physiological changes to stress, such as leaf

necrosis, which varies substantially among maize genotypes. Hyperspectral data may

capture valuable information about how genotypes respond to stress that is unable to be

captured using RGB imaging and could provide early detection of stress responses prior

to other manifestations. Documenting multiple traits across genotypes and growth

conditions will uncover the dynamics of maize responses to changing temperatures and

allow for the discovery of genomic loci that could provide improved tolerance.

Tissue specific electrical impedance as a potential screening tool

Kaitlyn Read, University of New Mexico

Patrick Hudson – University of New Mexico; Philip Miller – Sandia National

Laboratories; David Hanson – University of New Mexico

Electrical Impedance Spectroscopy (EIS) is a commonly used noninvasive method to

predict root dimensions, tissue damages, and other physiological parameters. These

methods typically rely on measuring through an electrically variable medium (ie soil,

hydroponic fluid, and epidermal layers), or destructively removing part of the plant. Here

we demonstrate the utility of microneedles to apply EIS methods to specific organs and

tissues in planta. Microneedles were placed on both the adaxial and abaxial surfaces of a

sorghum (Sorghum bicolor) leaf midrib, to measure water storage and water transport

tissues, respectively. An 18-gauge needle was placed 1 cm below the leaf-stalk junction to

function as the signal receiver for both microneedle placements. A handheld LCR meter

supplied a voltage of 0.6V AC, and measured impedance and phase angle at four different

frequencies. Microneedle impedance values were compared to planar metal transducers

as a control, which didn’t penetrate the plant tissue, and impedance values across all

frequencies tested were significantly lower with the microneedle devices. After in planta

EIS measurements were concluded, a fully expanded leaf was removed. Water storage

and water transport tissues were dissected, and EIS measurements were repeated in the

isolated tissues. Impedance was significantly lower in water transport tissue compared to

water storage tissue, both in planta and in isolation. One week after in planta

measurements, leaves showed no adverse response to microneedle applications, other

than superficial callose deposition at the injection site. Our results show that microneedle

EIS can distinguish specific tissues in a non-destructive fashion, and offer a novel

opportunity for high resolution, real-time plant monitoring.

Phenomics of stomata and water use efficiency in C4 species

Andrew Leakey, PhD, University of Illinois at Urbana-Champaign

Andrew D.B. Leakey1*, John Ferguson1, Nathan Miller2, Jiayang Xie1, Charles Pignon1,

Gorka Erice1, Timothy Wertin1, Nicole Choquette1, Maximilian Feldman3, Funda

Ogut4, Parthiban Prakash1, Peter Schmuker1, Anna Dmitrievna1, Dylan Allen1,

Elizabeth A. Ains

University of Illinois at Urbana-Champaign

Water use efficiency (WUE), which is physiologically distinct from drought tolerance, is a

key target for improving crop productivity, resilience and sustainability. This is because

water availability is the primary limitation to crop yield globally and irrigation uses the

largest fraction of our limited and diminishing freshwater supply. The exchange of water

and CO2 between a leaf and the atmosphere is regulated by the aperture and pattern of

stomata. Mechanistic modeling indicates that stomatal conductance could be reduced or

stomatal movements accelerated to improve water use efficiency in important C4 crops

such maize, sorghum and sugar cane. While molecular genetics has revealed much about

the genes regulating stomatal patterning and kinetics in Arabidopsis, knowledge of the

genetic and physiological control of WUE by stomatal traits in C4 crops is still poor.

Understanding of natural diversity in stomatal traits is limited by the lack of high-

throughput phenotyping methods. To this end two novel phenotyping platforms were

developed. First, a rapid method to assess stomatal patterning in three model C4 species

grown in the field – maize, sorghum and setaria has been implemented. Here the leaf

surface is scanned in less than two minutes with a modified confocal microscope,

generating a quantitative measurement of a patch of the leaf surface. An algorithm was

designed to automatically detect stomata in 10,000s of these images via a training of a

pattern-recognition neural network approach. Second, a thermal imaging capture

strategy, to rapidly screen the kinetics of stomatal closure in response to light has been

developed. We are gaining insight on the underlying genetics governing stomatal stomatal

patterning through quantitative trait loci and genome wide association studies in addition

to phenotypic evaluations of sorghum with transgenically modified expression of stomatal

patterning genes. These multifaceted approaches are complemented by a recently

established field facility for comprehensive evaluation of leaf, root and canopy WUE traits

under Midwest growing conditions.

Phenomics at Scale: Driving Advances in Plant Breeding with Insights from

Diverse Sensor Platforms

Nadia Shakoor, PhD, Donald Danforth Plant Science Center

Donald Danforth Plant Science Center

With the rapid advancement and implementation of robust and high quality genetic and

genomic technologies, the functional analysis of new genomes is currently limited by the

quality and speed of high throughput phenotyping. Ongoing advances in genomics and

high throughput phenotyping creates multiple layers of valuable information that can be

exploited to rapidly advance breeding. In recent years, major contributions from

government and private organizations have been invested in the creation and use of high

throughput tools to speed the development and deployment of phenotyping and breeding

technologies to benefit researchers and farmers. The TERRA-REF program and the

Sorghum Genomics Toolbox, funded by the Department of Energy’s ARPA-E program

and the Bill and Melinda Gates foundation, are employing cutting-edge technologies to

sequence and analyze crop genomes, along with deploying various scales of imaging

platforms (e.g, UAS, tractor-based and indoor and outdoor field scanner systems) to

capture millions of phenotypic observations across growing seasons and diverse

environments to accelerate crop breeding efforts by connecting those phenotypes to

genotypes.

For example, breeding for cold temperature adaptability is vital for the successful

cultivation of bioenergy sorghum at higher latitudes and elevations, and for early season

planting to extend the growing season. Through the TERRA-REF project, we have

successfully resequenced 400 bioenergy sorghum lines and carried out high throughput

phenotyping to identify candidate genes and alleles that enhance biomass accumulation

of sorghum grown under early season cold stress. Genome wide association studies

(GWAS) of temporal growth data identified potential genes and time specific quantitative

trait loci (QTL) controlling response to early cold stress, permitting an investigation into

the temporal genetic basis of cold stress response at different stages of plant development.

Heliaphen, an outdoor high-throughput phenotyping platform designed to

integrate genetics and crop modeling

Florie Gosseau, LIPM, Universite de Toulouse, INRA, CNRS, Castanet-Tolosan, France

Florie Gosseau – LIPM, Universite de Toulouse, INRA, CNRS, Castanet-Tolosan,

France; Nicolas Blanchet – LIPM, Universite de Toulouse, INRA, CNRS, Castanet-

Tolosan, France; Louise Gody – LIPM, Universite de Toulouse, INRA, CNRS, Castanet-

Tolosan, France; P

Heliaphen is an outdoor high-throughput phenotyping platform allowing automated

management of growth conditions and monitoring of plants during the whole plant cycle.

A robot moving between plants growing in 15L pots monitors plant water balance and

phenotypes plant or leaf morphology, from which we can compute more complex traits

such as the response of leaf expansion (LE) or plant transpiration (TR) to water deficit.

Here, we illustrate the platform capacities on two practical cases: a genetic association

study for yield-related traits and a simulation study, where we use measured traits as

inputs for a crop simulation model. For the genetic study, classical measurements of

thousand-kernel weight (TKW) were done under water stress and control condition

managed automatically on a sunflower bi-parental population. The association study on

TKW in interaction with hydric stress highlighted five genetic markers with one near to a

gene differentially expressed in drought conditions from a previous experiment. For the

simulation study, we used the SUNFLO crop growth model to assess the impact of two

traits measured in the platform (LE and TR) on crop yield in a large population of

environments. We conducted simulations in 42 contrasted locations across Europe and

21 years of climate data. We identified ideotypes (i.e. genotypes with specific traits values)

that are more adapted to specific growing conditions, defined by the pattern of abiotic

stress occurring in these type of environments.

This study exemplifies how phenotyping platforms can help with the identification of the

genetic architecture of complex response traits and the estimation of eco-physiological

model parameters in order to define ideotypes adapted to different environmental

conditions.

Beyond Orthomosaics: Multi-Image Spectral Analysis of Agricultural UAV

Imagery

Travis Gray, University of Saskatchewan

William van der Kamp – University of Saskatchewan; Travis Gray – University of

Saskatchewan; Steve Shirtliffe – University of Saskatchewan; Hema Duddu – University

of Saskatchewan; Kevin Stanley – University of Saskatchewan; Ian Stavness –

University of Saskatchewan

Imagery from Unmanned Aerial Vehicles (UAVs) is frequently used for crop assessment

and phenotyping in agricultural research fields. In particular, spectral indices, such as

NDVI, are commonly employed to estimate traits such as the health, growth stage, and

biomass in crops. In virtually all such studies, many overlapping images from a UAV flight

are first stitched into a single orthomosaic image, and then spectral indices are derived

from the orthomosaic. But this method necessarily discards (or aggregates) much of the

original pixel information. For example, a single plot may appear in 10 or more individual

images from the UAV, but appear only once in the final orthomosaic. In this paper, we

show that an index value extracted from individual calibrated images of the same plot can

deviate by 10% or more from the index value of the corresponding orthomosaic segment.

This could have important consequences for fine-grained comparison of indices. To

address this problem, we propose alternative approaches to estimating indices, each of

which more directly incorporates all of the individual UAV images. We evaluate these

approaches, and compare them with the orthomosaic approach, by analysing weekly

index values from plant breeding experiments for lentil, wheat, and canola crops in

comparison to relevant manually-measured phenotypes.

Technology Session Thursday, February 15, 2018 | 5:30 PM – 7:10 PM

Tackling the physiological phenotyping bottleneck with low-cost, enhanced-

throughput, do-it-yourself gas exchange and ceptometry

William Salter, , School of Life and Environmental Sciences, Sydney Institute of

Agriculture, The University of Sydney

William Salter – The University of Sydney; Matthew Gilbert – University of California,

Davis; Andrew Merchant – The University of Sydney; Thomas Buckley – University of

California, Davis

High throughput phenotyping platforms (HTPPs) are increasingly adopted in plant

breeding research due to developments in sensor technology, unmanned aeronautics and

computing infrastructure. Most of these platforms rely on indirect measurement

techniques therefore some physiological traits may be inaccurately estimated whilst

others cannot be estimated at all. Unfortunately, existing methods of directly measuring

plant physiological traits, such as photosynthetic capacity (Amax), have low throughput

and can be prohibitively expensive, creating a bottleneck in the breeding pipeline. We

have addressed this issue by developing new low-cost enhanced-throughput phenotyping

tools to directly measure physiological traits of wheat (Triticum aestivum). Our eight-

chamber multiplexed gas exchange system, OCTOflux, can directly measure Amax with

5-10 times the throughput of conventional instruments, whilst our handmade

ceptometers, PARbars, allow us to monitor the canopy light environment of many plots

simultaneously and continuously across a diurnal cycle. By custom-building and

optimizing these systems for throughput we have kept costs to a minimum, with

OCTOflux costing roughly half that of commercially available single-chamber gas

exchange systems and PARbars costing approximately 95% less than commercial

ceptometers. We recently used these tools to identify variation in the distribution of Amax

relative to light availability in 160 diverse wheat genotypes grown in the field. In a two-

week measurement campaign we measured Amax in over 1300 leaves with OCTOflux and

phenotyped the diurnal light environment of 418 plots using 68 PARbars. These tools

could be readily modified for use with any plant functional type and also be useful in

validating emerging HTPPs that rely on remotely sensed data to estimate photosynthetic

parameters.

Application of Crop Phenotyping to Computation Agronomy at CiBO

Jasenka French, PhD, Cibo Technologies

Cibo Technologies

At CiBO Technologies, we use software and science to solve problems across the whole

agriculture value chain. We are addressing challenges of sustainability, climate change,

and food security by unifying big data and advanced analytics with a fundamental

understanding of the complexities of agriculture.

Crop phenotyping and environment characterization via imaging technologies is an

important part of enabling massive simulations and inference problems that CiBO is

building the framework to address. We will discuss CiBO’s approach and some challenges

in this demanding quest.

CT image-based Segmentation and Reconstruction of Root Systems by

Machine Learning and Computational Methods

Zheng Xu, PhD, University of Nebraska-Lincoln

Camilo Valdes – Florida International University; Stefan Gerth – Fraunhofer Institute

for Integrated Circuits IIS; Jennifer Clarke, Sleep – University of Nebraska-Lincoln

Computed Tomography (CT) scanning technologies have been widely used in many

scientific fields, especially in medicine and materials research. A lot of progress has been

made in agronomic research thanks to CT technology. CT image-based phenotyping

methods enable high-throughput and non-destructive measuring and inference of root

systems, which makes downstream studies of complex mechanisms of plants during

growth feasible. An impressive amount of plant CT scanning data has been collected, but

how to analyze these data efficiently and accurately remains a challenge.

We present new computational and machine learning methods for better segmentation

and reconstruction of root systems from 3D CT scanning data. We propose new

approaches within the category of voxel thresholding methods. Considering special

characteristics of root systems, we propose our methods based on two new local-feature

statistics, i.e., proportion and weighted proportion. We found that methods based on our

two new statistics can calibrate root system magnitudes faster than traditional vessel-

based approaches while preserve similar levels of performance. In addition, we propose

and evaluate machine learning approaches in root-system segmentation and

reconstruction from CT-images, in particular simulation-assisted machine learning

approaches. We illustrate and compare different approaches using both simulated and

real CT scanning data from Fraunhofer Institute for Integrated Circuits IIS.

PlantEye F500: combine 3D and multispectral information in one sensor

Grégoire Hummel, PhD, CEO, Phenospex B.V.

PlantEye is a high-resolution 3D laser scanner that computes a robust and validated set

of morphological plant parameters fully automatically. A core feature of PlantEye is that

it can be operated in full sunlight without any restrictions - crucial for plant phenotyping

under field conditions or if you follow a “sensor-to-plant-concept”. Phenospex has now

developed a new sensor generation, which combines the actual features of PlantEye on

the fly with a 4-channel multispectral camera in the range between 400 – 900nm. This

unique hardware-based sensor fusion concept allows us to deliver spectral information

for each data point of the plant in X, Y, Z-direction and we can compute parameters like

NDVI, color index and many other vegetation indices. This new sensor generation opens

a wide range of new possibilities in plant phenotyping and increases its efficiency.

RhizoVision-Crown: An open hardware and software phenotyping platform

for root crowns using a backlight, a machine vision camera, and a new C++

image analysis program

Larry York, PhD, Noble Research Institute

Anand Seethepalli – Noble Research Institute; Haichao Guo – Noble Research Institute;

Marcus Griffiths – Noble Research Institute

Root crown phenotyping, or shovelomics, has become increasingly popular to evaluate

the root systems of crops from the field. Generally, a root crown is excavated and soil is

removed. Earlier methods used a combination of rating and manual measurements such

as number of axial roots, angles of axial roots, lateral root branching density and length,

and diameters. However, imaging followed by image analysis has become increasingly

popular because the techniques are faster and more precise. However, no standard for

imaging has emerged and reproducibility of imaging conditions is difficult, which leads

to images that may be hard to segment and analyze. Here, we describe a new phenotyping

platform that combines custom hardware and software to optimize the imaging of root

crowns and allows fast image analysis with software that is easy to install. The

RhizoVison-Crown hardware platform consists of an extruded aluminum tubular

structure with a 2 ft LED ceiling light panel on one side and a monochrome machine

vision camera on the other. Root crowns are affixed to top panels using a clip and inserted

into a fixed position for imaging, which makes switching crowns fast and ergonomic. The

hardware can be constructed for < $1000. Image acquisition settings are set so the output

of the camera is a quasi-segmented black root crown on a white background that is easily

fully segmented using simple thresholding. A barcode scanner is used to trigger the image

acquisition and the images are stored with the encoded identity using custom software.

The image analysis software is based on OpenCV and written in C++. Extracted features

include root length, area, convex hull, width, depth, diameters, and angles. Examples of

its operation and use in several species will be discussed. This relatively inexpensive and

reproducible system may allow more opportunities for researchers to conduct root

research.

Image Analysis using CyVerse

Blake Joyce, PhD, CyVerse, BIO5 Institute, University of Arizona

Blake Joyce – CyVerse, BIO5, University of Arizona

Phenotyping is poised to surpass genotyping as the next 'big data' challenge. UAVs,

tractors, and remote senors are capable of producing terabytes of data daily for individual

hectares of land. Now that data acquisition is becoming easier, analysis at scale will be the

next bottleneck. CyVerse can provide data storage and management to research teams,

specialized image analysis through CyVerse BisQue, and on-demand cloud computing

through CyVerse Atmosphere. We'll give an overview of image analysis using CyVerse.

Additionally, ongoing development will be discussed for phenotyping-related analyses

like machine learning image feature recognition, analyses running on GPUs, and

integration with geographical information system (GIS) analyses.

Phenotyping for Plant Breeding using 3D Sensors and a Generic 3D Leaf

Model

Oliver Scholz, Fraunhofer Development Center X-Ray Technology

Franz Uhrmann – Fraunhofer Development Center X-Ray Technology; Katharina Pieger

– Fraunhofer Development Center X-Ray Technology; Dominik Penk – University of

Erlangen-Nuernberg; Guenther Greiner, Prof. – University of Erlangen-Nuernberg

We present a setup to objectively assess sugar beet plant traits using multiview

stereoscopy with color cameras with the goal of generating relevant phenotyping

parameters to aid the breeder. The setup was tested in a greenhouse environment as well

as in the field. The assessment is performed using a generic leaf model adapted to the

specific requirements of the sugar beet breeder. The evaluation yields global plant

parameter including plant height, leaf area, leaf count, etc. as well as per-leaf parameters

for each leaf of the plant. The leaf model is based on semantic geometric parameters,

which can directly be interpreted by the breeder.

High Throughput Photosynthesis Characterization of C3 Plants

James Bunce, PP Systems

Dr. James Bunce, Ph.D. in Plant Physiology – PP Systems; Andrew Lintz, B.S. in

Mechanical Engineering – PP Systems

Single point measurements of leaf gas exchange provide basically only the parameters net

photosynthesis, stomatal conductance, and instantaneous leaf water use efficiency. From

analysis of assimilation rate vs. internal CO2 concentrations (A vs. Ci) curves, four or five

additional leaf parameters are obtained for plants with C3 carbon metabolism, which

allow estimation of photosynthesis over a range of conditions. However, determining A

vs. Ci curves conventionally requires at least 20 minutes per leaf, compared with about 2

minutes for single point measurements, which greatly limits through-put. PP Systems

has developed a method of linearly ramping CO2 rapidly in their CIRAS-3 Portable

Photosynthesis System, which provides a complete A vs. Ci curve in 5 minutes per leaf.

Two initial steps are required: storing the changes in analyzer sensitivity with background

CO2, and collecting data from ramping of CO2 with an empty chamber. These two steps

need only be done once per day. The 5 minute total measurement period per leaf includes

a 2 minute initial equilibration period followed by 3 minutes of ramping up of CO2

concentration until the rate of change of A with CO2 becomes small, i.e. until CO2

becomes nearly saturating to A. With the CIRAS-3 system post-processing of the gas

exchange data is very simple: the apparent “A” of the empty chamber is subtracted from

the “A” value obtained with a leaf in the chamber at each time point of the CO2 ramping

period. This provides the actual A value at each time point, and the Ci is obtained from

this actual A, stomatal conductance, and external CO2 as in the conventional calculation

of Ci. Because of the rapid change in CO2, we have seen no significant change in stomatal

conductance during the CO2 ramps.

In situ phenotyping of root system architecture

Eric Rogers, Doctor of Philosophy, Hi Fidelity Genetics

Root system architecture (RSA) plays a pivotal role in plant fitness and yield yet

remains an untapped resource for crop development. Breeders have primarily

ignored root phenotyping due to an absence of suitable in situ phenotyping

methods. We have developed a device that can detect root growth in real time in

the field, called a RootTracker. The RootTracker runs on battery power and sends

readings wirelessly to a nearby microcomputer that stores and uploads the data to

the cloud for analysis. In its current form factor, the RootTracker can identify

individual roots, and can distinguish growth angles and growth rates. We have

demonstrated long-term deployment of RootTrackers in three maize field sites.

These trials showed we could distinguish root growth rates between two different

maize cultivars. We are currently working to refine our design and add additional

soil property sensors that would be useful to farmers and breeders.

Leveraging Sensors, Probes and Drones to Enable Data Driven Decisions

for Growers

Bruce Schnicker, The Climate Corporation

Technologies and tools that agricultural scientists only dreamed of accessing even as

recently as 10 years ago are now a standard component of our agricultural data acquisition

platform. Sensors, probes, drones, cameras, and a plethora of other technologies are

routinely used at The Climate Corporation to accelerate the development of value added

models to our digital platform. These technologies are leveraged across multiple testing

formats, including Research & Development, Commercial, Growers, and our own

internally managed research farms. Data and insights from soil, weather, equipment, and

the plants themselves are required to enable development and deployment of predictive

models for agricultural use. Successful utilization of these data layers results in products

that enable growers to make data driven decisions to enhance their performance and

profitability.

General Session II Friday, February 16, 2018 | 8:30 AM – 12:30 PM

Automation and robotics for high-throughput phenotyping and precision

horticulture and agriculture

Rick Zedde, Wageningen University & Research

Wageningen University

Automated plant phenotyping offers plant scientists, breeders and growers a powerful

tool to gather vast amounts of growth data to understand and optimise plant performance

and productivity. For effective use in industry these tools need to be fast, accurate and

objective. Robots developed for phenotyping purposes can be translated to horticultural

production locations and solutions for precision agriculture might benefit phenotyping

purposes. This cross-disciplinary interaction launched as a catalyst a range of novel

technological developments.

Integrating truly transdisciplinary approaches in forming a novel pipeline

between questions and solutions addressing crop stress.

Diane Rowland, Doctor of Philosophy, University of Florida

D.L. Rowland1, A. Zare2, Y. Tseng1, S. Zou2, X. Guo2, H. Sheng2, B. Zurweller1, and R.

Gloaguen1

University of Florida

Critical breakthroughs in crop stress will require transdisciplinary approaches.

Transdisciplinarity follows the concept of “Convergent Research” from the National

Science Foundation: utilizing deep integration across disciplines by immersion in cross

disciplinary language, techniques, strategies, and constant team interaction – efforts all

aimed at one compelling problem. This contrasts “interdisciplinary” research, where

groups remain in disciplinary silos, approaching research objectives in isolation with

team members meeting periodically to simply report ongoing results. Achieving

transdisciplinarity takes a period of integration and education among disparate

disciplines, and confronting existing research paradigms. Research groups within the

Center for Stress Resilient Agriculture and the Machine Learning and Sensing Laboratory

have formed a transdisciplinary group aimed at solving critical