Source: TEXAS A&M UNIVERSITY submitted to
AERIAL AND GROUND PHENOTYPING ANALYTICAL TOOL DEVELOPMENT FOR PLANT BREEDERS USING THE MAIZE G2F PROJECT
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
EXTENDED
Funding Source
Reporting Frequency
Annual
Accession No.
1012030
Grant No.
2017-67013-26185
Project No.
TEX09685
Proposal No.
2016-09623
Multistate No.
(N/A)
Program Code
A1141
Project Start Date
Mar 15, 2017
Project End Date
Mar 14, 2021
Grant Year
2017
Project Director
Murray, S.
Recipient Organization
TEXAS A&M UNIVERSITY
750 AGRONOMY RD STE 2701
COLLEGE STATION,TX 77843-0001
Performing Department
Sponsored Research Services
Non Technical Summary
High throughput field phenotyping (HTFP) using unmanned aerial vehicle (UAV) or ground vehicle systems, equipped with sensors, is a promising new approach for plant breeders and plant scientists to measure different varieties and select the best ones. HTFP approaches are expected to help screen more varieties, for more measurable characteristics, faster and perhaps more accurately than current human-based measurements. These approaches will be useful to improve crops for yield, yield stability, stress-resistance, quality, safety or environmental impact, benefitting farmers, industry, society and the environment. Despite exciting possibilities from these approaches, there are many gaps in knowledge and tools for plant breeders to actually use HTFP approaches in decision making. In this project, new tools for HTFP data will be developed that will make the technology easier to use, from detection through action. The HTFP approaches will be developed and tested using largest and most diverse US corn experiment to date, the Genomes to Fields GxE experiment (G2F-GxE). Because the G2F-GxE will be grown under nine heat and drought stress environments, HTFP will be used to identify varieties and genetics with different tolerances to stress. The major outputs of this project will be big-data methods for HTFP, developed and deployed within the statistical computing environment 'R', and improved characterization of the maize G2F-GxE experiment. Student training, software, presentations, publications, and knowledge useful to public and private breeders will also result. Although HTFP is yet unproven, if successful, it is expected that the private and public sectors will adopt some or all of the methods to improve their own breeding processes; this will result in new industry jobs and more efficient agricultural production benefitting both farmers and society.?
Animal Health Component
0%
Research Effort Categories
Basic
50%
Applied
25%
Developmental
25%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
4042410206010%
4042410208010%
2022410209010%
2031510108120%
2021510108120%
2011510108110%
2021510108010%
2031510108010%
Goals / Objectives
The long-term goals of this project, applicable to all crops and researchers are [1] to facilitate plant breeders, and allied disciplines across species, to routinely and easily make actionable decisions and new discoveries using high-throughput field phenotyping (HTFP) data. The long-term goals relevant to maize breeding research and the Genomes to Fields (G2F) project are: [2] to automate routine measurements in maize, [3] identify novel phenotypic signatures of eliteness under stress that are infeasible or impossible to evaluate without HTFP tools and [4] to begin to apply these tools in a selection process.The supporting objectives of this project, are to: [1] Evaluate the Genomes to Fields Genomes by Environments (G2F-GxE) experiment under three different conditions of stress for three years. [2] Collect temporal data on the G2F-GxE experiment across three different HTFP platforms, including unmanned aerial vehicles (UAVs) and ground vehicles, and extract morphological and spectral measurements to compare entries. Of most value to the larger plant breeding community [3] develop methodology and approaches to analyze collected HTFP data to determine the value of extracted phenotypes and connection with genotypes. And finally, to make these tools available with helpful documentation to the larger plant research and breeding communities, and train other researchers to use these tools.?
Project Methods
The first objective will involve evaluating the genomes to fields genomes by environments (G2F-GxE) experiment. A 500 plot G2F-GxE hybrid panel will be grown under three different stress condition trials (planting date and irrigation treatments) in College Station, TX for each year of the project. Specific hybrid entries for the 500 plots of the G2F-GxE project will be chosen by a G2F-GxE sub-committee based on seed availability, community proposed experimental goals and geographical region. Weather data will be collected approximately every 30 minutes and field data will include days to anthesis, days to silk, terminal plant height (tassel and flag leaf), terminal ear height, and combine measured yield (grain weight per plot, moisture, test weight). Opportunistic rating notes will be taken in years when tests demonstrate strong visual segregating phenotypes including lodging, southern rust, southern corn leaf blight, leaf rolling, staygreen and, less often, common rust, northern corn leaf blight and charcoal rot; these provide opportunities to investigate unmanned aerial vehicle (UAV) and ground vehicle HTFP for biotic stress signatures. Data will be made available according to the online G2F data release guidelines. These tests will be evaluated for quality by variance component and repeatability estimates compared within and across replicates of the three plantings (and three years). Genetic mapping (a GWAS approach) will be performed in year three for alleles detected by HTFP. The second objective will collect temporal data on the G2F-GxE experiment across three different HTFP platforms and extract morphological and spectral measurements to compare entries. One team will use a fixed-wing UAV to collect aerial data using RGB and, as appropriate, near-infrared sensors. A second team will use a multi-rotor UAV to collect RGB morphological data, with the potential of multispectral and LiDAR data in years 1 & 2. Both teams will use ground control points for georeferencing and for spectral correction and have pipelines for mosaicking and making data available. A third team will use the ground vehicle with point sensors, as explained previously, with potential of using an unmanned ground vehicle by the second project year. Each team will strive to collect sensing data once per week throughout the growing season from planting through harvest. Images will be used to generate orthomosaics and digital elevation models for downstream analysis. Ground vehicle point sensor data will be output from a data logger as a matrix with GPS location, ready to analyze. The current best metric of success for UAV's are complete mosaicked images of the fields on a weekly basis throughout the growing season, at a high enough resolution to identify plants (~ 3cm) and ideally to resolve tassels (<1cm). Other success metrics will include correlations between the two UAV measurements, the ground vehicle measurement, and manual field measurements in addition to the genetic variance component and repeatability estimates within a mixed model framework within all measurement types. The third objective will develop methodology and approaches to analyze collected HTFP data, determine the value of extracted phenotypes and connect these with genotype. The statistical computing software R will be used to develop multiple tools including the automated methods to: A) Define and export plot coordinate boundaries (i.e. drawing polygons with names) from a few GPS points and a breeders field book; B) Automate plot calling and visualization for ground vehicle GPS datapoints; C) conduct outlier checking and analysis that leverages relationships between measurement platforms, sensors, time points and plots; and D) visualize and analyze plant growth curves. One basic metric of success is if genotypes can be differentiated using these approaches and if there is good consistency between replicates. There are few published examples of using these approaches in plant breeding so the ultimate long-term metric of success is if we or others adapt these measurements into our breeding programs. HTFP technologies and tools should 1) help to automate the collection of data that is already being collected and 2) provide phenotypes that were previously unable to be collected. It cannot be determined if these technologies will work until they are further developed and appropriate tools are available. If successful these tools will reduce labor, increase the ability to grow larger populations, and potentially identify new phenotypes that can lead to more accurate field breeding selection. For a commodity such as maize, the ultimate metric is for a cultivar to be developed with higher yield, higher yield stability, and/or higher farmer profit under diverse environments. To evaluate this, all HTFP phenotypes must and will be compared to combine harvested yield measurements. Finally, we will make these tools available with helpful documentation and train others to use them. Pipeline and analysis tools will be used and distributed in a phased rollout that allows troubleshooting beginning internally across the Texas A&M maize breeding program then by other G2F-GxE cooperators and TAMU breeders of other crops before a wide public release. If any tools or approaches are successful at improving end goals (yield, stress resistance, sustainability, nutrition, etc.) the larger plant breeding community will likely adapt them rapidly. The recipes of workflows and procedures used will be documented and distributed in a white paper at the end of the first year (potentially earlier on the project website) and similar information will also be included in the first publication targeted for the beginning of the second year. The R scripts will be well annotated so that researchers can easily modify them for their specific needs and specific experiments, and made available on the website as well. We will host a field day and workshop over two summers where students and researchers can go through the entire workflow.?

Progress 03/15/19 to 03/14/20

Outputs
Target Audience:Plant breeding, genetic mapping, and maize research communities are the main target audiences, with other maize Genomes to Fields (G2F) researchers especially relevant. These target audiences include the spectrum of undergraduate graduate students through senior scientists in both the public and private sectors. Graduate and undergraduate students that formally and informally participate in the project, students that attend field days and workshops, and students interested in learning about emerging areas of high-throughput field phenotyping (HTFP) are an important audience for advancing this interdisciplinary science. In the medium term, companies that sell seed, technology or agricultural services are an audience of users who could benefit from and contribute to this research, and the students trained in this research. As research needs and regulation barriers are identified, policy and decision makers may become an audience. More distantly, consumers and society will likely be interested in and benefit from this research; this research should result in better varieties that have higher and more stable yield, grown using less land, with fewer inputs, while providing more ecosystem services and minimizing the degradation of land. Changes/Problems:In 2019 we collected vastly more data than we had proposed. Existing staff has found it challenging to process and make this data and this volume of data publicly availible as proposed. We have therefore been approved for a one year no-cost extension of the project and we are looking for additional staff to help with the backlog of data. We are also collecting a 2020 dataset to ensure continuity of the project. What opportunities for training and professional development has the project provided?Students are taking classes in relevant fields to conduct this research. Students and post-doctoral scholars have attended and presented at multiple national and international meetings. Graduate students working on this project have or are leading first author publications. Software for the broader research, education, and extension community has been developed or is under development but has not yet been disseminated. How have the results been disseminated to communities of interest?To date the results have primarily been disseminated through presentations at local, national and international meetings; through publications, and through informal communications. A number of milestones and daily activities were also shared by the PD through social media (Twitter, Facebook). What do you plan to do during the next reporting period to accomplish the goals?In 2019 we are conducting the final field season of the project as proposed. Our Unmanned Aerial Flights have been more frequent and of better quality so far from what we have observed. Over the final reporting period we plan to finish up the field experiment, focus on data analysis, focus on making the data public, and focus on presentations and publications regarding the experiments and what we have learned. We are also specifically testing a number of hypotheses to make the mosaics of rotary wing data better, flying at different heights, using a reduced number of ground control points (after increasing the number from 12 [2017] to 39 [2018]), and comparing mosaicking software.

Impacts
What was accomplished under these goals? In 2018 a total of 1536 plots for this project from the primary genomes to fields (G2F) hybrid set were grown, divided across three environment and management conditions in College Station, Texas. A supplemental G2F trial of 208 plots of hybrids resulting from a G2F cooperators previous USDA-NIFA-AFRI project were also planted to look at a near isogenic hybrid series. The environments (E) and management (M) conditions included early planted irrigated and well fertilized (optimal), early planted dryland (non-irrigated and reduced fertilizer [sub-optimal]), and late planted irrigated and well fertilized (heat stress). These treatments provide relevant GxExM contrasts to evaluate genetic effects across producer realistic conditions. Standard agronomic data (flowering times, terminal plant and ear height, stand counts, grain yield, test weight and grain moisture) were collected, along with weather data. Two unmanned aerial systems (UAS) were flown throughout the growing season (around 30 flights) between a fixed wing aircraft collecting RGB and multispectral data and a rotary wing aircraft collecting RGB data. Unfortunately we experienced technical difficulties with both aircraft and many of the rotary wing flights could not be successfully mosaicked after canopy closure; we hypothesized a number of potential reasons for this and are addressing them in 2019. A similar G2F trial (1536 plots) has been planted in 2019 but not yet harvested. We have developed and improved UAS workflows and software. We have involved statisticians in the data analysis of 2017 G2F UAS derived tabular data which has further improved data analysis and reduced experimental error through better accounting for spatial and temporal variation. We have developed some of the first UAS derived plant growth curve fitting procedures (published). The growth curve approach was necessary and useful to compare data between tests planted of different dates or flown by UAS on different dates. We have given numerous presentations on this work, have one published paper, one submitted and another nearly ready to submit, all led by graduate students over this period. We have involved and trained many undergraduate, graduate, and some post-PhD level personnel in agronomy, plant breeding, remote sensing, statistical analysis, and the transdisciplinary integration of these areas for high-throughput field phenotyping (HTP / HTFP). The two most exciting outputs during this project period were 1) making public the first ever (to our knowledge) complete unmanned aerial vehicle season long survey of field experiments. Substantial effort was devoted to working with Cyverse staff (Dr. R. Walls) to ensure this dataset was a good case study on FAIR (findable, accessible, interoperable, and readable) standards for UAS data. This data will be very useful for data scientists and biologist developing new UAS analytical procedures, without having to plant trials or fly their own experiments. 2) We also released the first public software (R code) to our knowledge, to develop GIS shapefiles from plant breeding field books. This was previously a major bottleneck in our analysis and this tool should be very useful to anyone collecting small plot UAS data. In addition to the major outputs, we provided our 2018 data to the G2F project leader and this has since been made available to the other cooperators and the public.

Publications

  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Alper Adak*, Seth C. Murray, Clarissa Conrad, Yuanyuan Chen, Nithya Subramanian, Steven Anderson, Scott Wilde. 2020. Validation of Functional Polymorphisms Affecting Maize Plant Height by Unoccupied Aerial Systems (UAVs) Allows Novel Temporal Detection. Phenome 2020. Tucson, AZ. 2/24-27/2020.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Lane, Holly M.*, Seth C Murray, Osval A. Montesinos-Lopez, Abelardo Montesinos-Lopez, Jose Crossa, David K Rooney, Ivan D Barrero-Farfan, Gerald N De La Fuente and Cristine L. S. Morgan. 2019. Phenomic Prediction of Maize Grain Yield Using Near-Infrared Reflectance Spectroscopy. ASA-CSSA-SSSA International Annual Meeting. San Antonio, TX 11/10-13/2019. (Poster and Oral) ***2nd place poster in C-1 Division***
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2020 Citation: Adak, Alper, Jose Ignacio Varela, Dustin Eilert, Seth C Murray, Natalia De Leon, Jianming Yu. 2019. Identifying Loci for Delayed Temperate Flowering: Improving Southern Maize (Zea Mays L.) for Midwestern Seed Production. ASA-CSSA-SSSA International Annual Meeting. San Antonio, TX 11/10-13/2019. (Poster and Oral) ***1st place poster in C-1 Division***
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Lane, Holly M., Seth C. Murray*, Osval A. Montesinos?L�pez, Abelardo Montesinos?L�pez, Jose Crossa, David K. Rooney, Ivan D. Barrero Farfan, Gerald N. De La Fuente, Cristine L. Morgan. 2020. Phenomic Prediction of Maize Grain Yield from Near-Infrared Reflectance Spectroscopy of Kernels with Functional Regression Analyses. The Plant Phenome Journal 3: e20002.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Murray, S.C.* 2019. Plant phenomics and unoccupied aerial system (UAS, aka drone) phenotyping for Southern maize crop improvement, Madison, WI. 11/15/2019.


Progress 03/15/18 to 03/14/19

Outputs
Target Audience:Plant breeding, genetic mapping, and maize research communities are the main target audiences, with other maize Genomes to Fields (G2F) researchers especially relevant. These target audiences include the spectrum of undergraduate graduate students through senior scientists in both the public and private sectors. Graduate and undergraduate students that formally and informally participate in the project, students that attend field days and workshops, and students interested in learning about emerging areas of high-throughput field phenotyping (HTFP) are an important audience for advancing this interdisciplinary science. In the medium term, companies that sell seed, technology or agricultural services are an audience of users who could benefit from and contribute to this research, and the students trained in this research. As research needs and regulation barriers are identified, policy and decision makers may become an audience. More distantly, consumers and society will likely be interested in and benefit from this research; this research should result in better varieties that have higher and more stable yield, grown using less land, with fewer inputs, while providing more ecosystem services and minimizing the degradation of land. Changes/Problems:As is typical in field studies, changes and problems resulted from weather and technical delays. In the 2018 growing season there was sufficient rainfall, so there was little difference between irrigated and dryland trials, this is not a problem because we had different amounts of fertilizer in the two fields which still provides a contrast of environments. More unfortunately, the fixed wing Unmanned Aerial System (UAS) had technical issues and problems getting parts in a timely manner, so the early part of the season had lower than expected coverage. In 2019, weekly major rain events delayed our planting three weeks (March 20) beyond target, compared to 2017 and 2018 (March 1). It also eliminated our ability to get rows put up for furrow irrigation so all trials were dryland, but we had sufficient rainfall so there would have not been differences between irrigated and dryland anyhow. There were a few changes that were a function of human factors. First, given the challenges we continued to have with the ground vehicle for data collection (cannot go into muddy fields, often knocks down plants and break lodging/bent plants), as well as finding ground vehicle operators, and analysis of the ground vehicle data we have abandoned using it in favor of the UAS. The UAS equipment has been more reliable and efficient, as well as providing much more data (more than we can fully make use of), better data, and easier data-interpretation than the ground vehicles. Second, on the rotocopter we increased the number of ground control points substantially (12 in 2017 to 39 in 2018), however we still had problems mosaicking many of the rotocopter flights later in the season once canopy closure occurred. Third, there was miscommunication with G2F central seed planning in 2018 and instead of three 500 plot trials (optimal irrigated, optimal dryland, delay planted irrigated) each with exactly the same hybrids as proposed, we were sent three trials that between them had ~700 hybrids replicated twice or more. The challenge with this is in analysis since the trials were partial and not complete blocks. When something similar occurred again in 2019, combined with the delayed planting due to weather, we decided to plant all three trials at the same time and treat them the same (optimal dryland) to make subsequent analyses easier. While these changes and problems affected the experiment and data usability some, these issues were expected and learned from. Based on adjustments made, 2019 is likely to be our best dataset among the three years of the project. What opportunities for training and professional development has the project provided?Students are taking classes in relevant fields to conduct this research. Students and post-doctoral scholars have attended and presented at multiple national and international meetings. Graduate students working on this project have or are leading first author publications. Software for the broader research, education, and extension community has been developed or is under development but has not yet been disseminated. How have the results been disseminated to communities of interest?To date the results have primarily been disseminated through presentations at local, national and international meetings; through publications, and through informal communications. A number of milestones and daily activities were also shared by the PD through social media (Twitter, Facebook). What do you plan to do during the next reporting period to accomplish the goals?In 2019 we are conducting the final field season of the project as proposed. Our Unmanned Aerial Flights have been more frequent and of better quality so far from what we have observed. Over the final reporting period we plan to finish up the field experiment, focus on data analysis, focus on making the data public, and focus on presentations and publications regarding the experiments and what we have learned. We are also specifically testing a number of hypotheses to make the mosaics of rotary wing data better, flying at different heights, using a reduced number of ground control points (after increasing the number from 12 [2017] to 39 [2018]), and comparing mosaicking software.

Impacts
What was accomplished under these goals? In 2018 a total of 1536 plots for this project from the primary genomes to fields (G2F) hybrid set were grown, divided across three environment and management conditions in College Station, Texas. A supplemental G2F trial of 208 plots of hybrids resulting from a G2F cooperators previous USDA-NIFA-AFRI project were also planted to look at a near isogenic hybrid series. The environments (E) and management (M) conditions included early planted irrigated and well fertilized (optimal), early planted dryland (non-irrigated and reduced fertilizer [sub-optimal]), and late planted irrigated and well fertilized (heat stress). These treatments provide relevant GxExM contrasts to evaluate genetic effects across producer realistic conditions. Standard agronomic data (flowering times, terminal plant and ear height, stand counts, grain yield, test weight and grain moisture) were collected, along with weather data. Two unmanned aerial systems (UAS) were flown throughout the growing season (around 30 flights) between a fixed wing aircraft collecting RGB and multispectral data and a rotary wing aircraft collecting RGB data. Unfortunately we experienced technical difficulties with both aircraft and many of the rotary wing flights could not be successfully mosaicked after canopy closure; we hypothesized a number of potential reasons for this and are addressing them in 2019. A similar G2F trial (1536 plots) has been planted in 2019 but not yet harvested. We have developed and improved UAS workflows and software. We have involved statisticians in the data analysis of 2017 G2F UAS derived tabular data which has further improved data analysis and reduced experimental error through better accounting for spatial and temporal variation. We have developed some of the first UAS derived plant growth curve fitting procedures (published). The growth curve approach was necessary and useful to compare data between tests planted of different dates or flown by UAS on different dates. We have given numerous presentations on this work, have one published paper, one submitted and another nearly ready to submit, all led by graduate students over this period. We have involved and trained many undergraduate, graduate, and some post-PhD level personnel in agronomy, plant breeding, remote sensing, statistical analysis, and the transdisciplinary integration of these areas for high-throughput field phenotyping (HTP / HTFP). The two most exciting outputs during this project period were 1) making public the first ever (to our knowledge) complete unmanned aerial vehicle season long survey of field experiments. Substantial effort was devoted to working with Cyverse staff (Dr. R. Walls) to ensure this dataset was a good case study on FAIR (findable, accessible, interoperable, and readable) standards for UAS data. This data will be very useful for data scientists and biologist developing new UAS analytical procedures, without having to plant trials or fly their own experiments. 2) We also released the first public software (R code) to our knowledge, to develop GIS shapefiles from plant breeding field books. This was previously a major bottleneck in our analysis and this tool should be very useful to anyone collecting small plot UAS data. In addition to the major outputs, we provided our 2018 data to the G2F project leader and this has since been made available to the other cooperators and the public.

Publications

  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Steven L. Anderson II, Seth C. Murray*, Lonesome Malambo, Colby Ratcliff, Sorin Popescu, Dale Cope, Anjin Chang, Jinha Jung, and Alex Thomasson. 2019. Prediction of maize grain yield before maturity using improved temporal height estimates of unmanned aerial systems. The Plant Phenome Journal. 2:1 doi: 10.2135/tppj2019.02.0004
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Murray, S.C.* 2019. 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. Phenome2019, Tucson, AZ. 2/6-9/2019
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Murray, S.C.* 2019. Aerial and Ground Phenotyping Analytical Tool Development for Plant Breeders Using the Maize G2F Project. G2F meeting, Tucson, AZ. 2/6/2019.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Anderson, S.L.*, L. Malambo, S. Popescu, D. Cope, J. Jung, A. Chang and S.C. Murray. 2019. Utilizing structure from motion point clouds to estimate maize (Zea mays L.) height within a field-based breeding program. Phenome2019, Tucson, AZ. 2/6-9/2019. (Poster and Oral)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Lane, H.*, S.C. Murray, D. Rooney and C. Morgan 2019. Correlating Near-Infrared Spectra of Kernels to Grain Yield in Maize. Phenome2019, Tucson, AZ. 2/6-9/2019. (Poster)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Anderson, S.L.*, L. Malambo, S. Popescu, and S.C. Murray. 2018. Implementation of UAS Height Estimates within the Maize Breeding Program: Current Status. ASTA Policy and Leadership Development Conference, Washington DC. June 09-13, 2018.
  • Type: Journal Articles Status: Submitted Year Published: 2019 Citation: Lane, Holly M., Seth C. Murray, Osval A. Montesinos?L�pez, Abelardo Montesinos?L�pez, Jose Crossa, David K. Rooney, Ivan D. Barrero Farfan, Gerald N. De La Fuente, Cristine L. Morgan. (Submitted). Phenomic Prediction of Maize Grain Yield from Near-Infrared Reflectance Spectroscopy of Kernels with Functional Regression Analyses. The Plant Phenome Journal.


Progress 03/15/17 to 03/14/18

Outputs
Target Audience:Immediate target audience remains the plant breeding, genetic mapping, and maize research communities which include the spectrum of graduate students through senior scientists in both the public and private sectors. Graduate and undergraduate students that formally and informally participate in the project, students that attend the field days and workshops, and students that are interested in learning about the emerging areas of high-throughput field phenotyping (HTFP) are an important audience for advancing this interdisciplinary science. A surprising additional audience has been the popular press, who seem to take great interest in using unmanned aerial vehicles in plant breeding. In the medium term, companies that sell seed, technology or agricultural services are an audience of users who could benefit from and contribute to this research, and the students trained in this research. As research needs and regulation barriers are identified, policy and decision makers may become an audience. More distantly, consumers and society will likely be interested in and benefit from this research; breeding better varieties that have higher and more stable yield, grown using less land, with fewer inputs, while providing more ecosystem services and minimizing the degradation of land allows all of society to benefit. Changes/Problems:The main changes have been due to weather and technical delays. Our ground vehicle was of little use in 2017 because the field was nearly always too muddy to use it. In 2018 we have had challenges in finding student labor to collect routine data with it successfully, and have chosen to focus more effort on the unmanned aerial vehicles (UAVs) which we believe has more potential to help us to predict yield earlier in the season. On the UAVs, our improved workflows have identified some new technical issues (primarily trouble in mosaicking homogenous sections of field and artifacts in the mosaics); these issues were due to high image resolution, high genotype homogeneity, a larger field size and scope of data collection; these issues were further exacerbated by not enough ground control points and permitting weeds to grow in the field which covered static features (e.g. the soil). When combined with our improved sensitive analysis methods we have identified a number of flight dates we are now uncomfortable with the current mosaics and are working to address and improve these. For 2017 flights these are being addressed through different analytical methods. For the current years 2018 flights these are being addressed with an improved experimental design and protocol (add more GCPs, add more driveways/ alleys in the field, and to better control weeds). The issues experienced have led us to be approximately one year delayed in the dissemination of mosaics and in hosting a how-to workshop. New hires and more seniority and growth of key graduate personnel will likely advance these issues for next year. What opportunities for training and professional development has the project provided?Students are taking classes in relevant fields to conduct this research. Students and post-doctoral scholars have attended and presented at multiple national and international meetings. Training materials for the broader research, education, and extension community have been developed or are under development but have not yet been disseminated. How have the results been disseminated to communities of interest?To date the results have primarily been disseminated through presentations at local, national and international meetings; through publications, and through informal communications. The PI is recently or currently in leadership roles in various scientific societies (North American Plant Phenotyping Network, NAPPN; Crop Science Society of America, CSSA), in peer-reviewed journals (The Plant Phenome Journal, Crop Science) relevant to the project, which have been used as forums to share findings, issues and excitement from this project. What do you plan to do during the next reporting period to accomplish the goals?In the next reporting period we will grow our final season of three environment and management conditions of the G2F experiment with associated agronomic and unmanned vehicle data collection. We will further our data and analysis workflows. We will focus more on data cleaning and the subsequent dissemination of data and methods through presentations, and publications. We will host workshops to train others in these methods. We will finish multiple graduate students associated with this project.

Impacts
What was accomplished under these goals? We successfully grew and collected data on three environment and management conditions, each with two replicates of the 250+ genotype (G) maize Genomes to Fields (G2F) experiment in 2017, and have planted again in 2018. These environments (E) and management (M) conditions included early planted irrigated and well fertilized (optimal), early planted dryland (non-irrigated and reduced fertilizer [sub-optimal]), and late planted irrigated and well fertilized (heat stress). These treatments provide relevant GxExM contrasts to evaluate genetic effects across producer realistic conditions. Standard agronomic data (flowering times, terminal plant and ear height, stand counts, grain yield, test weight and grain moisture) were collected across these 1500+ plots. We also flew fixed and rotary wing aircraft over all plots throughout growth and have over 40 successful mosaics of our fields. We have extracted plant height and some vegetation indices so far over each plot and are working to extract additional features. We have developed and improved workflows and software; we have begun documenting these for broader distribution through presentations, online and ultimately through peer-reviewed publication. We have encountered a number of issues in the process and are documenting these as well. We have involved and trained many undergraduate, graduate, and some post-PhD level personnel in agronomy, plant breeding, remote sensing, statistical analysis, and the transdisciplinary integration of these areas for high-throughput field phenotyping (HTP / HTFP).

Publications

  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Malambo, L., S.C. Popescu*, S.C. Murray, E. Putman, N.A. Pugh, D.W. Horne, and M. Vidrine. 2018. Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery. International Journal of Applied Earth Observation and Geoinformation, 64, 31-42.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Pugh, N.A.; D.W. Horne; S.C. Murray, G. Carvalho Jr, L. Malambo, J. Jung, A. Chang, M. Maeda, S. Popescu, G. Richardson, T. Chu, M.J. Starek, M.J. Brewer, and W.L. Rooney*. 2018. Temporal estimates of crop growth in sorghum and maize breeding enabled by unmanned aerial systems. The Plant Phenome 1. doi:10.2135/tppj2017.08.0006
  • Type: Other Status: Published Year Published: 2017 Citation: Murray, S.C.*. 2017. Optical Sensors Advancing Precision in Agricultural Production. Photonics Spectra. 51:49+
  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Anderson, S.L.*, L. Malambo, S. Popescu, and S.C. Murray. 2018. Exploring the Genetic Variation in Maize Height Utilizing Unmanned Aerial Systems (UAS). 2018 Genomes to Fields USDA NIFA FACT Meeting, Ames, IA. 1/28-30/2018. (poster)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Cruzato, N.P.*, S.C. Murray, D. Cope, A. Chang, J. Jung, S.L. Anderson, and C. Ratcliff. 2018. Spectral and Three-Dimensional High-Throughput Phenotypes As Indicators of Plant Variability in a Maize Breeding Program. 2018 Genomes to Fields USDA NIFA FACT Meeting, Ames, IA. 1/28-30/2018. (poster)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Ratcliff, C.*, S.C. Murray, G2F Collaborators. 2018. Comparison of Growing Degree Days and Crop Heat Units of Maize Hybrids in Texas for the Accurate Prediction of Anthesis and Silking. 2018 Genomes to Fields USDA NIFA FACT Meeting, Ames, IA. 1/28-30/2018. (poster)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Cruzato, N.P.*, S.C. Murray, D. Cope, A. Chang, J. Jung, S.L. Anderson, and C. Ratcliff. 2017. Spectral and Three-Dimensional High-Throughput Phenotypes as Indicators of Plant Variability in a Maize Breeding Program. 2017 Annual Meeting. Tampa, FL 10/22-25/2018. (oral and poster)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Anderson, S.L.*, L. Malambo, S. Popescu, and S.C. Murray. 2017. Exploring the Genetic Variation in Maize Height Utilizing Unmanned Aerial Systems (UAS). 2017 Annual Meeting. Tampa, FL 10/22-25/2017. (oral and poster)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Murray, S.C.* 2018. Transdisciplinary Frontiers in Field Based Phenomics. UF Plant Science Symposium, Gainsville, FL. 1/25-26/2017.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Murray, S.C., S. Popescu, D. Cope, L. Malambo, S. Anderson, N. Cruzato, C. Ratcliff. 2017. Aerial and Ground Phenotyping Analytical Tool Development for Plant Breeders Using the Maize G2F project. National Association of Plant Breeders (NAPB) Annual Meeting, Davis, CA 8/7-10/2017
  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Murray, S.C.* 2018. Field Phenomics; the Next Difference Maker in Crop Improvement. Big Data Driven Agriculture USDA NIFA FACT Workshop, Washington DC. 2/26-27/2018.