Previously Funded Seed Grants

Round 2 Funding 11

Application Deadline: September 19, 2021

Creation of a database designed to promote dairy cow welfare using non-invasive phenotypic indicators of heat stress

Courtney Daigle, Brenda Murdoch, Scott Crawford, Barbara Jones, James Koltes, Jennifer Spencer, Juan Pinero, Sushil Paudyal, Clare Gill

There is a need to characterize the variability of the dairy cow heat stress response and to identify non-invasive phenotypic indicators of heat stress that can be automatically detected using existing technologies. We aim to

  1. Characterize regional variability in productivity responses to heat stress
  2. Identify and collate the needed phenotyping data to characterize the heat stress response
  3. Develop strategies for integrating disparate data types from animal monitoring systems to create a relational database of non-invasive phenotypic indicators of heat stress

GPS collars as precision agriculture tools for managing extensive rangeland production systems

Andrew Hess, Scott Huber, Tracy Shane, Robert Washington-Allen, Mike Cox
Download Narrative

Animals in extensive production systems are faced with many environmental challenges which may impact their ability to perform. We will use GPS units as a precision agriculture management tool to make land-use management decisions to maintain a healthy ecosystem, track animal behavior, and develop novel trait definitions for individual performance in a rangeland landscape. We expect GPS collars will provide a means to address the economic and environmental costs of an extensive sheep operation by providing quantitative measures of animal behavior in a rangeland environment.

Harnessing Ag Genomics Data to link genotype to phenotype

James Koltes, Chris Tuggle, Peter Harrison, Alenka Hafner

Plant and animal communities are accelerating the creation of functional genomics data. To capitalize on these investments, better methods and data standards are needed to integrate disparate datatypes, predict regulatory elements and glean new insights. We propose to bring together experts in plant and animal functional genomic data reuse and sustainability for a workshop and on-going discussion groups to identify and prioritize shared needs for data re-use tools to link genotype to phenotype.

Community engagement to improve standards and integration for genotype, phenotype, and environmental data for model and non-model plants

Irene Cobo, Meg Staton, Jill Wegrzyn

While the technical aspects of data integration are achievable, the metadata collection required for robust meta-analysis of G2P and G2E studies, especially for non-model plant systems, remains a hurdle. We will develop a fully FAIR data submission module (TPPS) that can be implemented by all Tripal plant databases, integrate the WildType mobile application to collect trait data for landscape-based studies, and train the scientific community from biocurators to Tripal database administrators.

Democratizing the access to artificial intelligence solutions for underrepresented and non-expert communities

Joao Dorea, Tiago Bresolin

Currently, the biggest challenge for underrepresented and non-expert users is to have access to AI techniques through a more user-friendly interface to perform basic AI tasks without requiring extensive expertise in the corresponding areas. In this project, we will develop an open-source software to democratize the access to AI techniques. Such software will be used to perform image classification by training new and customized deep learning algorithms on datasets provided by the user. Our goal is to create more accessible user interfaces so that coding ability is not a barrier.

Event-based plant phenotyping using deep learning: Algorithms, tools and datasets

Sruti Das Choudhury, Ashok Samal, Srinidhi Bashyam, Yufeng Ge

Event-based phenotyping analysis refers to the timing detection of the important events in a plant's life. This research will develop deep learning-based algorithms for emergence timing detection and growth tracking of seedlings using time-lapse image sequences, and detecting flowers and fruits from multi-view images to compute reproductive stage phenotypes. We will publicly release a benchmark dataset to develop and evaluate the algorithms. A software tool called iPlantSeg+ will be released to allow non-experts to perform segmentation and compute common phenotypes.

Developing a cost-effective method for collecting informative, population-level molecular phenotypes

Troy Rowan, Jon Beever, Kurt Lamour, Liesel Schneider
Download Narrative

We propose using a sub-$5 targeted gene expression approach as a molecular phenotype in beef cattle. This proposal aims to computationally identify 500 high-information genes that will be assayed in ~1,500 beef stocker calves. We will explore the utility of using these expression counts to predict future calf performance & health outcomes and as a latent phenotype. While we focus on beef cattle, we expect that this technology could be applied across species and genome to phenome applications.

Developing a new machine learning tool for improved genomic selection in non-model systems

James Polashock, Joseph Kawash, Abdollah Dehzangi

Identifying genetic adaptation in non-model systems (NMS) often cannot be sufficiently addressed using standard marker assisted selection (MAS) methodologies. While MAS is indispensable for the selective breeding of traits, NMS have not been able to take advantage of advancements in MAS due to the high overhead and imperfect data they often face. NMS contend with imperfect pedigrees, smaller populations, missing phenotypic/genotypic information, and complex interacting genetic components. Machine learning (ML) methods have shown to be tolerant of these biases and offer an alternative means of providing markers for genome selection. In spite of the potential for ML to vastly improve MAS in NMS, little is available by way of tools for researchers to utilize for breeding programs. We plan to address these problems through

  1. Development of an effective ML-based algorithm tailored for genome selection
  2. A simple to implement tool for use by those that are familiar with breeding/MAS

This tool will identify variant locations that are contributing to phenotypic variation of a dataset without adding to user workload by utilizing high throughput genotypic and phenotypic information that is common to a MAS breeding program. The selected variant sites will be utilized as the basis of genetic markers for population screening and selection towards the improvement of germplasm. This methodology would reduce the resources needed for MAS in non-model crop species or those with complex phenotypic targets.

Sharing Unoccupied Aerial System (UAS) based high-throughput plant phenotyping data via public cloud

Jinha Jung, Zhou Zhang, Alison Derbenwick
Download Narrative

The main goal of this project is to develop online educational material for managing and sharing UAS based HTP data using public cloud services. Although the importance of the FAIR principles in data-intensive science has been addressed multiple times, little attention has been paid to management and sharing of the big geospatial data generated from the UAS imageries yet. We propose to develop online educational material to provide tutorials on how to share the geospatial data products generated from the UAS data with the general public as web services.

Cross-species genomic analysis of photosystem II: Building connections from molecular structure to phenotype

Carmela Rosaria Guadagno, Marilyn Gunner
Download Narrative

The use of scale-invariant properties can improve our understanding of genome to phenome associations. This project uses the first principles of biophysics to develop cross-scale correlations between photosystem II related genes and drought phenotypes for agricultural species. We will perform a comparative genomic study searching for changes in the sequences to be imported into protein crystal structures for molecular modeling. Modeled water affinity across species will be correlated to existing phenotypic information to build associations for the ability of plants to grow and strive under water limitations.

Impact of breed type on beef production and sustainability

Kara Thornton, Sulaiman Matarneh, Brenda Murdoch, Gordon Murdoch

Despite years of research, there is not a clear understanding of how genotype contributes to phenotype of beef cattle. Our long term goal is to determine how underlying genetic differences present between cattle of different breed types translate to differences in animal performance, carcass quality, environmental impact and therefore economic viability. The overall objective of this proposal is to gather preliminary data to better understand how genetic differences relate to economically important traits and establish a resource for future genome and phenome comparison and manipulation.

Round 1 Funding 7

Application Deadline: March 19, 2021

Empowering High-Throughput Phenotyping using Unoccupied Aerial Vehicles (UAVs)

Max Feldman, Filipe Matias, Jennifer Lachowiec, David LeBauer
Download Narrative

The AG2P Initiative strives to connect genotype to phenotype in many environments. A growing source of phenome data in agricultural research across animals and plants are Unoccupied Aerial Vehicles (UAV). In this proposal we build a foundation to empower more researchers to use UAS. In the objectives, we will complete an international survey of agricultural animal science and plant science researchers of UAV use and build a community-informed web resource to provide instructional information and benchmarking tools to support the growing number of UAV users.

Expected Outcomes and Deliverables: In summary, this project will provide the foundation to accelerate high-throughput phenotyping in agriculture to support the mission of AG2PI. A survey will assess how UAV imagery has been applied to plant and animal sciences in agriculture and identify obstacles common among research groups that will pinpoint potential solutions. Simultaneously, developing standardized best practices will support UAV adoption and reduce barriers to entry. These efforts will be shared through workshops, videos, conferences, and manuscripts.

Ethics, Diversity and Inclusivity in G2P Research

Cassandra Dorius, Shawn Dorius, Kelsey Van Selous, Rachael Voas
Download Narrative

The AG2P Initiative strives to connect genotype to phenotype in many environments. A growing source of phenome data in agricultural research across animals and plants are Unoccupied Aerial Vehicles (UAV). In this proposal we build a foundation to empower more researchers to use UAS. In the objectives, we will complete an international survey of agricultural animal science and plant science researchers of UAV use and build a community-informed web resource to provide instructional information and benchmarking tools to support the growing number of UAV users.

Sustainably supporting worldwide food production is a wicked problem of immense scale and complexity. As such, there is an urgent need for novel ideas and technological innovations in the agricultural research. One strategy for rapidly infusing new ideas into existing knowledge networks is by making these person-centric networks more diverse, data more accessible to relevant stakeholders, and by infusing current practices with the kinds of Ethical, Legal, Social, Ecological, and Economic (ELSEE) considerations that will transform agricultural genome and phenome research practices. Bringing underrepresented groups to the table is one way to infuse AG2PI with new ideas. Improving and expediting knowledge transfer (e.g. data sharing), more effectively communicating research findings to the general public, policy makers, and funding agencies, and developing new science practices, can also help AG2PI to achieve sustainable genetic improvements. We propose to advance the aims of the AG2PI by conducting social science research to encourage cross-fertilization of AG2P data and ideas and motivate agriculture focused analysis from an ELSEE perspective. We will also create human-centered

Seeding public-private partnerships for AG2P training

Addie Thompson, Tammy Long, Jyothi Kumar
Download Narrative

The main goal of this project is to form public-private partnerships to expand meaningful interactions between industry and the public sector. Outcomes will include:

  1. Enhancing graduate student training through the use of current real-life AG2P project scenarios and datasets;
  2. Generating public educational resources including datasets and code for use by other scientific communities for AG2P training;
  3. Serving as a model and test bed for PPP engagement and seeding meaningful ongoing collaborations in interdisciplinary groups.

Optimizing 3D canopy architecture for better crops

Bedrich Benes, Duke Pauli, Fiona McCarthy, James Schnable

Implementing machine learning approaches to connect genotype-phenotype has been hindered by the lack of available, labeled training datasets. To overcome this limitation, we will use geometric modeling and object reconstruction, using point cloud data, to develop simulated datasets of organismal development. These simulated growth models will generate labeled datasets which can be used by machine learning algorithms as training data to study the complexities of phenotypic diversity. Moreover, we will develop and test a system for illumination estimation of the virtual crops.

Aim 1: We will provide a large, labeled dataset of point cloud data for sorghum plants with varying precision for ML and evaluation by the plant science community. We will develop the generative procedural model of sorghum that will be parameterized by branching angles, plant age, etc.

Aim 2: We will provide a highly parallel path tracer simulating sorghum illumination at a photonic level at varying wavelengths. This simulator will be expandable for further BRDF and BTDF values and will be parameterizable for varying longitude and latitude.

  1. Novel 3D simulations of sorghum grown at high-density with plant parts segmented & labeled,
  2. Plant and organ labeled simulated point cloud data for sorghum at high planting densities,
  3. ML models using simulated segmented plant data tested on greenhouse and field LIDAR data,
  4. Improved understanding of light interception and interaction dynamics with plant canopy,
  5. Engagement with animal researchers to assess how to extend approaches to animal science,
  6. Hosted AG2PI workshop to engage with plant and animal scientists and expand community.

Machine learning competitions for G2P and end-of-season phenotype prediction

Abby Stylianou
Download Narrative

In this project, we will organize competitions to engage with the broader machine learning community to produce models that can answer phenomic questions, using curated datasets from the Department of Energy ARPA-E Transportation Energy Resources from Renewable Agriculture Phenotyping Reference Platform (TERRA-REF) program [Burnette et al., 2018]. The TERRA-REF program aimed to transform plant breeding by using remote sensing from a state of the art field scanner gantry system, seen in Figure 1 (top) to increase the speed at which plant traits can be measured. The field scanner includes a number of sensors, including a millimeter resolution laser 3D scanner, high resolution stereo-RGB cameras, multiple hyperspectral sensors, and a thermal camera, among others (example data products are shown at the bottom of Figure 1). Over the course of several seasons, this sensor collected over a petabyte of sensor data for bioenergy sorghum lines, their corresponding genetic data, a large volume of ground truth measurements of plant phenotypes and growing conditions, and a baseline set of algorithmic approaches for extracting phenotypic data.

We propose that structured machine learning contests are an excellent way to share data and ensure that the desired scientific questions are actually the ones that are answered. Contests, where a specific problem is shared, training and testing data are provided, and a specific evaluation protocol is defined, are a frequent and popular means of advancing results to specific questions within the machine learning community. For example, the Fine-Grained Visual Classification community hosts annual contests on difficult visual recognition problems, such as the iNaturalist contest to recognize different species in image data, which had 1,477 submissions last year from 249 different competitors [Van Horn et al., 2018]. These communities are hungry for well organized datasets with specific scientific questions.

Identifying Educational Resources and Gaps in AG2P Data Science Across Plant and Animal Agriculture Genomics

Breno Fragomeni, Cedric Gondro, Margaret Young, Gabriella Dodd, Tasia Taxis
Download Narrative

Recent developments in genomics and the implementation of new technologies in agriculture have enabled a new research horizon in the field (Harper et al., 2018, Morota et al., 2018). The quality and quantity of genomic data, including microbiome, gene expression, high density SNP markers, sequence data, among others, have great potential to improve both plant and animal science enterprises. However, with these new developments a new challenge has arisen: practitioners must be able to manage the large data sets that result from these technologies – a skill that has traditionally is not been taught during the training of agricultural scientists (Eisen, 2008).

The goal of the Seed Grant is to catalog the available resources and resource gaps in data science to support the Agricultural Genome to Phenome (AG2P) initiative and to outline solutions to fill the gaps. We will develop surveys to identify how aware students and researchers are of the available resources. Additionally, we will create an online repository linking to available training materials in both plant and animal agricultural data science. In this repository we aim to provide the community a unified access point to information about workshops, seminars, online and in-person classes, and course curricula for different career stages. We will prepare a white paper describing our findings based on the survey and the catalog, focusing on how to advance data science education in AG2P. We will fund a graduate student to carry out this work. This student will work in the laboratory of PI Fragomeni with additional support from our team of investigators. We will use the results of the seed grant as preliminary data to apply for a large educational project to develop solutions to the needs identified in this project.

Cattle Genome to Herd Phenotyping for Precision Agriculture

Stephanie McKay, Darren Hagen, Robert Schnabel, Brenda Murdoch

The overarching goal of the Cattle Genome to Herd Phenotyping for Precision Ag initiative is to exploit new phenotyping technologies and high throughput genomics to improve cattle productivity and profitability. To accomplish this, we will establish a network of researchers from a variety of disciplines and agencies to facilitate generation and implementation of next generation phenotyping technologies in cattle and ascertain existing resources.

Expected Outcomes and Deliverables: PIs McKay, Hagen, Schnabel and Murdoch expect to form a CG2HP working group consisting of scientists from a variety of disciplines (i.e. nutrition, physiology, engineering and economics). Scientists will represent leaders from universities, government, industry and breed associations. This working group will be expected to attend an initial virtual meeting in September and meet in person in San Diego in January 2022. A CG2HP concept presentation will be given at PAG and a concept paper addressing the needed phenotyping data and technologies necessary to implement CG2HP in cattle will be generated and published. Further directions and future action items will be discussed, including future funding opportunities. Additionally, a presentation will be given at an AG2PI conference or workshop.