Project Details:

Field phenotyping using machine learning tools integrated with genetic mapping to address heat and drought induced flower abortion in soybean

Parent Project: This is the first year of this project.
Checkoff Organization:North Central Soybean Research Program
Categories:Environmental stress, Breeding & genetics, Technology
Organization Project Code:60065
Project Year:2023
Lead Principal Investigator:Krishna Jagadish (Texas Tech University)
Co-Principal Investigators:
Doina Caragea (Kansas State University)
William Schapaugh (Kansas State University)
Gunvant Patil (Texas Tech University)
Glen Ritchie (Texas Tech University)
Hamed Sari-Sarraf (Texas Tech University)
Impa Somayanda (Texas Tech University)
Henry Nguyen (University of Missouri)
Avat Shekoofa (University of Tennessee-Institute of Agriculture)
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Contributing Organizations

Funding Institutions

Information and Results

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Project Summary

A 30 to 80% flower drop in soybeans grown across different regions in the US is an unresolved and
persisting bottleneck that has limited soybeans ability to achieve the full genetic yield potential. The major
challenge has been the lack of robust, field-based high throughput phenotyping and analysis tools to
capture temporal variation in flower abortion and pod retention across large genetically diverse
germplasm. The multi-regional (KS, MO, TN and TX) and trans-disciplinary team will develop an image-based
field phenotyping system, integrated with deep-learning tools to capture large genetic variation in
flower abortion and pod retention under different soil and climatic conditions. A genetically diverse set of
250 genotypes including late group II, group III and early group IV will be tested under natural dryland
conditions in MO and KS, and under irrigated and severe drought and heat stress conditions in TX and
TN. Currently available deep re-sequenced genotypic data will be leveraged to identify environmentally
stable and region-specific genomic regions controlling flower abortion. This fundamental knowledge will
help discover molecular switches to enhance flower and pod retention, and thereby enhance yield
potential under diverse environmental conditions. The proposed project will address - Tools and
Technology for Soybean Improvement and utilizing these to induce Extreme Weather Resiliency. In
summary, the overall goal is to increase flower and pod retention by 20 to 30%, with a potential
to enhance yields by 10 to 15%, ultimately translating to an additional 400 million dollars to
the national soybean industry.

Project Objectives

Objectives (Year 1)
• Explore the genetic diversity in flower abortion under different soil moisture and climatic conditions
using a large diversity panel
• Develop an image-based field phenotyping system and deep-learning tools to precisely document
temporal dynamics in flower abortion and pod retention in genetically diverse soybeans
• Discover environmentally stable and region-specific genomic regions controlling flower abortion in
diverse soil types, moisture, and climatic conditions

Year 2 - Utilizing the findings from year 1, we will fine-tune the high-throughput phenotyping and deep-learning tools
to validate environmentally stable and region-specific genomic regions and identify candidate genes and
metabolites that control flower abortion and pod retention under different soil and climatic conditions (Year

Year -3 Initiate breeding populations development using germplasm with significantly higher flower and pod
retention, identify molecular markers and test CRISPR-based gene edited lines with higher flower and pod
retention under controlled environments and field conditions (Year 3).

Project Deliverables

- Identify novel soybean germplasm that have the potential to retain 20 to 30% more flowers, accompanied with a balanced source-sink relation to increase yield potential by 10 to 15%
- Common and regional soybean germplasm with increased flower retention identified and made available for breeding purposes
- A publicly available image-based high throughput phenotyping tool developed to track rate of flower abortion/retention to strengthen soybean breeding efforts
- Identify environmentally stable and region-specific genomic regions and molecular markers controlling flower abortion in soybean
- Identify QTLs and characterize promising genes controlling flower abortion using CRISPR-based gene editing technology
- Breeding populations to incorporate genes for increased flower and pod retention into elite germplasm for variety development

Progress of Work

Final Project Results

Benefit to Soybean Farmers

Retaining even a proportion of 30% to 80% of flower aborted under well-watered and stressful conditions, respectively, will allow for 10 to 20% increase in yield for the soybean producers in the US. This advantage can be extended to different soil and water available conditions, to support a wide range of soybean producers and is the major rationale for embarking on testing this hypothesis across four different soybean growing states with a focus on MG III to IV. The advantage proposed through this collaboration, will allow the soybean producers to gain additional economic return at the same level of investment i.e., with same seed cost, fertilizer level and management. With changing climate leading to an increase in temperature and lesser water available scenarios, the proportion of flower drop would increase proportionally, future lowering yield and producer profits. Hence, germplasm, breeding populations, novel QTL/genes and CRISPR edited lines developed with increased flower retention would help enhance the yield potential under current climates and retain the advantage even under future warmer and drier environments.

Performance Metrics

• Range in phenotypic variation associated with flower abortion and pod retention in different maturity
groups of soybean grown under different soil, moisture and climatic conditions determined.
• Image-based phenotyping system modified and established to count flowers and pods across all four
participating institutes.
• Deep learning tool developed can analyze images and acquire temporal changes in flower numbers
with minimal human interference, from images collected across all four locations.
• Candidate genomic loci identified for flower abortion under favorable and drought and heat stress

Project Years