Soybean production and profitability are impacted by crop achieving its yield potential. There are two main components of realizing the true genetic potential: (1) the assembly of favorable genetic combination of grain yield genes, and (2) the protection of yield from various deterrents including pests and diseases, and performance maximizing in a diverse set of growing and soil conditions. Our goals are to improve agricultural production output and positively impact IA farmers and the agricultural industry through the development of new products (cultivars and germplasm), gene discovery, research insights on pertinent topics of importance to farmers, processors, and consumers, and developing selection strategies that lead to higher yield and new products that will improve market penetration and expand export markets.

Our research projects combine hardware and software solutions to solve phenotyping bottleneck, which streamlines breeding and trait study pipeline for yield gain and improve protection traits. Our team uses digital phenotyping above- and below-ground traits and gleans insights using machine learning analytics. Each graduate student develops hardware tools and software solutions in a collaborative initiative; and are supported by scientific and professional staff to accomplish these goals. We mentor the next generation of plant breeders, develop and participate in teams that can work towards improved productivity and profitability of producers and processors, and provide enhanced nutritional quality for animal and human health. We integrate breeding and research activities with student mentoring and learning experiences. It is important to point out that most of incoming students came from a farming background with minimal or no computer programming knowledge, but have now developed expertise in this area making them valued in the job market, and also take this knowledge to their farms to help improve their crop production. They are the first generation of high-tech expert plant breeder employees building cultivars for our farmers geared toward technology driven higher profitability and sustainability. The fact that they come from farming background ensures that the future generation of US plant breeders will continue to be passionate about farmer issues and work for advancing their interests.
We utilize team based collaborations with faculty members from different disciplines, departments and colleges including engineering, social sciences, and statistics. Our group has become a research nucleus that brings together multiple researchers from several departments and industry. This creates wealth for the state and opportunities for IA farmers. We routinely receive requests from various stakeholders to access intellectual property we have created.

Our project goals are to develop superior soybean cultivars for Iowa farmers, to provide high yield and protection against crop stressors to keep Iowa farmers competitive and profitable. Our long-term team goal is to build highly desirable trait packages for our Iowa farmers. These will include an arsenal of performance genes and resistance against insects and diseases in soybean. This project will lead to new and improved varieties, new digital technology insights, and research in the area of aerial and ground robot based phenotyping. Our efforts to integrate performance using high throughput phenotyping (ground and aerial) continues to provide national and international prominence to our breeding and research activities and lead to benefits to Iowa farmers, farm economy and industries.

Research Goals: To develop superior soybean cultivars for Iowa farmers, to provide high yield and protection against biotic and abiotic factors to keep Iowa farmers competitive and profitable. Our long-term team goal is to build highly desirable trait packages for our Iowa farmers. These will include an arsenal of performance genes and resistance against insects and diseases in soybean. Our efforts to integrate performance using high throughput phenotyping (ground and aerial) continues to provide national and international prominence to our breeding and research activities and lead to benefits to Iowa farmers, farm economy and industries.

Objectives: The main objectives of this project are to (1) increase soybean seed yield using genetic and phenomics tools, (2) improve seed quality (develop clear hilum, high oleic, high sucrose) varieties for increased market capture, and (3) develop breeding population to improve protection traits (biotic and abiotic stress tolerance). We will focus on infusion of engineering and data analytic tools, as well as work with a larger population pool.

• Short term: development of breeding populations for objectives listed in this project.
• Long term: cultivar and germplasm lines that meet the requirements of Iowa farmers and industry of high yield trait with yield preservation.
• Proposed specific deliverables (long term; 2023): Checks used in MG 1 (IAS19C3), MG2 (IAS25C1), and MG3 (IAS31C1) will be used as benchmarks for yellow hilum soybean. These three varieties will be used as checks, as these are our recently developed varieties from ISU (with yellow hilum). Additionally, multi-location and -state testing will include conventional checks (except IA2102, all are non-yellow hilum). For example, in MG1, U11-917032 will be used as an additional check. In MG2, IA2102 will be additional check. In MG3, LD11-2170 will be an additional check. Some of the tests will include private company lines as checks. These additional checks are used in the northern U.S. states cooperative tests, and their usage will ensure farmers will have the ability to compare ISU lines with competitive checks.

Update:
In 2022, ISU experimental lines were entered in the Uniform Soybean tests, which are grown in locations across several mid-western states. Several candidate varieties performed well meeting the project objectives. These varieties have been advanced for further testing in 2023. Finally, several varieties with improved SCN tolerance were also identified.

Highlights:
In 2022, ISU experimental lines were entered in the Uniform Soybean tests, which are grown in locations across several mid-western states. This information was also provided in 2022 final report.
* In UT1 (Uniform test, maturity group 1; 14 locations across multiple-states): A16806-76 yielded 63.5 bu/ac, which is 11.5% higher seed yield than the mean of four checks. It had a seed protein of 42.6% (dry weight basis), which is 3.6% higher than the mean of the check (39% on dry weight basis). It was later maturing entry therefore it will be evaluated in early MG II test in 2023. However, the combination of seed yield-protein is very exciting and delivers on the project objectives.
* In UTIIA (Uniform test, maturity group 2 early): IAS19C3, IAS25C1, IAS31C1 completed their final year of multi-state testing. In 2023, IAS31C1 had 4.5% higher seed yield and 0.4% unit higher seed protein than the mean of the four checks. IAS25C1 had 2.3% higher seed yield and 1% unit higher seed protein than the mean of the four checks. IAS19C3 had 0.7% higher seed yield and 2.4% unit higher seed protein than the mean of the four checks. IAS25C1 and IAS31C1 also have soybean aphid tolerance. These three varieties have yellow hilum and have seed an industry acceptance with licensing agreements signed with various entities.
* In UTIIA (Uniform test, maturity group II early, 12 location of data across multiple locations): A15131-10 was the fourth highest yielding variety and had 5.9% higher seed yield than the mean of checks. One check that is 4 days later maturing than this line out yields this variety (74.2 bu/ac versus 75.7 bu/ac for the check). In 2021, across 10 locations of testing, it was 8.7% higher seed yield (77.8 bu/ac) than the mean of checks.
* There are other notable performers in first year of multi-state testing. For example, A16371-79 (in MG II) had 4.5% higher seed yield than the mean of the checks. It is six days earlier maturity than the highest yielding check but yield difference was only ~3.5 bu/ac compared to this later maturity check. In MG III, A16373-101 was the second highest yielding line in the test (11 location of multi-state data).
* In MGII, high oleic acid lines with >75% HO were identified. These lines had seed yield similar to the mean of check (checks are not HO).
* In MGIII test, a higher sucrose line with lower raffinose and stachyose content was identified that had seed yield similar to the checks.
* One variety was disclosed to ISURF. IAS25HPHS1 is a high protein, higher sucrose large seeded line with yellow hilum and seed yield similar to relevant check. This line attracted interest from industry, and foundation seed was sold. More details are included next.
New variety commercialized: Based on multi-state testing for yield, agronomics, quality and yield protection traits, foundation seed increase was completed for IAS25HPHS1. Invention disclosures were made to ISURF during the reporting period. This new variety combines good seed yield with high protein, higher sucrose, low raffinose, low stachyose, and larger seed size. It has a maturity 2.3-2.7 and will meet the need of companies and farmers interested to grow a food grade soybean due to clear hilum color along with favorable combination of yield, protein, carbohydrate and seed size traits. All foundation seed that was produced, has been purchased by companies showing a promising start for this variety. This variety provides farmers access to a higher protein line (49-50% meal protein) with protein levels of >42% on dry weight basis compared to generic beans that have ~36-37%. It also has higher energy due to higher sucrose that may be of interest to the aquaculture industry. The lower levels of raffinose and stachyose is a desirable trait for food industry applications. Similarly, larger seed size is also desired by soy food industry.

* In 2022, foundation seed demand for ISU varieties was high and seed sales were completed for four varieties.
* In the reporting period, a safe and successful harvest was completed in fall of 2022. Post-harvest, seed processing and trait data acquisition was also completed in late fall and winter. Selection decision on advancements were made. Additionally, plant material was sent to winter nursery to get the second crop cycle. Preparations for 2023 planting were initiated, and at the time of reporting packaging of 2023 was in full swing.

Student updates on their research projects:
1. Matt Carroll graduated with a Ph.D. He is working as a research scientist, Iowa Soybean Association’s analytics team.
2. Clayton Carley graduated with a Ph.D. He is working as a Field Experimentation Lead at Corteva.
3. Sarah Jones is working on a project that is combining multiple sensors to study plant growth and development for creating a tool that helps predict plant response in multiple scenarios. She is focusing on breeding for drought tolerance. She mentored an undergraduate student, Caitlyn Bruntz, on a project, which won the first prize in the R F Baker Symposium for undergraduate research.
4. Liza van der Laan is working on a project that is geared on improving soybean protein, and build heat tolerant varieties. Liza mentored undergraduate student Heidi Dornath, won the third prize in the R F Baker Symposium for undergraduate research.
5. Joscif Raigne is working on a project that is helping incorporate multiple sources of SCN resistance in elite varieties.
6. Sam Blair continued his work on the implementation of drone-based maturity estimation, and on use of ground robot for image-based yield estimation.
7. New student Srikanth Panthulgiri joined the team having worked in international research centers. He is aiming to work on improving excess water stress in soybean.

We continued our partnership with Iowa Soybean Association with funded projects, AIIRA, COALESCE and SIRAC that supported by USDA and NSF. These unique linkages are providing farmer motivated solutions through the use of machine learning and robotics that enhance breeding and crop production.

Update:
Variety development update:
Continued foundation seed production of three varieties was done through Committee for Agricultural Development (CAD): IAS19C3, IAS25C1, and IAS25HPHS1 was completed in 2023 crop season.
These varieties are non-GM, and yellow hilum with an excellent package of traits, and suitability for the state of IA. Contracts were established with multiple companies for seed sale of these lines demonstrating success of these lines.
IAS25HPHS1 combines good seed yield with high protein (meets or exceeds 48% meal protein), higher sucrose, low raffinose, low stachyose, and larger seed size. It has a maturity 2.3-2.7 and will meet the need of companies and farmers interested to grow a food grade soybean due to clear hilum color along with combination of yield, protein, carbohydrate and seed size traits. With previous efforts in soybean focused on high yield alone, we are lagging behind on high protein and therefore there is a demand for high protein soybean. Increased sucrose is a desirable trait for food grade applications. Foundation seed of this line was completed in 2022 field season.
New varieties will be disclosed to ISURF for foundation seed increase in 2024.

Breeding population updates:
We developed new populations, and F1, F2, F3 generations from segregating populations for breeding objectives were advanced using US and/or winter nurseries. We grew early generations in space planted nurseries and single pod picks were collected, while F4:5 single plants were pulled based on selection criteria. We identified single plant pulls for late MG1, MG2 and early MG3. Yield testing of progeny rows, preliminary yield tests, and advanced yield tests were completed. We continued our research work on robot-based yield estimation, and aerial imagery for trait selection.

Research project updates:
• Sarkar S, B Ganapathysubramanian, A Singh, F Fotouhi, S Kar, K Nagasubramanian, G Chowdhary, SK Das, G Kantor, A Krishnamurthy, N Merchant, AK Singh. 2023. Cyber-agricultural systems for crop breeding and sustainable production. Trends in Plant Science. DOI:https://doi.org/10.1016/j.tplants.2023.08.001
• Krause MD, HP Piepho, KOG Dias, AK Singh, WD Beavis. 2023. Models to Estimate Genetic Gain of Soybean Seed Yield from Annual Multi-Environment Field Trials. Theoretical Applied Genetics 136 (12), 252
• Herr AW, A Adak, ME Carroll, D. Elango, S Kar, C Li, SE Jones, AH Carter, SC Murray, A Paterson, S Sankaran, A Singh, AK Singh. (2023). Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding. Crop Science, 63, 1722–1749. https://doi.org/10.1002/csc2.21028
• Kar S, K Nagasubramanian, D Elango, ME Carroll, CA Abel, A Nair, DS Mueller, ME O'Neal, AK Singh, S Sarkar, B Ganapathysubramanian, A Singh. (2023). Self-supervised learning improves classification of agriculturally important insect pests in plants. The Plant Phenome Journal. 6(1): e20079.
• Young TJ, TZ Jubery, CN Carley, M Carroll, S Sarkar, AK Singh, A Singh, B Ganapathysubramanian. (2023). "Canopy fingerprints" for characterizing three-dimensional point cloud data of soybean canopies. Frontiers in plant science, 14, 1141153. https://doi.org/10.3389/fpls.2023.1141153
• Webster RW, M McCaghey, B Mueller, C Groves, FM Mathew, A Singh, M Kabbage, DL Smith. (2023). Development of Glycine max Germplasm Highly Resistant to Sclerotinia sclerotiorum. PhytoFrontiers. https://doi.org/10.1094/PHYTOFR-01-23-0009-R
• Rairdin A, F Fotouhi, J Zhang, DS Mueller, B Ganapathysubramanian, AK Singh, S Dutta, S Sarkar, and A Singh. (2022). Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean. Frontiers in plant science, 13, 966244. https://doi.org/10.3389/fpls.2022.966244
• Carley CN, MJ Zubrod, S Dutta, AK Singh. (2022). Using machine learning enabled phenotyping to characterize nodulation in three early vegetative stages in soybean. Crop Science, 00, 1– 23. https://doi.org/10.1002/csc2.20861
• Nagasubramanian K, AK Singh, A Singh, S Sarkar, B Ganapathysubramanian. (2022). Plant phenotyping with limited annotation: Doing more with less. The Plant Phenome Journal, 5, e20051. https://doi.org/10.1002/ppj2.20051

Conference papers:
• Feuer B, A Joshi, M Cho, K Jani, S Chiranjeevi, ZK Deng, A Balu, AK Singh, S Sarkar, N Merchant, A Singh, B Ganapathysubramanian, C Hegde. 2023. Zero-Shot Insect Detection via Weak Language Supervision. 2nd AAAI Workshop on AI for Agriculture and Food Systems (AIAFS). Washington DC, United States. Feb 13 2023. https://openreview.net/forum?id=VPDKe672pv
• Saleem N, B Ganapathysubramanian, A Balu, TZ Jubery, S Sarkar, A Singh, AK Singh. 2023. Optimized Class-specific Data Augmentation for Plant Stress Classification. 2nd AAAI Workshop on AI for Agriculture and Food Systems (AIAFS). Washington DC, United States. Feb 13 2023. https://openreview.net/forum?id=kPM87uCwFq
• Chattopadhyay S, ME Carroll, B Ganapathysubramanian, AK Singh, S Sarkar. 2023. Data driven ensemble learning for soybean yield prediction. 2nd AAAI Workshop on AI for Agriculture and Food Systems (AIAFS). Washington DC, United States. Feb 13 2023. https://openreview.net/pdf?id=vrXKC3eYMU
• Saadati M, S Chiranjeevi, A Balu, TZ Jubery, AK Singh, S Sarkar, A Singh, B Ganapathysubramanian. 2023. Out-of-distribution algorithms for robust insect classification. 2nd AAAI Workshop on AI for Agriculture and Food Systems (AIAFS). Washington DC, United States. Feb 13 2023. https://openreview.net/forum?id=39Eh1ifhsj
• Kar S., K. Nagasubramanian, D. Elango, A. Nair, D. S. Mueller, M. E. O’Neal, A. K. Singh, S. Sarkar, B. Ganapathysubramanian, A. Singh. Self-Supervised Learning Improves Agricultural Pest Classification. AI for Agriculture and Food Systems Workshop in Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2022), Virtual
• Cho M., K. Nagasubramanian, A. K. Singh, A. Singh, B. Ganapathysubramanian, S. Sarkar, C. Hegde. 2022. Privacy-Preserving Deep Models for Plant Stress Phenotyping. AI for Agriculture and Food Systems Workshop in Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2022), Virtual

Invited presentations in the reporting period:
• Singh AK (2023). “Cyber-Agricultural Systems – CPS in Agriculture.” Asian Association for Agricultural Engineering, India. Oct 2, 2023. Virtual.
• Singh AK (2023). “Cyber-Agricultural Systems in Crop Breeding and Production.” Plant Pathology, Entomology and Microbiology, seminar series. ISU. September 12, 2023.
• Singh AK (2023). “Cyber-Agricultural Systems in Crop Breeding and Production.” Keynote lecture in Machine Learning in Cyber-Agricultural Systems. Obihiro, Japan. Jul 3, 2023.
• Singh AK (2023). “Cyber-Agricultural Systems for Crop Production.” World Soybean Research Conference. Vienna, Austria. June 22, 2023.
• Singh AK (2023). “AI Institute for Resilient Agriculture and Translational AI Center at ISU.” Wageningen University. Netherlands. Jun 12, 2023.
• Singh AK (2023). “Integrating Technologies, and their Applications in Cultivar Development.” PhenoRob seminar series, U of Bonn, Germany. Jun 16, 2023.
• Singh AK (2023). “Breeding Crop for Climate Resilience.” International Conference on Vegetable Oils, India (virtual). Jan 18, 2023.
• Singh AK (2022). “Root Architecture and Nodulation Traits.” Tri-Societies Meetings. C01 division symposium. Baltimore. Nov 7, 2022.
• Singh AK (2022). “Diversity of disciplines, approaches and people for a more sustainable crop production.” P2IRC Symposium, University of Saskatchewan, Canada. Oct 25, 2022.

Professional development and research progress of students:
• Students who graduated with Ph.D.: Matheus Krause (Now at Corteva), Mariana Chiozza (Moved to Europe), Clayton Carley (Now at Corteva), Matt Carroll (Now at ISA).
• Sam Blair worked on drone-based maturity estimation, and on use of ground robot for image-based yield estimation. Research papers are in development, and deployments plans are underway.
• Joscif Raigne worked on the use of satellite data for estimating yield in breeding plots.
• Sarah Jones completed her projects that aims to combine multiple sensors to study plant growth and development to create a tool that helps predict plant response in multiple scenarios. Research papers are in development.
• Liza van der Laan completed her project to identify heat tolerant soybean accessions, and protein and oil prediction. Research papers are in development.
• Three new students started in the program: Juan Di Salvo, Srikanth Panthulugiri, Hernan Torres.
• Pre-University student mentoring: A.K. Singh’s group is involved in mentoring high school student for internships in the area of cyber-agricultural systems and phenomics. One of the students was recognized as the 2024 Regeneron Society For Science Top 300 Scholars – nations highest honor for high school students in science and technology. Two (previous) high school interns are working on scientific papers.

Staff, post-doctoral fellow, and graduate students continue to support the soybean breeding and research program.

Outreach:
• Soynomics video series (14 videos) to present research projects to a general audience: https://www.youtube.com/playlist?list=PLIwaQ0MQDrlIKtRC8_MW98q6noxpAzxdV
• Move towards Resilient Food Supply. https://movewhatmatters.com/move-toward/resilient-food-supply/
• ISU Agronomy Professor talks Innovations in Ag Research (Parts 1 and 2): https://www.iowaagribusinessradionetwork.com/isu-agronomy-professor-talks-innovation-in-ag-research-part-1/ and https://www.iowaagribusinessradionetwork.com/isu-agronomy-professor-talks-innovation-in-ag-research-part-2/
• US Farm report video (2022): https://www.youtube.com/watch?v=QPbBUtjoeuM

This project successfully advanced variety development and new varieties will be commercialized in spring’24 with foundation seed increase in 2024 crop season. Research projects in the area of cyber-agricultural systems and breeding innovations were successfully completed. We advanced variety development for drought, heat, flooding tolerance to meet the needs of industry and farmers. Several of our soybean varieties were adopted by companies demonstrating their success. We continued to work on the development of sophisticated engineering tools and solutions to improve soybean breeding and production. Research partnerships continued through federal funded projects, providing enhanced return on investment to Iowa farmers. Graduate students continue to advance breeding and production science.

The technology developed from this project, including varieties, germplasm, phenomic tools, phenotyping and screening methods will help the IA farmers, private and public breeders, and research community looking for outputs on increased yield, protection traits, and seed quality. Varieties developed for this project are dual purpose: generic and food grade therefore create and cater to a premium paying market. ISU varieties provide a seed cost advantage due to their lower price, and yield competitive with commercial varieties. The ISU lines do not have a traited herbicide gene, therefore can be used by small or large seed companies to integrate any herbicide trait for a more competitive market. Our varieties have been actively taken up by Iowa and mid-western seed companies through MTAs. The training of high caliber workforce to work as breeders and scientist in seed, chemical and agricultural companies is immeasurable but highly valued. ISA funding is also bringing in federal investments through research funding agencies, including new partnership projects between our team and ISA, which benefits ISA and ISU to advance farmer interests and profitability.