Brief Summary of Minutes of Annual Meeting

Meeting Minutes (Summary):

1. Welcome and Introduction:
• Yufeng Ge welcomed the attendees and introduced the leadership team.
2. Introduction to Multistate Project:
• Carolyn Lawrence-Dill provided an overview of the multistate project, highlighting its collaborative nature and the benefits it offers, such as identifying new collaborations and mentorship opportunities.
• Responsibilities of the committee and the academic advisor were discussed.
3. USDA NIFA Program and Funding:
• Jessica Shade and Bisoondat ‘Mac’ Macoon shared information about USDA NIFA programs and funding opportunities.
• Changes in the grant management system were highlighted, along with various funding opportunities available through NIFA.
4. NC1212 Project Update:
• Yufeng Ge provided an update on the NC1212 project, including its objectives and last year's meeting at UNL.
• Updates from Erin Sparks, University of Delaware regarding corn lodging and innovative techniques for measuring stalk stability were shared.
5. IPPN Updates:
• Updates on the IPPN and its upcoming events, including the IPPS conference, were provided by Jennifer Clarke.
6. Discussion on Collaborative Research Opportunities:
• Attendees discussed opportunities for collaborative research, including diurnal variation of NDVI and the availability of G2F data.
7. Updates from Attendees:
• Shangpeng Sun discussed AI and computer vision applications for plant phenotyping.
• Michael Kudenov shared updates on optical polarization and its applications in remote sensing.
• Seth Murray provided insights into trait evaluation, phenomics selection, and deep learning techniques.

Action Items:

• Provide feedback on the Plant Breeding Roadmap by emailing PBRoadmapComments@usda.gov.
• Consider becoming a reviewer for NIFA panels.
• Explore collaborative research opportunities discussed during the meeting.

Next Meeting: The next NC1212 meeting will be held alongside IPPS in October 2024.

 Photo: attached.

State Reports:

Nebraska (Ge, Awada, Choudhury, Clarke, Ghosh, Schnable, Walia) 

Ge:

In 2023 growing season, UNL's Spidercam field phenotyping facility continued to conduct the ongoing four phenotyping experiments: (1) phenotyping of HIPS corn hybrids performance, (2) a cover crop biomass estimation experiment, (3) an experiment to phenotyping corn water use and nitrogen use efficiency using a commercial variety, and (4) an experiment to phenotyping soybean water use efficiency using two commercial varieties.

We also conducted a greenhouse-based phenotyping project with UNL's LemnaTEC system. This project used 1 corn hybrid and 1 corn inbred (B73) and studied the coupling effect of nitrogen and sulfur deficiency and its detection with multiple imaging modules.

Other involvement: The Spidercam site has grown Genomes to Fields hybrids for four consecutive years.

Awada:

Ongoing work:

• Leading and participating in large data pipeline development for multi-source image, continuous, and points plant phenomics data.
• AI and machine learning tools development for image analysis that are accessible and inclusive.
• Focus on understanding the response of plants in managed and natural systems to climate variability and change using multiscale imaging techniques from leaf to satellite.
• Develop remote sensing / image based tools to quantify the impacts of anthropological management, invasive species, climate, and disturbance (fire/grazing) on ecosystem services (productivity, diversity, soil health, water balance) by linking proximal and remote sensed images to crops/plant and trees ecophysiological traits (e.g., tree rings and leaf level characteristics to optical traits and vegetation indices derives from images at multiple scales) in managed and natural systems.

Funding:

• Data Geospatial Solutions and Training for Agricultural Researchers and Practitioners, ARS/USDA, PI T. Awada, $400,000, 2023-2024.
• Data Solutions for Climate Smart and Resilient Agriculture. ARS/USDA, Co-PI T. Awada, $630,000 (per year), 2023-2028.
• Big Phenomics Data Architecture – Agriculture. Nebraska Research Initiative. PI. T. Awada, $700,000, 2020-2025.
• Resilient and Productive Agroecosystems Associated with the LTAR Network. Platte River – High Plains Aquifer, Long-Term Agro-Ecosystem Research (LTAR) Network. ARS/USDA. P.I. T. Awada. $1.4 M. 2019-2024.
• Response of a Man-Made Forest to the Catastrophic Wildfires of 2022:  Recovery of the Sandhills Halsey Nebraska National Forest. McIntire Stennis/USDA. PI. T. Awada, $350,000. 2023-2027.

 Choudhury:

• Keynote speaker, International Conference on Systems and Technology for Smart Agriculture, Kolkata, India, December 19-20, 2023.
• Keynote speaker, Usefulness of AI in daily life: a closer insight, Surendranath College, Kolkata, India, December 21, 2023.
• Speaker, Segmentation Techniques and Challenges in Plant Phenotyping: Introducing the iPlantSeg+ Tool workshop, Plant Biology, August 2023.

Clarke:

• Image analysis and segmentation methods for in situ 3D X-ray plant imaging. Development of ground truth and digital twin data; AI-based approaches to image segmentation and trait extraction; new metrics for evaluation of segmentation methods.
• Other involvement: Member of AG2PI executive team with associated publications and presentations; lead of AG2PI seed grant mechanism.

Walia:

Ongoing work:

• “Comparative genomics and phenomics approach to discover genes underlying heat stress resilience in cereals” - NSF, FEC-Track 2, (2017-2023), Walia PI
• “Bringing Nutrition Back into Rice Yield Gain” - FFAR (2023-2026), Walia CoPI
• “Genetic and Physiological Novelties for Salinity Tolerance in Rice Created by Transgressive Segregation” - NIFA (2023-2026), Walia CoPI
• "High-Definition Spatiotemporal Phenomics and Trascriptomics of Developing Maize Kernel Under Heat Stress" - NAES, Walia PI 

Delaware (Sparks, Bao)

Sparks:

New: “Bendy Box: A new tool to study plant biomechanics in space” NASA EPSCoR

Continuing from previous period:

• “Quantifying Crop Biomechanics Across Plant Lifespans” - NSF
• “Understanding Supply and Support Trade-offs in Maize Aerial Roots” - Royal Society
• “Collaborative Research: Linking brace root development and function in maize” - NSF
• “Linking Pythium soil abundance to disease development in Mid-Atlantic maize” - Delaware Biosciences Association
• “Effect of rye shading on corn roots and shoots in traditional and short stature corn” - USDA
• “Systems-level characterization of Pythium pathogenesis, an emerging threat to maize production” – USDA

Other involvement: Genomes to Fields site                  

Minnesota (Hirsch, Pardey, Runck)

• Implementing multi-omics approach to understand drought, temperature, and nutrient stress: integrate high-information phenotyping, ionomics, and global gene expression data to understand the physiological, morphological, elemental, and transcriptomic response of stress across seedling development. 
• Efficacy of Mineral rover for high fidelity/temporal resolution of field FHB severity. Determine the efficacy of a phenotyping rover and subsequent machine learning algorithms to detect FHB in the field on wheat and barley. Goal to increase the speed and accuracy of FHB phenotyping, the number of spikes evaluated per plot, and the number of times FHB is evaluated in each season. 
• Utilizing hyperspectral imaging for quick detection of barley sprouting. Using hyperspectral imaging and machine learning techniques to determine timing of sprouting and sprouting variation across diverse barley germplasm.

Other involvement: Genomes to Fields; Kernza CAP

North Carolina (Kudenov)

• Implementing on-line grading sensors for the peanut breeding program (pod width, length and kernel width, length, weight).
• Developed a custom lens design for a single-color-channel (550 nm green light) polarization camera that will be deployed on Nebraska Lincoln's Spidercam system. Lens was assembled, integrated, and sensor calibrated and shipped for imaging over summer 2024. This will test our glare (BRDF) correction algorithm when combined with their NIR channel.

Other involvement:

• Part of a multi-state SCRI project for sweetpotato Guava Root Knot Nematode resistance, "Rapid Development of Marketable Root-Knot Nematode Resistant Sweetpotato Varieties: Translation of Genomics and Advanced Phenomics into On-Farm Crop Management Solutions". This has involved developing tools that can detect RKN damage coming into the packing facility, quantifying grade distributions of sweetpotatoes being harvested in the field, and looking at foliar signatures (Raman spectra) as related to RKN incidence.
• Part of a multi-state project, "Advancing Optical Technologies for Enhanced Quality Evaluation, Grading and Sorting of Sweet Potatoes", in which we are developing **high speed** sensors capable of detecting internal quality characteristics of sweetpotatoes using light scattering and interactance spectroscopy.

South Dakota (Mural, Yadav) 

Mural:

The Mural Lab and the Yadav Lab at South Dakota State University, in collaboration, are bringing together plant genetics, machine vision, and optical sensors and are currently engaged in high-throughput phenotyping of sorghum, maize, and wheat leaves and seeds using a portable hyperspectral imaging system. Our research aims to gather high-density data on seeds and leaves and correlate it with seed and leaf composition traits. This data will be instrumental in classification, genetic characterization, and association studies, ultimately facilitating targeted breeding efforts to improve crop yield, quality, and nutritional attributes. Additionally, we are pioneering AI-driven computer vision algorithms for disease and pest management.

Michigan (Thompson, Rouched)

Thompson:

Ongoing work:

• Drone-based imaging of diverse sweet corn varieties
• Multi-modal hyperspectral imaging of phenolic compounds and their relationship to tar spot disease in maize
• High-throughput modeling and prediction of plant growth in maize and sorghum
• Prediction of nutritional properties of teff from aerial imagery
• Modeling nitrogen accumulation, remobilization, and plant senescence in maize hybrids and inbreds

Accomplishments during reporting period:

• Integration of plant phenomics into graduate and undergraduate courses (CSS 844 – Frontiers in Computational and Plant Sciences, and UGS 202H – Honors Research Seminar on Plant Genomic and Phenomic Prediction)
• Processing and analysis of RGB and multispectral aerial imagery to predict plant growth and phenotypic traits in maize and sorghum, including nitrogen response in maize
• Successful modeling of maize kernel phenolic compounds (from LCMS) using spectral data (from FT-MIR)
• Other involvement: Genomes to Fields; Sweet corn CAP; NC7; NAPPN

Rouached:

The current project aims to better understand how plants cope with limited nutrients while maintaining their biological functions, such as photosynthesis, at the molecular, physiological, and ecological levels. plant phenotyping is key for this project.

Washington (Sankaran)

• Acquisition of a versatile plant phenotyping platform (PhenoPlant) to advance food and agricultural research (USDA-NIFA EGP, PI: Helmut Kirchoff), 2023-2026
• Smart monitoring and acoustic spray system for precision crop stress management in controlled environment (BARD), 2023-2026
• High throughput phenotyping of 700 pea accessions for yield components (PI: Marilyn Warburton), 2023-2024

Other involvement: USDA-NIFA Research and Extension Experiences for Undergraduates program recruits students from other land-grant institutes to work on plant phenomics topics. 

Indiana (Tuinstra, Yang)

• Phenotyping maize in a Genomes2Fields experiment
• Phenotyping dhurrin-free sorghum in controlled and field environments
• Developing new phenotyping methods for abiotic stress tolerance and plant phenology in sorghum and maize

1. Bai, G., Koehler-Cole, K., Scoby, D., Thapa, V.R., Basche, A., Ge, Y., Enhancing estimation of cover crop biomass using field-based high-throughput phenotyping and machine learning models. Frontiers in Plant Science 14, 1277672. doi: 10.3389/fpls.2023.1277672
2. Cooper J, Du C, Beaver Z, Zheng M, Page R, Wodarek J, Matny O, Szinyei T, Quiñones A, Anderson J, Smith K, Yang C, Steffenson B, Hirsch C. An RGB based deep neural network for high fidelity Fusarium head blight phenotyping in wheat. bioRxiv.https://doi.org/10.1101/2023.09.20.558703
3. Das Choudhury, S., Rosaria Guadagno, C., Bashyam, S., Mazis, A., Ewers, E.B., Samal, A., and Awada, T. (2024). Stress Phenotyping Analysis Leveraging Autofluorescence Image Sequences with Machine Learning. Front. Plant. Sci, V15. doi: 10.3389/fpls.2024.1353110
4. DeLoose M, Cho H, Bouain N, Choi I, Prom-U-T , C, Zaigham S; Luqing Z; Rouached H. 2024. PDR9 Allelic Variation and MYB63 Modulate Nutrient-Dependent Coumarin Homeostasis in Arabidopsis. The Plant Journal. 2024.
5. Grubbs, E.K., Gruss, S.M., Schull, V.Z., Gosney, M.J., Mickelbart, M.V., Brouder, S., Gitau, M.W., Bermel, P., Tuinstra,R., Agrawal, R., 2024. Optimized agrivoltaic tracking for nearly-full commodity crop and energy production. Renewable and Sustainable Energy Reviews, 191, p.114018. https://doi.org/10.1016/j.rser.2023.114018
6. Gruss, S.M., Johnson, K.D., Radcliffe, J.S., Lemenager, R.P. and Tuinstra, M.R., Preference of dhurrin‐free sorghum by ewes. Crop, Forage & Turfgrass Management, 10(1), p.e20259. https://doi.org/10.1002/cft2.20259
7. Joseph K.T., K. Muvva, H. Mwunguzi, A. Haake, C. Liew, A. Balabantaray, S. Behera, A. Kalra, K. K. Vattiam Srikanth, S. Pitla and D. Choudhury, CottonHusker: Deep Learning Enabled Cotton Picking Robot for Smart Agriculture, International Conference on Systems and Technology for Smart Agriculture (ICSTA), Kolkata, India, December 2023.
8. Kick DR, Wallace JG, Schnable JC, Kolkman JM, Alaca B, Beissinger TM, Edwards J, Ertl D, Flint-Garcia S, Gage JL, Hirsch CN, Knoll JE, de Leon N, Lima DC, Moreta DE, Singh MP, Thompson AM, Weldekidan T, Washburn JD. Yield prediction through integration of genetic, environment, and management data through deep learning, G3 Genes|Genomes|Genetics, Volume 13, Issue 4, April 2023, jkad006, https://doi.org/10.1093/g3journal/jkad006
9. Kaiser, E., Von Gillhaussen, P., Clarke, J., and Schurr, U. (2024) IPPS 2022 - Plant Phenotyping for a Sustainable Future. Frontiers in Plant Sciences - Plant Breeding, 15. https://doi.org/10.3389/fpls.2024.1383766
10. Krafft, D., C. G. Scarboro, W. Hsieh, C. Doherty, P. Balint-Kurti, and Kudenov, "Mitigating Illumination-, Leaf-, and View-Angle Dependencies in Hyperspectral Imaging Using Polarimetry," Plant Phenomics **0**, (2024).
11. Lopez-Cruz, M., Aguate, F.M., Washburn, J.D., de Leon N, Kaeppler SM, Lima DC, Tan R, Thompson AM, De La Bretonne LW, de los Campos G. Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America. Nat Commun 14, 6904 (2023). https://doi.org/10.1038/s41467-023-42687-4
12. Nguyen, H.M., S. Gyurek, R. Mierop, K. V. Pecota, K. LaGamba, M. Boyette, G. C. Yencho, C. M. Williams, and W. Kudenov, "Deployment and Analysis of Instance Segmentation Algorithm for In-field Grade Estimation of Sweetpotatoes," (2023).
13. Pan, Y., Sun, J., Yu, H., Bai, G., Ge, Y., Luck, J., and Awada, T. (2024). Transforming Agriculture with Intelligent Data Management and Insights," 2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy, 2023, pp. 3489-3498, doi: 10.1109/BigData59044.2023.10386589.
14. Quiñones C, Adviento-Borbe MA, Larazo W, Shea Harris R, Mendez K, Cunningham SS, Campbell ZC, Medina-Jimenez K, Hein NT, Wagner D, Ottis B, Walia H, Lorence A. Field-based infrastructure and cyber–physical system for the study of high night air temperature stress in irrigated rice. 2023, The Plant Phenome Journal
15. Quiñones R., F. Munoz-Arriola, D. Choudhury, A. Samal, OSC-CO2: Coattention and Cosegmentation Framework for Plant State Change with Multiple Features, Frontiers in Plant Science, 14: doi: 10.3389/fpls.2023.1211409, October 2023.
16. Raman, M.G., Marzougui, A., Teh, S.L., York, Z.B., Evans, K.M., and Sankaran, S. Rapid assessment of architectural traits in pear rootstock breeding program. Remote Sensing, 15(6), 1483; https://doi.org/10.3390/rs15061483.
17. Sahay S, Grzybowski M, Schnable JC, Glowacka K (2023) Genetic control of photoprotection and photosystem II operating efficiency in plants. New Phytologist doi: 10.1111/nph.18980
18. Sangjan, W., McGee, R.J., and Sankaran, S. Evaluation of forage quality in a pea breeding program using a hyperspectral sensing system. Computer and Electronics in Agriculture, 212, 108052. https://doi.org/10.1016/j.compag.2023.108052.
19. Schrickx, H.M., S. Gyurek, C. Moore, E. Hernández-Pagán, C. J. Doherty, W. Kudenov, and B. T. O’Connor, "Flexible Self-Powered Organic Photodetector with High Detectivity for Continuous On-Plant Sensing," Advanced Optical Materials **n/a**, (2024).
20. Srivastava, S., Kumar, N., Malakar, A. Choudhury, S.D. A Machine Learning-Based Probabilistic Approach for Irrigation Scheduling. Water Resour Manage (2024). https://doi.org/10.1007/s11269-024-03746-7
21. Staswick P, Singh J, Shi Y, Zhang C, Petersen C, Walia H. Growth and Transcriptional Responses to the Tertiary Amine BMVE in Wheat and Rice. 2023, Frontiers of Plant Sciences
22. Tuggle CK, Clarke JL, Murdoch BM, Lyons E, Scott NM, Benes B, Campbell JD, Chung H, Daigle CL, Choudhury SD, Dekkers JCM, Dórea JRR, Ertl DS, Feldman M, Fragomeni BO, Fulton JE, Guadagno CR, Hagen DE, Hess AS, Kramer LM, Lawrence-Dill CJ, Lipka AE, Lübberstedt T, McCarthy FM, McKay SD, Murray SC, Riggs PK, Rowan TN, Sheehan MJ, Steibel JP, Thompson AM, Thornton KJ, Van Tassell CP, Schnable PS. Current challenges and future of agricultural genomes to phenomes in the USA. Genome Biol 25, 8 (2024). https://doi.org/10.1186/s13059-023-03155-w
23. Ying S, Webster B, Gomez-Cano L, Shivaiah KK, Wang Q, Newton L, Grotewold E, Thompson A, Lundquist PK. 2023. Multiscale physiological responses to nitrogen supplementation of maize hybrids, Plant Physiology, kiad583, https://doi.org/10.1093/plphys/kiad583
24. Zhao, B., Stephenson, B.M., Awada, T., Volesky, J., Wardlow, B., Zhou, Y., and Shi, Y. (2024). 15-Yr Biomass Production in Semiarid Nebraska Sandhills Grasslands: Part 1—Plant Functional Group Analysis. Rangeland Ecology & Management, 93:49-61. https://doi.org/10.1016/j.rama.2023.12.001