NC1212: Exploring the Plant Phenome in Controlled and Field Environments

(Multistate Research Project)

Status: Active

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SAES-422 Reports

Annual/Termination Reports:

[08/16/2023] [03/20/2024]

Date of Annual Report: 08/16/2023

Report Information

Annual Meeting Dates: 08/16/2023 - 08/16/2023
Period the Report Covers: 10/01/2021 - 08/16/2023

Participants

NC-1212 representatives: Mitch Tuinstra, Purdue University, Meeting chair; Carolyn Lawrence-Dill, Iowa State University (ISU), Administrative Advisor; Yufeng Ge, University of Nebraska Lincoln (UNL); Tala Awada, UNL; Jennifer Clarke, UNL; Sindhuja Sankaran, Washington State University; Addie Thompson, Michigan State University

Other participants: Sungchan Oh, Purdue University; Nipuna Chamara, UNL; Junxiao Zhang, UNL; Siviswanabh Wiswanadh, CIMMYT/Purdue University; Ravi Mural, UNL; Nikee Shrestha, UNL; Chidanand Ullagaddi, UNL; Michael Tross, UNL; Harkamal Walia, UNL; Seth Murray, TAMU; Souparno Ghosh, UNL

Brief Summary of Minutes

Meeting Minutes (Summary):

Welcome and Introduction:
- Mitch Tuinstra welcomed attendees and expressed gratitude to UNL for hosting the first official in-person meeting.
- Carolyn Lawrence-Dill introduced herself to the committee.
- Yufeng Ge introduced the agenda and goals for the meeting.

Presentations by Member Institutions:
- Sindhuja Sankaran provided an overview of plant phenotyping infrastructure at Washington State University, emphasizing drone-based imaging.
- Addie Thompson introduced research experts and activities at Michigan State University.
- Sungchan Oh discussed Purdue Plant Sciences' engagement in uploading G2F data to CyVerse.
- Ravi Mural provided an update on phenotyping-related research activities at James Schnable's lab at University of Nebraska.
- Jennifer Clarke outlined her lab's research focus on X-ray CT data processing and root segmentation algorithms at University of Nebraska.
- Yufeng Ge presented his lab's efforts in investigating the diurnal pattern of measured NDVI in soybean at University of Nebraska.

Administrative Discussions:
- Jennifer Clarke updated the committee on the 2024 International Plant Phenotyping Network conference and proposed UNL and NC State as potential hosts.
- Addie Thompson suggested having a satellite NC-1212 meeting at the next NAPPN annual conference, which was approved by all members.
- Yufeng Ge led discussions on the rotation of committee chairmanship, with Yufeng Ge as the next committee chair.
- Yang Yang nominated Addie Thompson as incoming vice chair, who would then become the next chair in 2024-2025, which was approved by all members.
- Addie Thompson nominated Singhuja Sankaran as the secretary, which was approved by all members.
- Carolyn Lawrence-Dill indicated that Patrick Schnable has left the committee, and she is looking for a replacement representative from Iowa State.

Collaboration Opportunities:
- Mitch Tuinstra highlighted the core goals of the NC-1212 committee and discussed collaboration opportunities for image acquisition, processing pipeline, and trait extraction/modeling.
- Active discussion and brainstorming identified multiple potential future projects of interest to the group, including the potential to leverage ongoing projects such as Genomes to Fields.

Committee Updates and Discussions:
- Carolyn Lawrence-Dill mentioned the need for relevant publications, grant submissions, and meeting agendas in the committee report.
- Tala Awada emphasized the inclusion of collaboration efforts/activities in future reports.

Action Items:

Submit feedback on the International Plant Phenotyping Network conference location.

Coordinate the satellite NC-1212 meeting at the next NAPPN annual conference.

Prepare committee report including relevant publications, grant submissions, and meeting agendas (Purdue to lead submission, due Oct. 1).

Continue discussion of potential collaborations, including possible equipment grant.

Next Meeting: The next NC-1212 meeting will be held alongside the NAPPN annual conference, Feb. 12, 2024.

Photo: See attachment.

Accomplishments

First in-person meeting. Coordination and creation of shared research for this newly formed committee has commenced. Outcomes of the group so far have included: <ol> <li>Identifying and sharing ongoing work and interests at each state institution</li> <li>Outlining specific shared research ideas of broad interest to the group to enable coordinated projects across participants' institutions</li> </ol> Initial state reports included traditional accomplishments when relevant, but otherwise participants were encouraged to describe current projects and efforts in plant phenomics, as the group was getting to know each other’s interests and skills.   Accomplishments and Impacts - State Reports: Nebraska (Ge, Awada, Choudhury, Clarke, Ghosh, Schnable, Walia) Ge: Successfully conducted four experiments at UNL's Spidercam field phenotyping facility in 2021 and 2022 growing seasons: <ol> <li>phenotyping of HIPS corn hybrids performance,</li> <li>a cover crop biomass estimation experiment,</li> <li>an experiment to phenotyping corn water use and nitrogen use efficiency using a commercial variety, and</li> <li>an experiment to phenotyping soybean water use efficiency using two commercial varieties. </li> </ol> Clarke: <ul> <li>Image analysis and segmentation methods for in situ 3D X-ray plant imaging. Development of ground truth and digital twin data.</li> <li>AI-based approaches to image segmentation and trait extraction; new metrics for evaluation of segmentation methods.</li> </ul> Awada: <ul> <li>Leading and participating in large data pipeline development for multi-source images, continuous, and points plant phenomics data. </li> <li>AI and machine learning tools development for image analysis that are accessible and inclusive.</li> <li>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.</li> <li>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 derived from images at multiple scales) in managed and natural systems.</li> </ul> Walia: <ul> <li>“Comparative genomics and phenomics approach to discover genes underlying heat stress resilience in cereals” - NSF, FEC-Track 2, (2017-2023), Walia PI</li> <li>“Bringing Nutrition Back into Rice Yield Gain” - FFAR (2023-2026), Walia CoPI</li> <li>“Genetic and Physiological Novelties for Salinity Tolerance in Rice Created by Transgressive Segregation” - NIFA (2023-2026), Walia CoPI</li> <li>"High-Definition Spatiotemporal Phenomics and Transcriptomics of Developing Maize Kernel Under Heat Stress" - NAES, Walia PI</li> </ul> Ghosh: <ul> <li>Automatic Tassel detection and prediction from UAV images with associated uncertainty quantification</li> <li>Global shrinkage and local selection of genomic features from functional phenotypic data</li> <li>High-Definition Spatiotemporal Phenomics and Transcriptomics of Developing Maize Kernel under Heat stress </li> </ul> Delaware (Sparks, Bao) Ongoing funded projects include: <ul> <li>“Field Robotic Systems to Support BlueTech Platforms for Sustainable Ocean-Based Activities” - UNIDEL Foundation</li> <li>“Quantifying Crop Biomechanics Across Plant Lifespans” - NSF</li> <li>“Understanding Supply and Support Trade-offs in Maize Aerial Roots” - Royal Society</li> <li>“Collaborative Research: Linking brace root development and function in maize” - NSF</li> <li>“Linking Pythium soil abundance to disease development in Mid-Atlantic maize” - Delaware Biosciences Association</li> <li>“Effect of rye shading on corn roots and shoots in traditional and short stature corn” - USDA</li> <li>“Systems-level characterization of Pythium pathogenesis, an emerging threat to maize production” - USDA</li> </ul> Minnesota (Hirsch, Pardey, Runck) An integrated 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. North Carolina (Kudenov)  <ul> <li>Bidirectional reflectance correction using polarized light. Implemented field trials in 2021, 2022 and 2023 to collect diurnal data and created a glare correction algorithm for multispectral (and soon hyperspectral) imagery.</li> <li>Developed and deployed real-time sweetpotato phenotyping algorithms in two sweetpotato stakeholder production facilities. These are linked to real-time dashboards that track historical information and let stakeholders view product grades in real time.</li> <li>Developed a leaf wetness sensor that serves as an alternative to conventional capacitive wetness sensors (artificial leaves), thermal IR, and RGB-based imaging for strawberry.</li> <li>Developed leaf spot scoring algorithm for peanut breeding program that uses polarized light and compared the sensor to a RGB pipeline, demonstrating that use of an RGB+P (polarized RGB) camera may offer a simple way to leverage these data. </li> </ul> South Dakota (Mural, Yadav) Newly established lab / no activity reported Michigan (Thompson, Rouched) Thompson: Ongoing funded projects include: <ul> <li>Drone-based imaging of diverse sweet corn varieties</li> <li>Multi-modal hyperspectral imaging of phenolic compounds and their relationship to tar spot disease in maize</li> <li>High-throughput modeling and prediction of plant growth in maize and sorghum</li> <li>Prediction of nutritional properties of teff from aerial imagery</li> <li>Modeling nitrogen accumulation, remobilization, and plant senescence in maize hybrids and inbreds </li> </ul> Rouached: Efficiently regulating growth to adapt to varying resource availability is crucial for organisms, including plants. In particular, the acquisition of essential nutrients is vital for plant development, as a shortage of just one nutrient can significantly decrease crop yield. However, plants constantly experience fluctuations in the presence of multiple essential mineral nutrients, leading to combined nutrient stress conditions. Unfortunately, our understanding of how plants perceive and respond to these multiple stresses remains limited. Unlocking this mystery could provide valuable insights and help enhance plant nutrition strategies. The Rouached lab is interested in applying plant phenotyping and system genetics to decipher how plants make sense of multiple environmental cues. Washington (Sankaran) Ongoing funded projects include: <ul> <li>Acquisition of a versatile plant phenotyping platform (PhenoPlant) to advance food and agricultural research (USDA-NIFA EGP, PI: Helmut Kirchoff), 2023-2026</li> <li>Smart monitoring and acoustic spray system for precision crop stress management in controlled environment (BARD), 2023-2026</li> <li>High throughput phenotyping of 700 pea accessions for yield components (PI: Marilyn Warburton), 2023-2024</li> <li>Development of phenomics tools for pulse breeding programs, 2022-2024</li> <li>Phenomics and modeling enabled decision support for climate-adapted wheat germplasm development, 2022-2025</li> <li>FACT: Research experience for undergraduates on phenomics big data management, 2020-2025</li> <li>High-throughput phenotyping techniques to advance variety selection in grain legume, 2016-2023</li> </ul> Indiana (Tuinstra, Yang) Dr. Tuinstra and his collaborators are responding to increasing challenges of droughts and heat waves through efforts to develop "climate resilient" cultivars of maize and sorghum that contribute to the adaptation of agriculture to warmer and drier environments. His research focuses on identifying genes and genetic resources that contribute to improved crop performance in stressful environments. This work is done in collaboration with scientists and breeders in the Americas, Africa and Asia. <ul> <li>Phenotyping maize in a Genomes2Fields experiment</li> <li>Phenotyping dhurrin-free sorghum in controlled and field environments</li> <li>Developing new phenotyping methods for abiotic stress tolerance and plant phenology in sorghum and maize</li> </ul>

Publications

<ol> <li>Ahmed Z, Khalid M, Ghafoor A, Shah MKN, Raja GK, Rana RM, Mahmood T, Thompson AM. SNP-Based Genome-Wide Association Mapping of Pollen Viability Under Heat Stress in Tropical Zea mays L. Inbred Lines. Front Genet, 15(13).</li> <li>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.</li> <li>Das A, Choudhury SD, Das AK, Samal A, Awada T, EmergeNet: A Novel Deep-Learning based Ensemble Segmentation Model for Emergence Timing Detection of Coleoptile, Frontiers in Plant Science, 14(2023), February 2023.</li> <li>Allen, R. Mazis, A., Wardlow, B., Cherubini, P., Hiller, J., Wedin, D., and Awada, T. (2023). Coupling Dendroecological and Remote Sensing Techniques to Assess the Biophysical Traits of Juniperus Virginiana and Pinus Ponderosa within the Semi-Arid Grasslands of the Nebraska Sandhills. Forest Ecology and Management. 544, 121184, https://doi.org/10.1016/j.foreco.2023.121184</li> <li>Alzadjali A, Veeranampalayam-Sivakumar A, Alali MH, Deogun JS, Scott S, Schnable JC, Shi Y (2021) “Maize tassel detection from UAV imagery using deep learning.” Frontiers in Robotics and AI doi: 10.3389/frobt.2021.600410</li> <li>Atefi A, Ge Y, Pitla S, Schnable JC (2021) “Robotic technologies for high-throughput plant phenotyping: reviews and perspectives.” Frontiers in Plant Science doi: 10.3389/fpls.2021.611940</li> <li>Bacher H, Zhu F, Gao T, Liu K, Dhatt BK, Awada T, Zhang C, Distelfeld A, Yu H, Peleg Z, Walia H*. Wild emmer introgression alters root-to-shoot growth dynamics in durum wheat in response to water stress. 2021, Plant Physiology</li> <li>Bashyam, S., Das Choudhury, S., Samal, A., and Awada, T. (2021). Visual growth tracking for automated leaf stage monitoring based on image sequence analysis. Remote Sensing, 13(5): 961. https://doi.org/10.3390/rs13050961</li> <li>Bouain N, Cho H, Sandhu J, Tuiwong P, Prom-u-thai C, Zheng L, Shahzad Z, Rouached H*. Plant growth stimulation by high CO2 depends on phosphorus homeostasis in chloroplasts. Current Biology. 2022.</li> <li>G. Scarboro, C. J. Doherty, P. J. Balint-Kurti, and M. W. Kudenov, "Multistatic fiber-based system for measuring the Mueller matrix bidirectional reflectance distribution function," Appl. Opt., AO **61**, 9832–9842 (2022).</li> <li>Chai, Y.N., Ge, Y., Stoerger, V., Schachtman, D.P., 2021. High-resolution phenotyping of sorghum genotypic and phenotypic response to low nitrogen and synthetic microbial communities. Plant, Cell & Environment 44(5), 1611-1626. https://doi.org/10.1111/pce.14004</li> <li>Chandran AKN, Sandhu J, Irvin L, Paul P, Dhatt BK, Hussain W, Gao T, Staswick P, Yu H, Morota G, Walia H*. Rice Chalky Grain 5 regulates natural variation for grain quality under heat stress. 2022, Frontiers of Plant Sciences</li> <li>Choudhury, S.D., Saha, S., Samal, A., Mazis, A., and Awada, T. (2023). Drought stress prediction and propagation using time series modeling on multimodal plant image sequences, Frontiers in Plant Science, 14:1003150. https://doi.org/10.3389/fpls.2023.1003150</li> <li>Clarke, J., Qiu, Y., and Schnable, J. Experimental Design for Controlled Environment High Throughput Plant Phenotyping. In: High Throughput Plant Phenotyping: Methods and Protocols, In: Lorence, A., Medina Jimenez, K. (eds) High-Throughput Plant Phenotyping. Methods in Molecular Biology, vol 2539. July 2022. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2537-8_7</li> <li>Krafft, G. Scarboro, P. Balint-Kurti, C. Doherty, and M. Kudenov, "Mitigating illumination-, leaf-, and view-angle dependencies in hyperspectral imaging using polarimetry (Conference Presentation)," in Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII (SPIE, 2023), Vol. PC12539, p. PC1253908.</li> <li>Das Choudhury, S., Guha, S., Das, A., Kumar Das, A., Samal, A., Awada, T. (2022). Flowernetpheno: automated flower detection from multi-view image sequences using deep neural networks for temporal plant phenotyping analysis, Remote Sensing, 14(24), 6252. https://doi.org/10.3390/rs14246252.</li> <li>Dharni, J.S., Dhatt, B.K., Paul, P., Gao, T., Awada, T., Staswick, P., Hupp, J., Yu, H., and H., (2022). A non-destructive approach for measuring rice panicle-level photosynthetic responses using 3D-image reconstruction. Plant Methods, 18, 126. https://doi.org/10.1186/s13007-022-00959-yA.</li> <li>Díaz-Martínez V, Orozco-Sandoval J, Manian V, Dhatt BK, Walia H. A deep learning framework for processing and classification of hyperspectral rice seed images grown under high day and night temperatures. 2023, Sensors</li> <li>Divyanth, L.G., Marzougui, A., Gonzalez-Bernal, M.J., McGee, R.J., Rubiales, D., and Sankaran, S. Evaluation of effective class-balancing techniques for CNN-based assessment of Aphanomyces root rot resistance in pea (Pisum sativum L.). Sensors, 22(19), 7237; https://doi.org/10.3390/s22197237.</li> <li>Martinez, M. Kudenov, H. Nguyen, R. Mierop, K. Pecota, C. Yencho, and C. Williams, "‪Statistical Phenotyping of Sweetpotatoes by Imaging Bins: Preliminary Results from a High-throughput Truck Scanner," (2022).</li> <li>Gaillard M, Benes B, Tross MC, Schnable JC (2023) Multi-view triangulation without correspondences. Computers and Electronics in Agriculture doi: 10.1016/j.compag.2023.107688</li> <li>Gao T, Chandran AKN, Paul P, Walia H, Yu H. HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds. 2021, Sensors</li> <li>Gao T, Zhu F, Paul P, Sandhu J, Doku HA, Sun J, Pan Y, Staswick P, Walia H, Yu H. Novel 3D imaging systems for high-throughput phenotyping of plants. 2021, Remote Sensing</li> <li>Gruss, S.M., Ghaste, M., Widhalm, J.R., Tuinstra, M.R., Seedling growth and fall armyworm feeding preference influenced by dhurrin production in sorghum. Theoretical and Applied Genetics. https://doi.org/10.1007/s00122-021-04017-4</li> <li>Gruss, S.M., Souza, A., Yang, Y., Dahlberg, J. and Tuinstra, M.R., 2023. Expression of stay‐green drought tolerance in dhurrin‐free sorghum. Crop Science, 2023, 1–14. https://doi.org/10.1002/csc2.20947</li> <li>Grzybowski M, Wijewardane NK, Atefi A, Ge Y, Schnable JC (2021) “Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: progress and challenges.” Plant Communications doi: 10.1016/j.xplc.2021.100209</li> <li>Grzybowski M, Zweiner M, Jin H, Wijewardane NK, Atefi A, Naldrett MJ, Alvarez S, Ge Y, Schnable JC (2022) Variation in morpho-physiological and metabolic responses to low nitrogen stress across the sorghum association panel. BMC Plant Biology 10.1186/s12870-022-03823-2 bioRxiv doi: 10.1101/2022.06.08.495271</li> <li>Herr, A. W., Adak, A., Carroll, M. E., Elango, D., Kar, S., Li, C., Jones, S.E., Carter, A.H., Murray, S.C., Paterson, A., Sankaran, S., Singh, A., and Singh, A. Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding. Crop Science, 63 (4), 1722-1749.</li> <li>Herrero, M., Meline, V., Iyer-Pascuzzi, A.S., Souza, A.M., Tuinstra, M.R. and Yang, Y., 4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography. Plant Methods 17, 123. https://doi.org/10.1186/s13007-021-00819-1</li> <li>Herrero-Huerta, M., Meline, V., Iyer-Pascuzzi, A.S., Souza, A.M., Tuinstra, M.R. and Yang, Y., Root Phenotyping from X-Ray Computed Tomography: Skeleton Extraction. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, pp.417-422. https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-417-2021</li> <li>Herrero-Huerta, M., Tolley, S., Tuinstra, M.R. and Yang, Y., 2021, April. Individual maize extraction from UAS imagery-based point clouds by 3D deep learning. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI (Vol. 11747, p. 1174704). International Society for Optics and Photonics. https://doi.org/10.1117/12.2587100</li> <li>Hostetler AH*, Erndwein L*, Ganji E, Reneau JW, Killian ME and Sparks EE. “Maize brace root mechanics vary by whorl, genotype, and reproductive stage” Annals of Botany, 2022 Mar.</li> <li>Hostetler AN, Erndwein L, Reneau JW, Stager A, Tanner HG, Cook DD and Sparks EE. “Brace root phenotypes predict root lodging susceptibility and the contribution to anchorage in maize” Plant, Cell & Environment, 2022 May 45(5): 1573-1583.</li> <li>Interdisciplinary Plant Science Consortium (Baxter, I). 2023. Inclusive collaboration across plant physiology and genomics: Now is the Time! Plant Direct, 7 (5), e493. https://doi.org/10.1002/pld3.493.</li> <li>Khan SH, Karkhanis M, Hatasaka B, Tope S, Noh S, Dalapati R, Bulbul A, Mural RV, Banerjee A, Kim KH, Schnable JC, Ji M, Mastrangelo CH, Zang L, Kim H (2022) “Field deployment of a nanogap gas sensor for crop damage detection.” MEMS 2022 doi: 10.1109/MEMS51670.2022.9699614</li> <li>Khan SH, Tope S, Dalpati R, Kim KH, Noh S, Bulbul A, Mural RV, Banerjee A, Schnable JC, Ji M, Mastrangelo C, Zang L, Kim H (2021) “Development of a gas sensor for green leaf volatile detection.” Transducers 2021 doi: 10.1109/Transducers50396.2021.9495597</li> <li>Zhou, X. Fan, T. Tjahjadi, S. D. Choudhury, Discriminative Attention-augmented Feature Learning for Facial Expression Recognition in the Wild, Neural Computing and Applications, 34, 2022, 925-936.</li> <li>LeBauer, D., Bucksch, A., Clarke, J., Potts, J., and Roy, S. Providing conference participation support to increase racial diversity in the North American Plant Phenotyping Network. Special Section: North American Plant Phenotyping Network (NAPPN) Proc. 2022. The Plant Phenotyping Journal 2023, 6:1 e20075 https://doi.org/10.1002/ppj2.20075</li> <li>Li D, Bai D, Tian Y, Li Y, Zhao C, Wang Q, Gou S, Gu Y, Luan X, Wang R, Yang J, Hawkesford MJ, Schnable JC, Jin X, Qiu L (2022) “Time series canopy phenotyping enables the identification of genetic variants controlling dynamic phenotypes in soybean.” Journal of Integrative Plant Biology doi: 10.1111/jipb.13380</li> <li>Li, J., Schachtman, D.P., Creech, C.F., Wang, L., Ge, Y., Shi, Y., 2022. Evaluation of UAV-derived multimodal remote sensing data for biomass prediction and drought tolerance assessment in bioenergy sorghum. The Crop Journal 10(5), 1363-1375. https://doi.org/10.1016/j.cj.2022.04.005</li> <li>Lima, D.C., Aviles, A.C., Alpers, R.T., McFarland, B.A., Kaeppler, S., Ertl, D., Romay, M.C., Gage, J.L., Holland, J., Beissinger, T. Bohn, M., Buckler, E., Edwards, J., Flint-Garcia, S., Hirsch, C.N., Hood, E., Hooker, D.C., Knoll, J.F., Kolkman, J.M., Liu, S., McKay, M., Minyo, R., Moreta, D.E., Murray, S.C., Nelson, R., Schnable, J.C., Sekhon, R.S., Singh, M.P., Thomison, P., Thompson, A., Tuinstra, M.R., Wallace, J., Washburn, J.D., Weldekidan, T., Wisser, R.J., Xu, W., de Leon., N. 2023. 2018–2019 field seasons of the Maize Genomes to Fields (G2F) G x E project. BMC Genomic Data, 24(1), pp.1-4. https://doi.org/10.1186/s12863-023-01129-2</li> <li>Lima, D.C., Aviles, A.C., Alpers, R.T., Perkins, A., Schoemaker, D.L., Costa, M., Kaeppler, S., Ertl, D., Romay, M.C., Gage, J.L., Holland, J., Beissinger, T. Bohn, M., Buckler, E., Edwards, J., Flint-Garcia, S., Gore, M.A., Hirsch, C.N., Knoll, J.F., McKay, M., Minyo, R., Murray, S.C., Schnable, J.C., Sekhon, R.S., Singh, M.P., Sparks, E.E., Thomison, P., Thompson, A., Tuinstra, M.R., Wallace, J., Washburn, J.D., Weldekidan, T., Xu, W., de Leon., N. 2023. 2020-2021 Field Seasons of Maize G x E Project within Maize Genomes to Fields Initiative. https://doi.org/10.21203/rs.3.rs-2908766/v1</li> <li>Lima, D.C., Washburn, J.D., Varela, J.I., Chen, Q., Gage, J.L., Romay, M.C., Holland, J., Ertl, D., Lopez-Cruz, M., Aguate, F.M. and de los Campos, G., Kaeppler, S., Beissinger, T., Bohn, M., Buckler, E., Edwards, J., Flint-Garcia, S., Gore, M.A., Hirsch, C.N., Knoll, J.E., McKay, J., Minyo, R., Murray, S.C., Ortez, O.A., Schnable, J.C., Sekhon, R.S., Singh, M.P., Sparks, E.E., Thompson, A., Tuinstra, M.R., Wallace, J., Weldekidan, T., Xu, W., de Leon, N., 2023. Genomes to Fields 2022 Maize genotype by Environment Prediction Competition. BMC Res Notes 16, 148. https://doi.org/10.1186/s13104-023-06421-z</li> <li>Lin, M., Lynch, V., Ma, D., Maki, H., Jin, J., Tuinstra, M.R., Multi-species prediction of physiological traits with hyperspectral modeling. Plants, 11, 676. https://doi.org/10.3390/plants11050676.</li> <li> W. Kudenov, A. Altaqui, and C. Williams, "Practical spectral photography II: snapshot spectral imaging using linear retarders and microgrid polarization cameras," Opt. Express, OE **30**, 12337–12352 (2022).</li> <li> W. Kudenov, C. G. Scarboro, A. Altaqui, M. Boyette, G. C. Yencho, and C. M. Williams, "Internal defect scanning of sweetpotatoes using interactance spectroscopy," PLOS ONE **16**, e0246872 (2021).</li> <li> W. Kudenov, D. Krafft, C. G. Scarboro, C. J. Doherty, and P. Balint-Kurti, "Hybrid spatial–temporal Mueller matrix imaging spectropolarimeter for high throughput plant phenotyping," Appl. Opt., AO **62**, 2078–2091 (2023).</li> <li>Ma, D., Rehman, T.U., Zhang, L., Maki, H., Tuinstra, M.R. and Jin, J., 2021. Modeling of Environmental Impacts on Aerial Hyperspectral Images for Corn Plant Phenotyping. Remote Sensing, 13, p.2520. https://doi.org/10.3390/rs13132520</li> <li>Ma, D., Rehman, T.U., Zhang, L., Maki, H., Tuinstra, M.R. and Jin, J., 2021. Modeling of diurnal changing patterns in airborne crop remote sensing images. Remote Sensing, 13(9), p.1719. https://doi.org/10.3390/rs13091719</li> <li>Maki, H., Lynch, V., Ma, D., Tuinstra, M.R., Yamasaki, M., and Jin, J., 2023. Comparison of Various Nitrogen and Water Dual Stress Effects for Predicting Relative Water Content and Nitrogen Content in Maize Plants through Hyperspectral Imaging. AI, 4(3), pp.692-705. https://doi.org/10.3390/ai4030036</li> <li>Marzougui, A., McGee R.J., Van Vleet, S., and Sankaran, S. Remote sensing for field pea yield estimation: A study of multi-scale data fusion approaches in phenomics. Frontiers in Plant Science, 14:1111575. 10.3389/fpls.2023.1111575.</li> <li>Mazis, A., Awada, T., Erickson, G.E., Wardlow, B., Wienhold, B.J., Jin, V., Schmer, M., Suyker, A., Zhou, Y., Hiller, J. (2023). 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Optimization of UAV-based imaging and image processing orthomosaic and point cloud approaches for estimating biomass in a forage crop. Remote Sensing, 14(10), 2396; https://doi.org/10.3390/rs14102396.</li> <li>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.</li> <li>Sankaran S, Marzougui A, Hurst JP, Zhang C, Schnable JC, Shi Y (2021) “Can high resolution satellite imagery be used in high-throughput field phenotyping?” Transactions of the ASABE doi: 10.13031/trans.14197</li> <li>Sankaran, S., Carlos, E. F., and Raman, M. G. 2022. Modeling 3D architecture of adult peach trees (Prunus persica (L.) Batsch) using remote sensing data. Acta Horticulturae, 1360, 307-214.</li> <li>Stager A, Tanner HG and Sparks EE. “Design and Construction of Unmanned Ground Vehicles for Sub-Canopy Plant Phenotyping” Methods in Molecular Biology, 2022 Jul; 2539: 191-211.</li> <li>Su WH, Yang C, Dong Y, Johnson R, Page R, Szinyei T, Hirsch CD, Steffenson B. 2021. Hyperspectral imaging and improved feature variable selection for automated determination of deoxynivalenol in various genetic lines of barley kernels for resistance screening. Food chemistry. 343: 128507. https://doi.org/10.1016/j.foodchem.2020.128507</li> <li>Su WH, Zhang J, Yang C, Page R, Szinyei T, Hirsch CD, Steffenson B. 2021. Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision. 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Preprints-57181, Computers and Electronics in Agriculture, 207, 107709.</li> <li>Tolley, S., Carpenter, N., Crawford, M., Delp, E.J., Habib, A.F. and Tuinstra, M.R., Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modelling. Frontiers in Plant Science, 14, p.1202536. https://doi.org/10.3389/fpls.2023.1202536</li> <li>Tolley, S.A., Brito, L.F., Wang, D.R., Tuinstra, M.R., Genomic Prediction and Association Mapping of Maize Grain Yield in Multi-environment Trials Based on Reaction Norm Models. Frontiers in Genetics, 14, p.1221751. https://doi.org/10.3389/fgene.2023.1221751</li> <li>Tolley, S.A., Singh, A. and Tuinstra, M., Heterotic Patterns of Temperate and Tropical Maize by Ear Photometry. 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Biogenic VOCs emission profiles associated with plant-pest interaction for phenotyping applications. Sensors, 22(13), 4870. https://doi.org/10.3390/s22134870.</li> <li>Wang, T., Crawford, M.M. and Tuinstra, M.R., A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers. Frontiers in Plant Science, 14. https://doi.org/10.3389/fpls.2023.1138479</li> <li>Wijewardane NK, Zhang H, Yang J, Schnable JC, Schachtman DP, Ge Y (2023) A leaf-level spectral library to support high throughput plant phenotyping: Predictive accuracy and model transfer. Journal of Experimental Botany doi: 10.1093/jxb/erad129</li> <li>Fan, R. Zhou, T. Tjahjadi, S. D. Choudhury, Q. Ye, A Segmentation-Guided Deep Learning Framework for Leaf Counting, Frontiers in Plant Science, 13:844522, 2022.</li> <li>Xie X, Ge Y, Walia H, Yang J, Yu H. 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Impact Statements

Enhanced Collaboration Across Institutions
Acceleration of Phenomics Research and Applications

Date of Annual Report: 03/20/2024

Report Information

Annual Meeting Dates: 02/12/2024 - 02/12/2024
Period the Report Covers: 08/17/2023 - 02/12/2024

Participants

Name (* designates representatives) Institution
*Yufeng Ge Chair NC-1212, University of Nebraska Lincoln
Carolyn Lawrence-Dill Academic Project Advisor, Iowa State University
Bisoondat ‘Mac’ Macoon USDA-NIFA
Jessica Shade USDA-NIFA
*Addie Thompson Vice-Chair NC-1212, Michigan State University
*Sindhuja Sankaran Secretary, Washington State University
Zhaocheng Xiang University of Nebraska-Lincoln
*James Schnable University of Nebraska-Lincoln
*Jennifer Clarke University of Nebraska-Lincoln
*Cory Hirsch University of Minnesota
Humberto Colletes Michigan State University
Isabella Sepanak Michigan State University
Bethany Nolta Michigan State University
Syndie Muir Michigan State University
Valerio Hoyos-Villegas McGill University
*Erin Sparks University of Delaware
*Yang Yang Purdue University
Eleanor Carr Michigan State University
Tim Strebe Inari Ag
Sven Nelson Heliponix (ANU)
Shangpeng Sun McGill University
Jiwei Zhou Heliponix (ANU)
Elizabeth Leshuk Heliponix (ANU)
Andres Cortona VBC
Brian Sopcak Valent Biosciences
Sungchan Oh Purdue University
*Mitch Tuinstra Purdue University
*Michael Kudenov NC State University
Kantilata Thapa Agricen Sciences
Junxiao Zhang University of Nebraska-Lincoln
Nipuna Chamara University of Nebraska-Lincoln
Zhaocheng Xiang University of Nebraska-Lincoln
Shree Pariyar SALK
Jonathan Concepcion Michigan State
Ali Taheri Tennessee State University

Brief Summary of Minutes

Meeting Minutes (Summary):

Welcome and Introduction:

Yufeng Ge welcomed the attendees and introduced the leadership team.

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.

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.

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.

IPPN Updates:

Updates on the IPPN and its upcoming events, including the IPPS conference, were provided by Jennifer Clarke.

Discussion on Collaborative Research Opportunities:

Attendees discussed opportunities for collaborative research, including diurnal variation of NDVI and the availability of G2F data.

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.

Accomplishments

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: <ul> <li>Leading and participating in large data pipeline development for multi-source image, continuous, and points plant phenomics data.</li> <li>AI and machine learning tools development for image analysis that are accessible and inclusive.</li> <li>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.</li> <li>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.</li> </ul> Funding: <ul> <li>Data Geospatial Solutions and Training for Agricultural Researchers and Practitioners, ARS/USDA, PI T. Awada, $400,000, 2023-2024.</li> <li>Data Solutions for Climate Smart and Resilient Agriculture. ARS/USDA, Co-PI T. Awada, $630,000 (per year), 2023-2028.</li> <li>Big Phenomics Data Architecture – Agriculture. Nebraska Research Initiative. PI. T. Awada, $700,000, 2020-2025.</li> <li>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.</li> <li>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.</li> </ul>  Choudhury: <ul> <li>Keynote speaker, International Conference on Systems and Technology for Smart Agriculture, Kolkata, India, December 19-20, 2023.</li> <li>Keynote speaker, Usefulness of AI in daily life: a closer insight, Surendranath College, Kolkata, India, December 21, 2023.</li> <li>Speaker, Segmentation Techniques and Challenges in Plant Phenotyping: Introducing the iPlantSeg+ Tool workshop, Plant Biology, August 2023.</li> </ul> Clarke: <ul> <li>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.</li> <li>Other involvement: Member of AG2PI executive team with associated publications and presentations; lead of AG2PI seed grant mechanism.</li> </ul> Walia: Ongoing work: <ul> <li>“Comparative genomics and phenomics approach to discover genes underlying heat stress resilience in cereals” - NSF, FEC-Track 2, (2017-2023), Walia PI</li> <li>“Bringing Nutrition Back into Rice Yield Gain” - FFAR (2023-2026), Walia CoPI</li> <li>“Genetic and Physiological Novelties for Salinity Tolerance in Rice Created by Transgressive Segregation” - NIFA (2023-2026), Walia CoPI</li> <li>"High-Definition Spatiotemporal Phenomics and Trascriptomics of Developing Maize Kernel Under Heat Stress" - NAES, Walia PI </li> </ul> Delaware (Sparks, Bao) Sparks: New: “Bendy Box: A new tool to study plant biomechanics in space” NASA EPSCoR Continuing from previous period: <ul> <li>“Quantifying Crop Biomechanics Across Plant Lifespans” - NSF</li> <li>“Understanding Supply and Support Trade-offs in Maize Aerial Roots” - Royal Society</li> <li>“Collaborative Research: Linking brace root development and function in maize” - NSF</li> <li>“Linking Pythium soil abundance to disease development in Mid-Atlantic maize” - Delaware Biosciences Association</li> <li>“Effect of rye shading on corn roots and shoots in traditional and short stature corn” - USDA</li> <li>“Systems-level characterization of Pythium pathogenesis, an emerging threat to maize production” – USDA</li> </ul> Other involvement: Genomes to Fields site                   Minnesota (Hirsch, Pardey, Runck) <ul> <li>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. </li> <li>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. </li> <li>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.</li> </ul> Other involvement: Genomes to Fields; Kernza CAP North Carolina (Kudenov) <ul> <li>Implementing on-line grading sensors for the peanut breeding program (pod width, length and kernel width, length, weight).</li> <li>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.</li> </ul> Other involvement: <ul> <li>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.</li> <li>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.</li> </ul>   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: <ul> <li>Drone-based imaging of diverse sweet corn varieties</li> <li>Multi-modal hyperspectral imaging of phenolic compounds and their relationship to tar spot disease in maize</li> <li>High-throughput modeling and prediction of plant growth in maize and sorghum</li> <li>Prediction of nutritional properties of teff from aerial imagery</li> <li>Modeling nitrogen accumulation, remobilization, and plant senescence in maize hybrids and inbreds</li> </ul> Accomplishments during reporting period: <ul> <li>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)</li> <li>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</li> <li>Successful modeling of maize kernel phenolic compounds (from LCMS) using spectral data (from FT-MIR)</li> <li>Other involvement: Genomes to Fields; Sweet corn CAP; NC7; NAPPN</li> </ul> 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) <ul> <li>Acquisition of a versatile plant phenotyping platform (PhenoPlant) to advance food and agricultural research (USDA-NIFA EGP, PI: Helmut Kirchoff), 2023-2026</li> <li>Smart monitoring and acoustic spray system for precision crop stress management in controlled environment (BARD), 2023-2026</li> <li>High throughput phenotyping of 700 pea accessions for yield components (PI: Marilyn Warburton), 2023-2024</li> </ul> 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) <ul> <li>Phenotyping maize in a Genomes2Fields experiment</li> <li>Phenotyping dhurrin-free sorghum in controlled and field environments</li> <li>Developing new phenotyping methods for abiotic stress tolerance and plant phenology in sorghum and maize</li> </ul>

Publications

<ol> <li>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</li> <li>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</li> <li>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</li> <li>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.</li> <li>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</li> <li>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</li> <li>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.</li> <li>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, <a href="https://doi.org/10.1093/g3journal/jkad006">https://doi.org/10.1093/g3journal/jkad006</a></li> <li>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</li> <li>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).</li> <li>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</li> <li>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).</li> <li>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.</li> <li>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</li> <li>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.</li> <li>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.</li> <li>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</li> <li>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.</li> <li>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).</li> <li>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</li> <li>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</li> <li>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</li> <li>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</li> <li>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</li> </ol>

Impact Statements

Better understanding and enhancing crop performance under various stress conditions such as drought, heat, and nutrient deficiency
These technologies enable the processing of large data sets for the precise identification of desirable traits, ultimately leading to more efficient breeding programs and optimized crop health monitoring.
The use of advanced technologies to understand plant structure and health will result in new strategies to combat diseases and pests, reducing crop losses and enhancing yield.
Building a knowledgeable workforce ready to tackle the future challenges in agriculture with innovative solutions ensures the sustainability and advancement of the agricultural sector.