Brief Summary of Minutes of Annual Meeting

Meeting Minutes (Summary):

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.

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:

1. Identifying and sharing ongoing work and interests at each state institution
2. Outlining specific shared research ideas of broad interest to the group to enable coordinated projects across participants' institutions

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:

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. 

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.

Awada:

• Leading and participating in large data pipeline development for multi-source images, 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 derived from images at multiple scales) in managed and natural systems.

Walia:

• “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 Transcriptomics of Developing Maize Kernel Under Heat Stress" - NAES, Walia PI

Ghosh:

• Automatic Tassel detection and prediction from UAV images with associated uncertainty quantification
• Global shrinkage and local selection of genomic features from functional phenotypic data
• High-Definition Spatiotemporal Phenomics and Transcriptomics of Developing Maize Kernel under Heat stress 

Delaware (Sparks, Bao)

Ongoing funded projects include:

• “Field Robotic Systems to Support BlueTech Platforms for Sustainable Ocean-Based Activities” - UNIDEL Foundation
• “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

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) 

• 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.
• 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.
• 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.
• 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. 

South Dakota (Mural, Yadav)

Newly established lab / no activity reported

Michigan (Thompson, Rouched)

Thompson:

Ongoing funded projects include:

• 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 

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:

• 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
• Development of phenomics tools for pulse breeding programs, 2022-2024
• Phenomics and modeling enabled decision support for climate-adapted wheat germplasm development, 2022-2025
• FACT: Research experience for undergraduates on phenomics big data management, 2020-2025
• High-throughput phenotyping techniques to advance variety selection in grain legume, 2016-2023

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.

• 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. 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).
2. 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.
3. 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.
4. 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
5. 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
6. 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
7. 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
8. 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
9. 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.
10. 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).
11. 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
12. 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
13. 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
14. 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
15. 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.
16. 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.
17. 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.
18. 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
19. 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.
20. 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).
21. 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
22. Gao T, Chandran AKN, Paul P, Walia H, Yu H. HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds. 2021, Sensors
23. 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
24. 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
25. 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
26. 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
27. 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
28. 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.
29. 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
30. 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
31. 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
32. 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.
33. 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.
34. 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.
35. 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
36. 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
37. 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.
38. 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
39. 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
40. 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
41. 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
42. 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
43. 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
44. 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.
45. 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).
46. 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).
47. 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).
48. 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
49. 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
50. 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
51. 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.
52. Mazis, A., Awada, T., Erickson, G.E., Wardlow, B., Wienhold, B.J., Jin, V., Schmer, M., Suyker, A., Zhou, Y., Hiller, J. (2023). Synergistic use of optical and biophysical traits to assess Bromus inermis pasture performance and quality under different management strategies in Eastern Nebraska, U.S., Agriculture, Ecosystems & Environment, 348, 108400, https://doi.org/10.1016/j.agee.2023.108400.
53. Meier M, Xu G, Lopez-Guerrero, Li G, Smith C, Sigmon B, Herr J, Alfano J, Ge Y, Schnable JC, Yang J (2022) “Maize root-associated microbes likely under adaptive selection by the host to enhance phenotypic performance.” eLife doi: 10.7554/eLife.75790 bioRxiv doi: 10.1101/2021.11.01.466815
54. Meier MA, Lopenz-Guerrero MG, Guo M, Schmer MR, Herr JR, Schnable JC, Alfano JR, Yang J (2021) “Rhizosphere microbiomes in a historical maize/soybean rotation system respond to host species and nitrogen fertilization at genus and sub-genus levels.” Applied and Environmental Microbiology doi: 10.1128/AEM.03132-20 bioRxiv doi: 10.1101/2020.08.10.244384
55. Méline V, Caldwell DL, Kim B-S, Khangura RS, Baireddy S, Yang C, Sparks EE, Dilkes B, Delp EJ and Iyer-Pascuzzi AS. “Image-based assessment of plant disease progression identifies new genetic loci for resistance” The Plant Journal, 2023 Mar; 113(5):887-903.
56. Miao C, Guo A, Thompson AM, Yang J, Ge Y, Schnable JC “Automation of leaf counting in maize and sorghum using deep learning.” The Plant Phenome Journal doi: 10.1002/ppj2.20022 bioRxiv doi: 10.1101/2020.12.19.423626
57. Mural RV, Grzybowski M, Miao C, Damke A, Sapkota S, Boyles RE, Salas Fernandez MG, Schnable PS, Sigmon B, Kresovich S, Schnable JC (2021) “Meta-analysis identifies pleiotropic loci controlling phenotypic trade-offs in sorghum.” Genetics doi: 10.1093/genetics/iyab087 bioRxiv doi: 10.1101/2020.10.27.355495
58. Mural RV, Sun G, Grzybowski M, Tross MC, Jin H, Smith C, Newton L, Andorf CM, Woodhouse MR, Thompson AM, Sigmon B, Schnable JC (2022) “Association mapping across a multitude of traits collected in diverse environments identifies pleiotropic loci in maize.” Gigascience doi: 10.1093/gigascience/giac080 bioRxiv doi: 10.1101/2022.02.25.480753
59. Nam HI, Shahzad Z, Dorone Y, Clowez S, Zhao K, Bouain N, Lay-Pruitt KS, Cho H, Rhee SY*, Rouached H*. Interdependent Iron and Phosphorus Availability Controls Photosynthesis Through Retrograde Signaling. Nature Communications. 2021. Faculty Opinions
60. Nazeri, B., Crawford, M. and Tuinstra, M.R., Estimating Leaf Area Index in Row Crops Using Wheel-Based and Airborne Discrete Return Lidar Data. Frontiers in Plant Science, p.2727. https://doi.org/10.3389/fpls.2021.740322
61. Obayes S, Timber L, Head M, and Sparks EE. “Evaluation of Brace Root Parameters and Its Effect on the Stiffness of Maize” In Silico Plants, 2022 May.
62. Orozco J, Manian V, Alfaro E, Walia H, Dhatt BK. Graph Convolutional Network Using Adaptive Neighborhood Laplacian Matrix for Hyperspectral Images with Application to Rice Seed Image Classification. 2023, Sensors
63. Pabuayon ICM, Kitazumi A, Cushman KR, Singh RK, Gregorio GB, Dhatt B, Zabet-Moghaddam M, Walia H, de Los Reyes BG. Novel and Transgressive Salinity Tolerance in Recombinant Inbred Lines of Rice Created by Physiological Coupling-Uncoupling and Network Rewiring Effects. 2021, Frontiers of Plant Sciences
64. Parhi, A., Zhang, C., Sonar, C. R., Sankaran, S., Rasco, B., Tang, J., and Sablani, S. 2022. Finding a carbohydrate gel-based oxygen indicator for expedited detection of defects in metal-oxide coated food packaging. Food Packaging and Shelf Life, 34, 100973.
65. Quiñones, F. Munoz-Arriola, S. D. Choudhury, A. Samal, Multi-feature Data Repository Development and Analytics for Image Co-segmentation in High Throughput Plant Phenotyping, Plos One, 2021. http://doi.org//10.1371/journal.pone.0257001
66. Raman, M.G., Carlos, E.F., and Sankaran, S. Optimization and evaluation of sensor angles for precise assessment of architectural traits in peach trees. Sensors, 22(12), 4619; https://doi.org/10.3390/s22124619.
67. 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.
68. Rodene, E., Xu, G., Delen, S.P., Zhao, X., Smith, C., Ge, Y., Schnable, J., Yang, J., 2022. A UAV-based high-throughput phenotyping approach to assess time-series nitrogen responses and identify trait-associated genetic components in maize. The Plant Phenome Journal 5(1), e20030. https://doi.org/10.1002/ppj2.20030
69. Bashyam, S. D. Choudhury, A. Samal, T. Awada, Visual Growth Tracking for Automated Leaf Stage Monitoring based on Image Sequence Analysis, Remote Sensing, 13(5), 2021.
70. D. Choudhury, S. Saha, A. Samal, A. Mazis, T. Awada, Drought Stress Prediction and Propagation using Time Series Modeling on Multimodal Plant Image Sequences, Frontiers in Plant Science, 14: 1003150, February 2023.
71. Sandhu, K.S., Merrick, L.F., Sankaran, S., Zhang, Z., and Carter, A.H. 2022. Prospectus of genomic selection and phenomics in cereal, legume and oilseed breeding programs. Frontiers in Genetics, 12, https://doi.org/10.3389/fgene.2021.829131.
72. Sangjan, W., Carpenter-Boggs, L., Hudson, T., and Sankaran, S. Pasture productivity assessment under mob grazing and fertility management using satellite and UAS imagery. Drones, 6(9), 232; https://doi.org/10.3390/drones6090232.
73. Sangjan, W., Carter, A.H., Pumphrey, M., Hagemeyer, K., Jitkov, V., and Sankaran, S. Effect of high-resolution satellite and UAV imagery plot pixel resolution in wheat crop yield prediction. International Journal of Remote Sensing, 45(5), 1678-1698.
74. Sangjan, W., McGee, R.J., and Sankaran, S. 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.
75. 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.
76. 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
77. 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.
78. 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.
79. 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
80. 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. Remote Sensing. 13: 1-26. https://doi.org/10.3390/rs13010026
81. Sun G, Mural RV, Turkus JD, Schnable JC (2021) “Quantitative resistance loci to southern rust mapped in a temperate maize diversity panel.” Phytopathology doi: doi.org/10.1094/PHYTO-04-21-0160-R bioRxiv doi: 10.1101/2021.04.02.438220
82. Sweet D, Tirado S, Springer N, Hirsch CN, Hirsch CD. Opportunities and challenges in phenotyping row crops using drone‐based RGB imaging. The Plant Phenome Journal, 5(1), e20044. https://doi.org/10.1002/ppj2.20044
83. Tang, Z., Wang, M., Schirrmann, M., Dammer, K.H., Li, X., Brueggeman, R., Sankaran, S., Carter, A., Pumphrey, M., Hu, Y., Chen, X., and Zhang, Z. 2023. Affordable high throughput field detection of wheat stripe rust using deep learning with semi-automated image labeling. Preprints-57181, Computers and Electronics in Agriculture, 207, 107709.
84. 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
85. 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
86. Tolley, S.A., Singh, A. and Tuinstra, M., Heterotic Patterns of Temperate and Tropical Maize by Ear Photometry. Frontiers in Plant Science, 12, p.1117. https://doi.org/10.3389/fpls.2021.616975
87. Tross MC, Gaillard M, Zweiner M, Miao C, Li B, Benes B, Schnable JC “3D reconstruction identifies loci linked to variation in angle of individual sorghum leaves.” PeerJ doi: 10.7717/peerj.12628 bioRxiv doi: 10.1101/2021.06.15.448566
88. Umani, K., Zhang, C., McGee, R.J., Vandemark, G. J., and Sankaran, S. A pulse crop dataset of agronomic traits and multispectral images from multiple environments. Data-in-Brief, 53, 110013. https://doi.org/10.1016/j.dib.2023.110013.
89. Valencia-Ortiz, M., and Sankaran, S. Development of a semi-automated volatile organic compounds (VOCs) sampling system for field asymmetric ion mobility spectrometry (FAIMS) analysis. HardwareX, 12, e00344; https://doi.org/10.1016/j.ohx.2022.e00344.
90. Valencia-Ortiz, M., Marzougui, A., Zhang, C., Bali, S., Odubiyi, S., Sathuvalli, S., Bosque-Pérez, N.A., Pumphrey, M.O., and Sankaran, S. Biogenic VOCs emission profiles associated with plant-pest interaction for phenotyping applications. Sensors, 22(13), 4870. https://doi.org/10.3390/s22134870.
91. 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
92. 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
93. 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.
94. Xie X, Ge Y, Walia H, Yang J, Yu H. Leaf-counting in monocot plants using deep regression models. 2023, Sensors
95. Yang Q, Van Haute M, Korth N, Sattler S, Toy J, Rose D, Schnable JC, Benson A “Genetic analysis of seed traits in Sorghum bicolor that affect the human gut microbiome.” Nature Communications doi: 10.1038/s41467-022-33419-1 Research Square doi: 10.21203/rs.3.rs-1490527/v1
96. Yang, K.W., Chapman, S., Carpenter, N., Hammer, G., McLean, G., Zheng, B., Chen, Y., Delp, E., Masjedi, A., Crawford, M. Ebert, D., Habib, A., Thompson, A., Weil, C., Tuinstra, M.R., Integrating crop growth models with remote sensing for predicting biomass yield of sorghum. in silico Plants, 3(1), p.diab001. https://doi.org/10.1093/insilicoplants/diab001
97. Yu H, Du Q, Campbell M, Yu B, Walia H, Zhang C. Genome-wide discovery of natural variation in pre-mRNA splicing and prioritising causal alternative splicing to salt stress response in rice. 2021, New Phytologist
98. Zaidi, P.H., Vinayan M.T., Nair, S.K., Kuchanur P.H., Kumar, R., Singh, S.B., Tripathi, M.P., Patil, P., Ahmed, S., Hussain, A., Kulkarni, A.P., Wangmo, P., Tuinstra, M.R., Prasanna, B.M., 2023. Heat-tolerant maize for rainfed hot, dry environments in the lowland tropics: From breeding to improved seed delivery. The Crop Journal. https://doi.org/10.1016/j.cj.2023.06.008
99. Zhan, Y., Zhang, R., Zhou, Y., Stoerger, V., Hiller, J., Awada, T., and Y. (2022). Rapid online plant leaf area change detection with high-throughput plant image data. Journal of Applied Statistics. https://doi.org/10.1080/02664763.2022.2150753. 
100. Zhang J, Min A, Steffenson B, Su W, Hirsch C, Anderson J, Wei J, Ma Q, Yang C. 2022. Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on an Optimized Hybrid Task Cascade Model. Frontiers in Plant Science, 13. https://doi.org/10.3389/fpls.2022.834938
101. Zhang, C., Chen, T., Chen, W., and Sankaran, S. Non-invasive evaluation of Ascochyta blight disease severity in chickpea using field-asymmetric ion mobility spectrometry and hyperspectral imaging techniques. Crop Protection, 165, 106163.
102. Zhang, C., Serra, S., Quirós-Vargas, J., Sangjan, W., Musacchi, S., and Sankaran, S. Non-invasive sensing techniques to phenotype multiple apple tree architectures. Information Processing in Agriculture, 10 (1), 136-147, https://doi.org/10.1016/j.inpa.2021.02.001.
103. Zhang, H., Ge, Y., Xie, X., Atefi, A., Wijewardane, N.K., Thapa, S., 2022. High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion. Plant Methods 18, 60. https://doi.org/10.1186/s13007-022-00892-0
104. Zhou Y, Kusmec A, Mirnezami SV, Srinivasan L, Jubery TZ, Schnable JC, Salas-Fernandez MG, Nettleton D, Ganapathysubramanian B, Schnable PS (2021) “Identification and utilization of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping.” The Plant Cell doi: 10.1093/plcell/koab134
105. Zhu F, Pan Y, Gao T, Walia H, Yu H. Interactive visualization of hyperspectral Images Based on Neural Networks. 2021, IEEE Computer Graphics and Applications
106. Zhu F, Paul P, Hussain W, Wallman K, Dhatt BK, Sandhu J, Irvin L, Morota G, Yu H, Walia H*. SeedExtractor: An Open-Source GUI for Seed Image Analysis. 2021, Frontiers of Plant Science
107. Zhu F, Saluja M, Dharni JS, Paul P, Sattler SE, Staswick P, Walia H, Yu H. PhenoImage: An open-source graphical user interface for plant image analysis. 2021, The Plant Phenome Journal
108. Zhu Y, Chen Y, Ali Md. A, Dong L, Wang X, Archontoulis SV, Schnable JC, Castellano MJ (2021) “Continuous in situ soil nitrate sensors: a comparison with conventional measurements and the value of high temporal resolution measurements.” Soil Science Society of America Journal doi: 10.1002/saj2.20226