NC1212: Exploring the Plant Phenome in Controlled and Field Environments

(Multistate Research Project)

Status: Active

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SAES-422 Reports

08/16/2023


  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.

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  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.

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  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

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  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.

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03/20/2024


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  8. Kick DR, Wallace JG, Schnable JC, Kolkman JM, Alaca B, Beissinger TM, Edwards J, Ertl D, Flint-Garcia S, Gage JL, Hirsch CN, Knoll JE, de Leon N, Lima DC, Moreta DE, Singh MP, Thompson AM, Weldekidan T, Washburn JD. Yield prediction through integration of genetic, environment, and management data through deep learning, G3 Genes|Genomes|Genetics, Volume 13, Issue 4, April 2023, jkad006, https://doi.org/10.1093/g3journal/jkad006

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  12. Nguyen, H.M., S. Gyurek, R. Mierop, K. V. Pecota, K. LaGamba, M. Boyette, G. C. Yencho, C. M. Williams, and W. Kudenov, "Deployment and Analysis of Instance Segmentation Algorithm for In-field Grade Estimation of Sweetpotatoes," (2023).

  13. Pan, Y., Sun, J., Yu, H., Bai, G., Ge, Y., Luck, J., and Awada, T. (2024). Transforming Agriculture with Intelligent Data Management and Insights," 2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy, 2023, pp. 3489-3498, doi: 10.1109/BigData59044.2023.10386589.

  14. Quiñones C, Adviento-Borbe MA, Larazo W, Shea Harris R, Mendez K, Cunningham SS, Campbell ZC, Medina-Jimenez K, Hein NT, Wagner D, Ottis B, Walia H, Lorence A. Field-based infrastructure and cyber–physical system for the study of high night air temperature stress in irrigated rice. 2023, The Plant Phenome Journal

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  16. Raman, M.G., Marzougui, A., Teh, S.L., York, Z.B., Evans, K.M., and Sankaran, S. Rapid assessment of architectural traits in pear rootstock breeding program. Remote Sensing, 15(6), 1483; https://doi.org/10.3390/rs15061483.

  17. Sahay S, Grzybowski M, Schnable JC, Glowacka K (2023) Genetic control of photoprotection and photosystem II operating efficiency in plants. New Phytologist doi: 10.1111/nph.18980

  18. Sangjan, W., McGee, R.J., and Sankaran, S. Evaluation of forage quality in a pea breeding program using a hyperspectral sensing system. Computer and Electronics in Agriculture, 212, 108052. https://doi.org/10.1016/j.compag.2023.108052.

  19. Schrickx, H.M., S. Gyurek, C. Moore, E. Hernández-Pagán, C. J. Doherty, W. Kudenov, and B. T. O’Connor, "Flexible Self-Powered Organic Photodetector with High Detectivity for Continuous On-Plant Sensing," Advanced Optical Materials **n/a**, (2024).

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  21. Staswick P, Singh J, Shi Y, Zhang C, Petersen C, Walia H. Growth and Transcriptional Responses to the Tertiary Amine BMVE in Wheat and Rice. 2023, Frontiers of Plant Sciences

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