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.


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

03/20/2024

1. Bai, G., Koehler-Cole, K., Scoby, D., Thapa, V.R., Basche, A., Ge, Y., Enhancing estimation of cover crop biomass using field-based high-throughput phenotyping and machine learning models. Frontiers in Plant Science 14, 1277672. doi: 10.3389/fpls.2023.1277672


2. Cooper J, Du C, Beaver Z, Zheng M, Page R, Wodarek J, Matny O, Szinyei T, Quiñones A, Anderson J, Smith K, Yang C, Steffenson B, Hirsch C. An RGB based deep neural network for high fidelity Fusarium head blight phenotyping in wheat. bioRxiv.https://doi.org/10.1101/2023.09.20.558703


3. Das Choudhury, S., Rosaria Guadagno, C., Bashyam, S., Mazis, A., Ewers, E.B., Samal, A., and Awada, T. (2024). Stress Phenotyping Analysis Leveraging Autofluorescence Image Sequences with Machine Learning. Front. Plant. Sci, V15. doi: 10.3389/fpls.2024.1353110


4. DeLoose M, Cho H, Bouain N, Choi I, Prom-U-T , C, Zaigham S; Luqing Z; Rouached H. 2024. PDR9 Allelic Variation and MYB63 Modulate Nutrient-Dependent Coumarin Homeostasis in Arabidopsis. The Plant Journal. 2024.


5. Grubbs, E.K., Gruss, S.M., Schull, V.Z., Gosney, M.J., Mickelbart, M.V., Brouder, S., Gitau, M.W., Bermel, P., Tuinstra,R., Agrawal, R., 2024. Optimized agrivoltaic tracking for nearly-full commodity crop and energy production. Renewable and Sustainable Energy Reviews, 191, p.114018. https://doi.org/10.1016/j.rser.2023.114018


6. Gruss, S.M., Johnson, K.D., Radcliffe, J.S., Lemenager, R.P. and Tuinstra, M.R., Preference of dhurrin‐free sorghum by ewes. Crop, Forage & Turfgrass Management, 10(1), p.e20259. https://doi.org/10.1002/cft2.20259


7. Joseph K.T., K. Muvva, H. Mwunguzi, A. Haake, C. Liew, A. Balabantaray, S. Behera, A. Kalra, K. K. Vattiam Srikanth, S. Pitla and D. Choudhury, CottonHusker: Deep Learning Enabled Cotton Picking Robot for Smart Agriculture, International Conference on Systems and Technology for Smart Agriculture (ICSTA), Kolkata, India, December 2023.


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


9. Kaiser, E., Von Gillhaussen, P., Clarke, J., and Schurr, U. (2024) IPPS 2022 - Plant Phenotyping for a Sustainable Future. Frontiers in Plant Sciences - Plant Breeding, 15. https://doi.org/10.3389/fpls.2024.1383766


10. Krafft, D., C. G. Scarboro, W. Hsieh, C. Doherty, P. Balint-Kurti, and Kudenov, "Mitigating Illumination-, Leaf-, and View-Angle Dependencies in Hyperspectral Imaging Using Polarimetry," Plant Phenomics **0**, (2024).


11. Lopez-Cruz, M., Aguate, F.M., Washburn, J.D., de Leon N, Kaeppler SM, Lima DC, Tan R, Thompson AM, De La Bretonne LW, de los Campos G. Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America. Nat Commun 14, 6904 (2023). https://doi.org/10.1038/s41467-023-42687-4


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


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


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


15. Quiñones R., F. Munoz-Arriola, D. Choudhury, A. Samal, OSC-CO2: Coattention and Cosegmentation Framework for Plant State Change with Multiple Features, Frontiers in Plant Science, 14: doi: 10.3389/fpls.2023.1211409, October 2023.


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


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


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


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


20. Srivastava, S., Kumar, N., Malakar, A. Choudhury, S.D. A Machine Learning-Based Probabilistic Approach for Irrigation Scheduling. Water Resour Manage (2024). https://doi.org/10.1007/s11269-024-03746-7


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


22. Tuggle CK, Clarke JL, Murdoch BM, Lyons E, Scott NM, Benes B, Campbell JD, Chung H, Daigle CL, Choudhury SD, Dekkers JCM, Dórea JRR, Ertl DS, Feldman M, Fragomeni BO, Fulton JE, Guadagno CR, Hagen DE, Hess AS, Kramer LM, Lawrence-Dill CJ, Lipka AE, Lübberstedt T, McCarthy FM, McKay SD, Murray SC, Riggs PK, Rowan TN, Sheehan MJ, Steibel JP, Thompson AM, Thornton KJ, Van Tassell CP, Schnable PS. Current challenges and future of agricultural genomes to phenomes in the USA. Genome Biol 25, 8 (2024). https://doi.org/10.1186/s13059-023-03155-w


23. Ying S, Webster B, Gomez-Cano L, Shivaiah KK, Wang Q, Newton L, Grotewold E, Thompson A, Lundquist PK. 2023. Multiscale physiological responses to nitrogen supplementation of maize hybrids, Plant Physiology, kiad583, https://doi.org/10.1093/plphys/kiad583


24. Zhao, B., Stephenson, B.M., Awada, T., Volesky, J., Wardlow, B., Zhou, Y., and Shi, Y. (2024). 15-Yr Biomass Production in Semiarid Nebraska Sandhills Grasslands: Part 1—Plant Functional Group Analysis. Rangeland Ecology & Management, 93:49-61. https://doi.org/10.1016/j.rama.2023.12.001