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Dr. Sindhuja Sankaran Phenomics Lab

Publications

Refereed Publications

2025

  • Umani, K., de Almeida Teixeira, G.H., Schroder, B.K., and Sankaran, S. Evaluation of spatial variability of volatile organic compounds in potato bulk storage facility using FAIMS. Journal of Food Measurement and Characterization, https://doi.org/10.1007/s11694-025-03819-0.
  • Valencia-Ortiz, M., McGee, R.J, and Sankaran, S. 2025. Early detection of Aphanomyces root rot in pea plants using hyperspectral imaging. Physiological and Molecular Plant Pathology, 140, 102862, https://doi.org/10.1016/j.pmpp.2025.102862.
  • Hoyos-Villegas, V., Jackson, M., Vargas-Cedeño, M., Farmer, E.E., Hanneman, M, Mazis, A., Singh, K.D., Sangjan, W., McNair, M., Sankaran, S., Tirado Tolosa, S., Gore, M.A., and Rife, T.W. 2025. Affordable Phenomics: Expanding access to enhancing genetic gain in plant breeding. 8 (1), e70034, The Plant Phenome Journal, https://acsess.onlinelibrary.wiley.com/doi/10.1002/ppj2.70034.
  • Valencia-Ortiz, M., McGee, R.J, and Sankaran, S. Field asymmetric ion mobility spectrometry for early detection of Aphanomyces root rot in peas using volatile biomarkers. Journal of Agricultural and Food Chemistry, 73, 19, 12083–12092. 10.1021/acs.jafc.4c12571. [ACS’s Editor Choice Article]
  • Umani, K., Daba, S., Piaskowski, J., McGee, R.J., Vandemark, G.J., and Sankaran, S. 2025. Evaluation of genotype x environment interaction using yield and UAV-based vegetation index data from multi-environment trials in chickpea. Journal of Crop Improvement, 39(3), 225–250. https://doi.org/10.1080/15427528.2025.2489605.
  • Valencia-Ortiz, M., McGee, R.J, Carter, A.H., and Sankaran, S. Variability in vegetation indices as a function of unmanned aerial vehicle flight altitudes and other factors during crop monitoring applications. Agricultural Engineering International: CIGR Journal, 27 (2), 268-284. https://cigrjournal.org/index.php/Ejounral/article/view/9393
  • Upadhaya, S.G.C., Zhang, C., Sankaran, S., Paulitz, T., and Wheeler, D.. Classification of Verticillium dahliae vegetative compatibility groups (VCGs) with machine learning and hyperspectral imagery. Applied Microbiology, 5(2), 41. https://doi.org/10.3390/applmicrobiol5020041.
  • Marzougui, A., McConnel, C.S., Adams-Progar, A., Biggs, T.D., Ficklin, S., and Sankaran, S. Machine learning-derived cumulative health measure for assessing disease impact in dairy cattle. Frontiers in Animal Science, 6:1532385. https://doi.org/10.3389/fanim.2025.1532385.
  • Veloo, K., Zúñiga-Espinoza, C., Espinoza Salgado, A., Jacoby, P.W., and Sankaran, S. 2025. Multispectral, thermal, and hyperspectral sensing data depict stomatal conductance in grapevine. Remote Sensing, 17(1), 137. https://doi.org/10.3390/rs17010137.
  • Valencia-Ortiz, M., McGee, R.J, and Sankaran, S. 2025. Optimization of field asymmetric ion mobility spectrometry-based assessment of Aphanomyces root rot in pea. Crop Protection, 187, 106982. https://doi.org/10.1016/j.cropro.2024.106982.
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Book Chapters

  • Zhang, C., and Sankaran, S. 2022. High-throughput extraction of seed traits using image acquisition and analysis. Editor: Lorence, A. Springer. In Methods in Molecular Biology, 2539:71-76, 10.1007/978-1-0716-2537-8_8, 71-76.
  • Sankaran, S., Khot, L.R., and Ehsani, R. 2019. Applies remote sensing systems in phytopathology. In ‘Precision phytopathology: Frontiers of Science’, Editors: Edson Luiz Furtado, Sérgio Florentino Pascholati, Waldir Cintra de Jesus Junior, Willian Bucker Moraes, Fundação de Estudos e Pesquisas Agrícolas e Florestais (FEPAF), Brazil, pp. 25-45.
  • Sankaran, S., Zhang, C., and Marzougui, A. 2017. Sensing for stress detection and high-throughput phenotyping in precision horticulture. In ‘Automation in tree fruit production: Principles and practice’, Editor: Zhang Q. CABI, pp. 28-42.
  • Sankaran, S., Khot, L.R., and Ehsani, R. 2014. Mid- and Far-infrared Imaging. In ‘Imaging with Electromagnetic Spectrum: Applications in Food and Agriculture’. Editors: Alahakoon, M.K., Annamalai, M., and Jayasuriya, H.P.W. Springer, 129-146.
  • Sankaran, S., and Ehsani, R. 2014. Introduction to the Electromagnetic Spectrum. In ‘Imaging with Electromagnetic Spectrum: Applications in Food and Agriculture’. Editors: Alahakoon, M.K., Annamalai, M., and Jayasuriya, H.P.W. Springer, 1-15.
  • Aksenov, A., Guaman, N.A.V., Sankaran, S., Fung, A.G., Pasamontes, A., Frederico, M., Cheung, W.H.K., Ehsani, R., Dandekar, A.M., and Davis, C.E. 2013. Volatile organic compounds (VOCs) for noninvasive plant diagnostics. In ‘Pest Management with Natural Products’. Editors, J.J. Beck, J.R. Coats, S.O. Duke, M.E. Koivunen. ACS Publications, doi: 10.1021/bk-2013-1141.fw001, pp. 73-95.
  • Van Leeuwen, J. (Hans), Rasmussen, M.L., Sankaran, S., Koza, C.R., Erickson, D.T., Mitra, D., and Jin, B. 2011. Fungal treatment of crop processing wastewaters with value-added co-products. In ‘Sustainable Bioenergy and Bioproducts’. Eds: Kasthurirangan Gopalakrishnan, Hans van Leeuwen, Robert C. Brown. Chapter II, Springer-Verlag Inc., pp. 13-44.

Proceeding Publications/Reports

  • Veloo, K., Carter, A.H., Garland-Campbell, K., Pumphrey, M.O., Rajagopalan, K., and Sankaran, S. 2024. UAV-derived digital trait analysis for consistent representation of wheat grain yield and adaptability across variable environments. Paper No. 2401227, 2024 ASABE AIM, Anaheim, CA, 29-31 July 2024.
  • Sangjan, W., Pukrongta, N., Carter, A. H., Pumphrey, M. O., and Sankaran, S. 2022. Development of IoT-based camera system for automated in-field monitoring to support crop breeding Programs. Authorea Preprints.
  • Sangjan, W., Carter, A. H., Pumphrey, M. O., and Sankaran, S. 2021. Development of sensor system for the internet of things (IoT)-based automated in-field monitoring to support crop improvement programs. Paper No. 2100696, In 2021 American Society of Agricultural and Biological Engineers (ASABE) Annual International Virtual Meeting (AIM), American Society of Agricultural and Biological Engineers.
  • Parhi, A., Sonar, C. R., Zhang, C., Rasco, B., Sankaran, S., Tang, J., and Sablani, S. S. 2020. Agar gel-based oxygen indicator for detection of defects in metal-oxide-coated multilayered food packaging films. Paper No. 2001337, In 2020 ASABE Annual International Virtual Meeting (p. 1). American Society of Agricultural and Biological Engineers.
  • Sankaran, S., Zhang, C., Hurst, P.J., Marzougui, A., Sivakumar, A.V.N., Li, J., Schnable, J., and Shi, Y. 2020. Investigating the potential of satellite imagery for high-throughput field phenotyping applications. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V (Vol. 11414). International Society for Optics and Photonics (SPIE).
  • Zhang, C., Craine, W., Davis, J. B., Khot, L. R., Marzougui, A., Brown, J., Hulbert, S. H., and Sankaran, S. 2018. Detection of canola flowering using proximal and aerial remote sensing techniques. Proceeding SPIE 10664, Autonomous air and ground sensing systems for agricultural optimization and phenotyping (III).
  • Zhang, C., Pumphrey, M., Zhou, J., Gao, H., Zhang, Q., and Sankaran, S. 2017. Development of automated high-throughput phenotyping system for controlled environment studies, Paper No. 1700581, 2017 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting (AIM), Spokane, WA.
  • Sankaran, S., Carter, A.H., Slaughter, D.C., Kirchhoff, H., Okamuro, J.K., Poland, J., and Kalcsits, L.A. 2016. Conference Summary Report on ‘Advances in Field-Based High-throughput Phenotyping and Data Management: Grains and Specialty Crops’.
  • Sankaran,  S., Khot, L.R., Quirós, J., Vandemark, G.J., and McGee, R.J. 2016. UAV-based high-throughput phenotyping in legume crops, Proceeding SPIE 9866, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping, 98660G (May 17, 2016); doi: http://dx.doi.org/10.1117/12.2228550.
  • Zúñiga, C.E., Khot, L.R., Jacoby, P., and Sankaran, S. 2016. Remote sensing based water-use efficiency evaluation in sub-surface irrigated wine grape vines. Proceeding SPIE 9866, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping, 98660O (May 17, 2016); doi: http://dx.doi.org/10.1117/12.2228791.
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