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Dr. Sindhuja Sankaran Phenomics Lab

Complete List of Refereed Publications

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2023

  • 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.
  • 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.
  • 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.
  • Raman, M.G., Marzougui, A., Teh, S.L., York, Z.B., Evans, K.M., and Sankaran, S. 2023. Rapid assessment of architectural traits in pear rootstock breeding program. Remote Sensing, 15(6), 1483; https://doi.org/10.3390/rs15061483.
  • 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.
  • 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. 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.
  • Zhang, C., Serra, S., Quirós-Vargas, J., Sangjan, W., Musacchi, S., and Sankaran, S. 2023. 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.
  • 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.

2022

  • 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, 106163.
  • 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.
  • 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.
  • Sangjan, W., Carpenter-Boggs, L., Hudson, T., and Sankaran, S. 2022. Pasture productivity assessment under mob grazing and fertility management using satellite and UAS imagery. Drones, 6(9), 232; https://doi.org/10.3390/drones6090232.
  • Valencia-Ortiz, M., and Sankaran, S. 2022. 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.
  • Valencia-Ortiz, M., Marzougui, A., Zhang, C., Bali, S., Odubiyi, S., Sathuvalli, S., Bosque-Pérez, N.A., Pumphrey, M.O., and Sankaran, S. 2022. Biogenic VOCs emission profiles associated with plant-pest interaction for phenotyping applications. Sensors, 22(13), 4870. https://doi.org/10.3390/s22134870.
  • Raman, M.G., Carlos, E.F., and Sankaran, S. 2022. 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.
  • Sangjan, W., McGee, R.J., and Sankaran, S. 2022. 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.
  • 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.
  • Sangjan, W., Marzougui, A., Mattinson, D.S., Schroeder, B.K., Bates, A.A., Khot, L.R., and Sankaran, S. 2022. Identification of volatile biomarkers for high-throughput sensing of soft rot and Pythium leak diseases in stored potatoes. Food Chemistry, 370, 130910.
  • Marzougui, A., Rajendran, A., Mattinson, D.S., Ma, Y., McGee, R.J., Garcia-Perez, M., Ficklin, S.P., and Sankaran, S. Evaluation of biogenic markers-based phenotyping for resistance to Aphanomyces root rot in field pea. Information Processing in Agriculture, 9 (1), 1-10, https://doi.org/10.1016/j.inpa.2021.01.007.
  • Sinha, R., Quirós-Vargas, J., Sankaran, S., and Khot, L.R. 2022. High resolution aerial photogrammetry-based 3D mapping of fruit crop canopies for precision inputs management. Information Processing in Agriculture, 9 (1), 11-12, https://doi.org/10.1016/j.inpa.2021.01.006.

2021

  • Valencia-Ortiz, M., Sangjan, W., Selvaraj, M.O., McGee, R.J., and Sankaran, S. Effect of the solar zenith angles at different latitudes on estimated crop vegetation indices. Drones, 5 (3), 80, https://doi.org/10.3390/drones5030080.
  • Sangjan, W., and Sankaran, S. Phenotyping architecture traits of tree species using remote sensing techniques. Transactions of the ASABE, 64(5): 1611-1624.
  • Zhi, Q., Tang, J., Sablani, S.S., Ross, C.F., Sankaran, S., and Shah, D.H. Quality changes in chicken livers during cooking. Poultry Science, 100 (9), 101316, https://doi.org/10.1016/j.psj.2021.101316.
  • Kothawade, G.S., Chandel, A.K., Khot, L.R., Sankaran, S., Bates, A., and Schroeder, B.K. 2021. Field asymmetric ion mobility spectrometry for pre-symptomatic rot detection in stored Ranger Russet and Russet Burbank potatoes. Postharvest Biology and Technology, 181, 111679, https://doi.org/10.1016/j.postharvbio.2021.111679.
  • Sangjan, W., Carter, A.H., Pumphrey, M., Jitkov, V., and Sankaran, S. Development of a Raspberry Pi-based sensor system for automated in-field monitoring to support crop breeding Inventions, 6, 42, https://doi.org/10.3390/inventions6020042.
  • Kholova, J., Urban, M.O., Cock, J., Arcos, J., Arnaud, E., Aytekin, D., Azevedo, V., Barnes, A.P., Ceccarelli, S., Chavarriaga, P., Cobb, J., Connor, D., Cooper, M., Craufurd, P., Debouck, D., Fungo, R., Grando, S., Hammer, G.L., Jara, C., Messina, C., Mosquera, G., Nchanji, E., Ng, E., Prager, S., Sankaran, S., Selvaraj, M., Tardieu, F., Thornton, P., Valdes, S., van Etten, J., Wenzl, P., and Xu, Y. 2021. Together for the food-secure future: The crop improvement strategy for 2020’s and beyond. Journal of Experimental Botany, 72 (14), 5158-5179, https://doi.org/10.1093/jxb/erab226.
  • Sexton, T., Sankaran, S., and Cousins, A.B. 2021. Predicting photosynthetic capacity in tobacco (Nicotiana tabacum) using shortwave infrared spectral reflectance. Journal of Experimental Botany, 72(12), 4373-4383.
  • Zhang, C., Craine, W.A., McGee, R.J., Vandemark, G.J., Davis, J.B., Brown, J., Hulbert, S.H., and Sankaran, S. 2021. High-throughput phenotyping of plant height in cool-season crops using proximal and remote sensing techniques. Agronomy Journal, 113 (4), 3269-3280, https://doi.org/10.1002/agj2.20632.
  • Sankaran, S., Marzougui, A., Hurst, J.P., Zhang, C., Schnable, J.C., and Shi, Y. 2021. Can high-resolution satellite multispectral imagery be used to phenotype canopy traits and yield potential in field conditions? Transactions of the ASABE, 64(3): 879-891.
  • Zhang, C., McGee, R.J., Vandemark, G.J., and Sankaran, S. 2021. Crop performance evaluation of chickpea and dry pea breeding lines across seasons and locations using phenomics data. 2021. Frontiers in Plant Science, 12, 61.
  • Zhi, Q., Tang, Z., Liu, F., Sablani, S.S., Ross, C.F., Sankaran, S., and Tang, J. Quality of green beans (Phaseolus vulgaris L.) influenced by microwave and hot water pasteurization. Food Control, 124, 107936.

2020

  • Ma, Yu., Marzougui, A., Coyne, C.J., Sankaran, S., Main, D., Porter, L.D., Mugabe, D., Smitchger, J.A., Zhang, C., Amin, M.D., Rasheed, N., Ficklin, S., and McGee, R.J. 2020. Dissecting the genetic architecture of Aphanomyces root rot resistance in lentil by QTL mapping and genome-wide association. International Journal of Molecular Sciences. International Journal of Molecular Sciences, 21 (6): 2129.
  • Zhang, C., Craine, W.A., McGee, R.J., Vandemark, G.J., Davis, J.B., Brown, J., Hulbert, S.H., and Sankaran, S. 2020. Image-based phenotyping of flowering intensity in cool-season crops. Sensors, 20 (5): 1450.
  • Kothawade, G.S., Sankaran, S., Bates, A., Schroeder, B.K., and Khot, L.R. 2020. Feasibility of volatile biomarker-based detection of Pythium leak in postharvest stored potato tubers using field asymmetric ion mobility spectrometry. Sensors, 20, 7350.
  • Marzougui, A., Ma, Y., McGee, R.J., Khot, L., and Sankaran, S. Generalized linear model with elastic net regularization and convolutional neural network for evaluating Aphanomyces root rot severity in lentil. Plant Phenomics, 2020: 2393062, https://doi.org/10.34133/2020/2393062.

2019

  • Sankaran, S.*, Quirós, J.J.*, and Miklas, P. 2019. Unmanned aerial system and satellite-based high resolution imagery for high-throughput phenotyping in dry bean. Computer and Electronics in Agriculture, 165: 104965.
  • Zhang, C., Chen, W., and Sankaran, S. 2019. High-throughput field phenotyping of Ascochyta blight disease severity in chickpea. Crop Protection, 125: 104885.
  • Quirós, J.J., McGee, R.J., Vandemark, G., Romanelli, T., and Sankaran, S. 2019. Field phenotyping using multispectral imaging in pea (Pisum sativum L) and chickpea (Cicer arietinum L). Engineering in Agriculture, Environment and Food, Accepted, July, 2019.
  • Quirós, J.J., Zhang, C., Smitchger, J., McGee, R.J., and Sankaran, S. 2019. Phenotyping of plant biomass and performance traits using remote sensing techniques in pea (Pisum sativum, L). Sensors, 19 (9): 2031.
  • Chakraborty, M., Khot, L.R., Sankaran, S., and Jacoby, P. 2019. Evaluation of mobile 3D light detection and ranging based canopy mapping system for tree fruit crops, Computers and Electronics in Agriculture, 158: 284-293.
  • Zhang, C., Pumphrey, M., Zhou, J., Zhang, Q., and Sankaran, S. 2019. Development of automated high-throughput phenotyping system for wheat evaluation in controlled environment, Transactions of the ASABE, 62(1): 61-74.
  • Sankaran, S.*, Zúñiga, C.E.*, Hinojosa, L., Ma, X., and Murphy, K. 2019. High-throughput field phenotyping to assess irrigation treatment effects in quinoa. Agrosystems, Geosciences & Environment, 2, 180063, doi:10.2134/age2018.12.0063.
  • Marzougui, A., Ma, Yu, Zhang, C., McGee, R.J., Coyne, C.J., Main, D., and Sankaran, S. 2019. Advanced imaging for quantitative evaluation of Aphanomyces root rot resistance in lentil. Frontiers in Plant Science, doi: 10.3389/fpls.2019.00383.
  • Jarolmasjed, S.*, Sankaran, S.*, Marzougui, A., Kostick, S., Si3, Y., Quirós, J.J., and Evans, K. 2019. High-Throughput Phenotyping of fire blight disease symptoms using sensing techniques in apple. Frontiers in Plant Science, doi: 10.3389/fpls.2019.00576.

2018

  • Zúñiga, C.E.*, Rathnayake, A.P.*, Chakraborty, M., Sankaran, S., Jacoby, P., and Khot, L.R. 2018. Applicability of time-of-flight-based ground and multispectral aerial imaging for grapevine canopy vigour monitoring under direct root-zone deficit irrigation. International Journal of Remote Sensing, 39 (23), 8818-8836.
  • Sankaran, S., Zhou, J., Khot, L.R., Trapp, J.J., Mndolwa, E., and Miklas, P.N. 2018. High-throughput field phenotyping in dry bean using small unmanned aerial vehicle based multispectral imagery. Computers and Electronics in Agriculture, 151: 84-92.
  • Zhang, C., Si, Y., Lamkey, J., Boydston, R.A., Garland-Campbell, K.A., and Sankaran, S. 2018. High-throughput phenotyping of seed/seedling evaluation using digital image analysis. Agronomy, 8, DOI: 10.3390/agronomy8050063.
  • Si, Y., Sankaran, S., Knowles, N.R., and Pavek, M. J. 2018. Image-based automated potato tuber shape evaluation. Journal of Food Measurement and Characterization, 12 (2): 702-706.
  • Jarolmasjed, S., Sankaran, S., Kalcsits, L., and Khot, L.R. 2018. Proximal hyperspectral sensing of stomatal conductance to monitor the efficacy of exogenous abscisic acid applications in apple. Crop Protection, 109: 42-50.
  • Sinha, R., Khot, L.R., Schroeder, B.K., and Sankaran, S. 2018. FAIMS based volatile fingerprinting for real-time postharvest storage infections detection in stored potatoes and onions. Postharvest Biology and Technology, 135: 83-92.

2017

  • Zúñiga, C.E., Khot, L.R., Sankaran, S., and Jacoby, P. 2017. High resolution multispectral and thermal remote sensing-based water stress assessment in subsurface irrigated grapevines. Remote Sensing, 9 (9): 961, doi:10.3390/rs9090961.
  • Sankaran, S., Quirós, J.J., Knowles, N.R., and Knowles, L.O. 2017. High-resolution aerial imaging based estimation of crop emergence in potatoes. American Journal of Potato Research, 94: 658-663. DOI 10.1007/s12230-017-9604-2. [Research featured in journal cover page]
  • Zúñiga, C.E., Jarolmasjed, S., Sinha, R., Zhang, C., Kalcsits, L.S., Dhingra, A., and Sankaran, S. 2017. Spectrometric techniques for elemental profile analysis associated with bitter pit in apples. Postharvest Biology and Technology, 128: 121-129.
  • Kalcsits, L., Musacchi, S., Layne, D., Schmidt, T., Mupambi, G., Serra, S., Mendoza, M., Asteggiano, L., Jarolmasjed§, S., Sankaran, S., Khot, L.R., and Zúñiga§, C.E. 2017. Above and below-ground environmental changes associated with the use of photoselective protective netting to reduce sunburn in apple. Agricultural and Forest Meteorology, 237-238: 9-17.
  • Jarolmasjed, S., Zúñiga, C.E., and Sankaran, S. 2017. Near infrared spectroscopy to predict bitter pit development in different varieties of apples. Journal of Food Measurement and Characterization, 11 (3): 987-993.
  • Si, Y., Sankaran, S., Knowles, N.R., and Pavek, M. 2017. Automated potato tuber length-width ratio assessment using image analysis. American Journal of Potato Research, 94 (1): 88-93.

2016

  • Wang, M., Ellsworth, P., Zhou, J., Cousins, A., and Sankaran, S. 2016. Evaluation of water-use efficiency in foxtail millet (Setaria italica) using visible-near infrared and thermal spectral sensing techniques. Talanta, 152: 531-539.
  • Sankaran, S., Wang, M., and Vandemark, G. 2016. Image-based rapid phenotyping method of chickpeas seed size. Engineering in Agriculture, Environment and Food, 9 (1): 50-55.
  • Kafle, G.P., Khot, L.R., Sankaran, S., Bahlol, H.Y., Tufariello, J.A., and Hill Jr. H.H. 2016. State of ion mobility spectrometry and applications in agriculture: A review. Engineering in Agriculture, Environment and Food, 9 (4): 346-357.
  • Khot, L.R., Sankaran, S., Carter, A.H., Johnson, D.A., and Cummings, T.F. 2016. UAS imaging-based decision tools for arid winter wheat and irrigated potato production management, International Journal of Remote Sensing, 37 (1): 125-137.
  • Trapp, J.J., Urrea, C.A., Miklas, P.N., Zhou, J., Khot, L. R., Sankaran, S., and Miklas, P.N. 2016. Selective phenotyping traits related to multiple stress and drought response in common bean, Crop Science, 56: 1-13.
  • Jarolmasjed, S., Zúñiga Espinoza, C., Sankaran, S., and Khot, L.R. 2016. Postharvest bitter pit detection and progression evaluation in ‘Honeycrisp’ apples using computed tomography images, Postharvest Biology and Technology, 118: 35-42.
  • Zhou, J., Pavek, M.J., Shelton, S.C., Holden, Z.J., and Sankaran, S. 2016. Aerial multispectral imaging for crop hail damage assessment in potato. Computer and Electronics in Agriculture, 127: 406-412.
  • Si, Y., and Sankaran, S. 2016. Computed tomography imaging-based bitter pit rating in apples. Biosystems Engineering, 51, 9-16.
  • Zhang C., Gao, H., Zhou, J., Cousins, A., Pumphrey, M. O., and Sankaran, S. 2016. 3D robotic system development for high-throughput crop phenotyping. IFAC-PapersOnLine, 49 (16), 242-247.

2015

  • Sankaran, S., Khot, L.R., Zúñiga, C., Jarolmasjed, S., Sathuvalli, V., Vandemark, G., Miklas, P.N., Carter, A.H., Pumphrey, M.O., Knowles, N.R., and Pavek, M.J. 2015. Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A Review, European Journal of Agronomy, 70: 112-123.
  • Sankaran, S., Ehsani, R., and Morgan, K. Detection of anomalies in citrus leaves using laser induced breakdown spectroscopy (LIBS). 2015. Applied Spectroscopy, 69 (8): 913-919.
  • Sankaran, S., Khot, L.R., and Carter, A.H. 2015. Field-based crop phenotyping: multispectral aerial imaging for evaluation of winter wheat emergence and spring stand, Computers and Electronics in Agriculture, 118: 372-379.

2014

  • Liaghat, S., Mansor, S.B., Ehsani, R., Shaffri, H.Z.M., Meon, S., and Sankaran, S.  Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm. 2014. Computers and Electronics in Agriculture, 101, 48-54.
  • Liaghat, S., Mansor, S.B., Ehsani, R., Shaffri, H.Z.M., Meon, S., Sankaran, S., and Azam, S.H.M.N.  2014. Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms. International Journal of Remote Sensing, 35 (10), 3427-3439.

2013

  • Sankaran, S., and Ehsani, R. 2013. Detection of Huanglongbing-infected citrus leaves using statistical models with a fluorescence sensor. Applied Spectroscopy, 67 (4), 463-469.
  • Sankaran, S., Maja, J.M., Buchanon, S., Ehsani, R. 2013. Huanglongbing (citrus greening) detection using visible-near infrared and thermal imaging techniques. Sensors, 13: 2117-2130; DOI:http://dx.doi.org/10.3390/s130202117.
  • Garcia-Ruiz, Sankaran, S., Maja, J.M., Lee, W.S., Rasmussen, J., and Ehsani, R. 2013. Comparison of two aerial imaging platforms for identification of Huanglongbing infected citrus trees. Computers and Electronics in Agriculture, 91: 106-115.
  • Wetterich, C.B., Kumar, R., Sankaran, S., Belasque Jr., J., Ehsani, R., and Marcassa, L.G. 2013. A comparative study on application of computer vision and fluorescence imaging spectroscopy for detection of Huanglongbing citrus disease in USA and Brazil. Journal of Spectroscopy, 2013, Article ID 841738, DOI: http://dx.doi.org/10.1155/2013/841738.
  • Sankaran, S., and Ehsani, R. 2013. Comparison of visible-near infrared and mid-infrared spectroscopy for classification of Huanglongbing and citrus canker infected leaves. Agricultural Engineering International: CIGR Journal, 15 (3): 75-79.
  • Johnson, K., Sankaran, S., and Ehsani, R. 2013. Identification of water stress in citrus leaves using sensing technologies. Agronomy, 3 (4), 747-756.

2012

  • Sankaran, S., Ehsani, R., Inch, S.A., and Ploetz, R.C. 2012. Evaluation of visible-near infrared reflectance spectra of avocado leaves as a non-destructive sensing tool for detection of laurel wilt. Plant Disease, 96 (11): 1683-1689.
  • Sankaran, S., and Ehsani, R. 2012.  Detection of Huanglongbing disease in citrus using fluorescence spectroscopy. Transactions of the ASABE, 55 (1): 313-320.
  • Saeed, O.M.B., Sankaran, S., Shariff, A.R.M., Shariff, H.Z.M., Ehsani, R., Alfatni, M.S., and Hazir, M.H.M. 2012. Classification of oil palm fresh fruit bunches based on their maturity using portable four-band sensor system. Computers and Electronics in Agriculture, 82: 55-60.
  • Sankaran, S., Khot, L.R., and Panigrahi, S. 2012. Biology and applications of olfactory sensing system: A review. Sensors and Actuators B, 171-172: 1-17.
  • Panigrahi, S., Sankaran, S., Mallik, S., Gaddam, B., and Hanson, A. A. 2012. Olfactory receptor-based polypeptide sensor for acetic acid VOC detection.Material Science and Engineering C, 32: 1307-1313.
  • Khot, L.R., Sankaran, S., Maja, J.M., and Ehsani, R. 2012. Applications of nanomaterials in agricultural production and crop protection: a review. Crop Protection, 35: 64-70.
  • Sankaran, S., and Panigrahi, S. 2012. Investigation on ZnO-Fe2O3 based nanocomposite sensors for butanol detection related to food contamination.Journal of Nanoscience and Nanotechnology, 12: 2346-2352.

2011

  • Sankaran, S., and Panigrahi, S. 2011. Nanoparticulate zinc oxide chemoresistive sensor for volatile acetic acid detection. Nanoscience and Nanotechnology Letters, 3 (6): 755-762.
  • Sankaran, S., Panigrahi, S., and Mallik, S. 2011. Olfactory receptor based piezoelectric biosensors for detection of alcohols related to food safety applications. Sensors and Actuators B, 155 (1): 8-18.
  • Sankaran, S., Panigrahi, S., and Mallik, S. 2011. Odorant binding protein based biomimetic sensors for detection of alcohols associated with Salmonellacontamination in packaged beef. Biosensors and Bioelectronics, 26 (7): 3103-3109.
  • Sankaran, S., and Ehsani, R. 2011. Visible-near infrared spectroscopy based citrus greening detection: Evaluation of spectral feature extraction techniques.Crop Protection, 30 (11): 1508-1513.
  • Sankaran, S., Mishra, A., Maja, J.M., and Ehsani, R. 2011. Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards. Computers and Electronics in Agriculture, 77 (2): 127-134.

2010

  • Sankaran, S., Mishra, A., Ehsani, R., and Davis, C. 2010. A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture, 72 (1): 1-13.
  • Sankaran, S., Ehsani, R., and Etxeberria, E. 2010. Mid-infrared spectroscopy for detection of Huanglongbing (greening) in citrus leaves. Talanta, 83 (2): 574-581.
  • Sankaran, S., Khanal, S.M., Jasti, N., Jin, B., Pometto, A.L., and Van Leeuwen, J. 2010. Use of filamentous fungi for wastewater treatment and production of high value fungal byproducts: a review. Critical Reviews and Environmental Science and Technology, 40 (5): 400-449.

2004-2008

  • Sankaran, S., Khanal, S.M., Pometto, A.L., and Van Leeuwen, J. 2008. Ozone as a selective disinfectant for nonaseptic fungal cultivation on corn-processing wastewater. Bioresource Technology, 99 (17): 8265-8272.
  • Wichitsathian, B., Sankaran, S., Visvanathan, C., and Ahn, K. H. 2004. Biokinetic parameters as an indicator to ammonia toxicity in leachate treatment using membrane reactor. Asian Journal of Microbiology, Biotechnology and Environmental Science, 6 (1): 1-6.
  • Wichitsathian, B., Sankaran, S., Visvanathan, C., and Ahn, K. H. 2004. Landfill leachate treatment by yeast and bacteria based membrane bioreactor. Journal of Environmental Science and Health A., 39 (9): 2391-2404.