Yeyin Shi

Yeyin Shi

Contact Information:

East Campus (Lincoln)
213 L.W. Chase Hall
402-472-2140
yshi18@unl.edu

Associate Professor and Agricultural Intelligence Engineer

Academic Degrees

  • Ph.D., Biosystems and Agricultural Engineering, Oklahoma State University, May 2014
  • M.S., Biosystems and Agricultural Engineering, Oklahoma State University, December 2010
  • B.S., Mechanical Engineering, Nanjing Forestry University, China, July 2007

Certifications

  • PhD

Research Opportunities (Graduate/Undergraduate)

Appointment

  • 50% research
  • 50% teaching

Areas of Research and Professional Interest

  • Agricultural information/data generation, analysis, modeling and management
  • Agricultural remote sensing systems (satellite, aircraft, and UAS/drone based platforms)
  • Crop abiotic and biotic stresses sensing and modeling
  • Field-based high-throughput phenotyping
  • Precision crop and livestock management

      Google Scholar page

Courses Taught

  • AGST 316 Technologies and Techniques for Digital Agriculture - Spring, 3 hrs Lecture + Lab
  • AGEN/AGRO/AGST 431/892 Site-Specific Crop Management - Fall, 3 hrs Lecture + Lab (Teaching Assistants needed!)

About Yeyin Shi

Yeyin’s research group at UNL focuses on applying artificial intelligence in agricultural applications for improved productivity and sustainability. More details about the group are coming soon. Yeyin is currently looking for talented students with passion and determination and active collaborators to work together.

Honors and Awards

  • ASABE Outstanding Manuscript Reviewer for the 2014 publication year, Information Technology, Sensors, & Control Systems Division, April 2015.
  • 1st Place in the postdoc category, 4th Annual Poster Research Gallery and Competition, Citrus Research and Education Center, University of Florida, April 2015.
  • 2nd Place in Student Robotic Competition (team), 2012 ASABE Annual International Meeting. 2012.
  • Precision Agriculture Outstanding Graduate Student Award 2010, 10th International Conference on
    Precision Agriculture Awards Committee, 2010.
  • Sitlington Enriched Graduate Scholarship, Division of Agricultural Sciences and Natural Resources,
    Oklahoma State University, 2010.
  • Alpha Epsilon, Honor Society for Outstanding Biological and Agricultural Engineers, 2008

Selected Publications

  • de Castro, A.I., Shi, Y., Maja, J.M., Peña, J.M. (2021). UAVs for vegetation monitoring: overview and recent scientific contributions. Remote Sensing, accepted.
  • Wang, L., Zhou, Y., Hu, Q., Tang, Z., Ge, Y., Smith, A., ... & Shi, Y.* (2021). Early Detection of Encroaching Woody Juniperus virginiana and Its Classification in Multi-Species Forest Using UAS Imagery and Semantic Segmentation Algorithms. Remote Sensing, 13(10), 1975.
  • Yu, C., Shen, S., Zhang, K., Zhao, H., & Shi, Y. (2021). Energy-driven resource allocation for joint federated learning in Internet of Agriculture Things. IoT and Sensor Networks Symposium. IEEE GLOBECOM 2021.
  • Alzadjali, A., A. N., Alali, Veeranampalayam-Sivakumar, M. H., Deogun, J. S., Scott, S., Schnable, J. C., & Shi, Y.* (2021). Maize Tassel Detection from UAV Imagery Using Deep Learning. Frontiers in Robotics and AI, 8, 136.
  • Shi, Y., Qiu, G., Wang, N. Book Chapter: Basic Microcontroller Use for Measurement and Control. Holden, N. M., Wolfe, M. L., Ogejo, J. A., and E. J. Cummins. (2021) Introduction to Biosystems Engineering, ASABE and Virginia Tech Publishing. DOI: https://doi.org/10.21061/IntroBiosystemsEngineering, CC BY 4.0, https://creativecommons.org/licenses/by/4.0
  • Sankaran, S.*, Marzougui, A., Hurst, J.P., Zhang, C., Schnable, J.C., 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. (in press). (DOI: 10.13031/trans.14197)
  • Zhao, B., Li, J., Baenziger, P.S., Belamkar, V., Ge, Y., Zhang, J., & Shi, Y.* (2020). Automatic wheat lodging detection and mapping in aerial imagery to support high-throughput phenotyping and in-season crop management. Agronomy, 10, 1762. (DOI: 10.3390/agronomy10111762)
  • Pang, Y., Shi, Y., Gao, S., Jiang, F., Veeranampalayam-Sivakumar, A.N., Thompson, L., Luck, J., & Liu, C. (2020). Improved crop row detection with deep neural network for early-season maize stand count in UAV imagery. Computers and Electronics in Agriculture, 178, 105766. (DOI: https://doi.org/10.1016/j.compag.2020.105766)
  • Zhang, J., Wang, C., Yang, C., Jiang, Z., Zhou, G., Wang, B., Shi, Y., Zhang, D., You, L., & Xie, J. (2020). Evaluation of a UAV-mounted consumer grade camera with different spectral modifications and two handheld spectral sensors for rapeseed growth monitoring: Performance and influencing factors. Precision Agriculture, 1-29. (DOI: https://doi.org/10.1007/s11119-020-09710-w)
  • Deng, X., Thomasson, J. A., Pugh, N. A., Chen, J., Rooney, W. L., Brewer, M. J., & Shi, Y. * (2020). Estimating the severity of sugarcane aphids infestation on sorghum with machine vision. International Journal of Precision Agricultural Aviation, 3(2). (DOI: 10.33440/j.ijpaa.20200302.89)
  • Veeranampalayam Sivakumar, A. N. V., Li, J., Scott, S., Psota, E., J Jhala, A., Luck, J. D., & Shi, Y. * (2020). Comparison of Object Detection and Patch-Based Classification Deep Learning Models on Mid-to Late-Season Weed Detection in UAV Imagery. Remote Sensing, 12(13), 2136. (DOI: 10.3390/rs12132136)
  • Li, J., Oswald, C., Graef, G., & Shi, Y. * (2020). Improving model robustness for soybean iron deficiency chlorosis rating by unsupervised pre-training on unmanned aircraft system derived images. Computers and Electronics in Agriculture, 175, 105557. (DOI: 10.1016/j.compag.2020.105557)
  • Zhang, J., Zhao, B., Yang, C., Shi, Y., Liao, Q., Zhou, G., Wang, C., Xie, T., Jiang, Z., Zhang, D. & Yang, W. (2020). Rapeseed stand count estimation at leaf development stages with UAV imagery and convolutional neural networks. Frontiers in Plant Science, 11, 617. (DOI: https://doi.org/10.3389/fpls.2020.00617)
  • Li, J., Veeranampalayam-Sivakumar, A.N., Bhatta, M., Garst, N.D., Stoll, H., Baenziger, P.S., Belamkar, V., Howard, R., Ge, Y., & Shi, Y. * (2019). Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery. Plant Methods, 15(1), 123. (DOI: https://doi.org/10.1186/s13007-019-0508-7)
  • Guo, S., Li, J., Yao, W., Zhan, Y., Li, Y., Shi, Y. * (2019). Distribution characteristics on droplet deposition of wind field vortex formed by multi-rotor UAV. PloS ONE, 14(7): e0220024. (DOI: https://doi.org/10.1371/journal.pone.0220024)
  • Li, J., Shi, Y., Lan, Y., & Guo, S. (2019). Vertical distribution and vortex structure of rotor wind field under the influence of rice canopy. Computers and Electronics in Agriculture, 159, 140-146. (DOI: https://doi.org/10.1016/j.compag.2019.02.027)
  • Yuan, W., Li, J., Bhatta, M., Shi, Y., Baenziger, P., & Ge, Y. (2018). Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS. Sensors, 18(11), 3731. (DOI: https://doi.org/10.3390/s18113731)
  • Li, J., Shi, Y. *, Veeranampalayam-Sivakumar, A. N., & Schachtman, D. P. (2018). Elucidating sorghum biomass, nitrogen and chlorophyll contents with spectral and morphological traits derived from unmanned aircraft system. Frontiers in Plant Science, 9, 1406. (DOI: 10.3389/fpls.2018.01406)
  • Zhao, B., Zhang, J., Yang, C., Zhou, G., Ding, Y., Shi, Y., Zhang, D., Xie, J., & Liao, Q. (2018). Rapeseed seedling stand counting and seeding performance evaluation at two early growth stages based on unmanned aerial vehicle imagery. Frontiers in Plant Science, 9, 1362. (DOI: https://doi.org/10.3389/fpls.2018.01362)
  • Chen, D., Shi, Y., Huang, W., Zhang, J., & Wu, K. (2018). Mapping wheat rust based on high spatial resolution satellite imagery. Computers and Electronics in Agriculture, 152, 109-116. (DOI: https://doi.org/10.1016/j.compag.2018.07.002)
  • Shafian, S., Rajan, N., Schnell, R., Bagavathiannan, M., Valasek, J., Shi, Y., & Olsenholler, J. (2018). Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development. PloS one, 13(5), e0196605. (DOI: https://doi.org/10.1371/journal.pone.0196605)
  • Zhao, X., Zhang, J., Yang, C., Song, H., Shi, Y., Zhou, X., Zhang, D., & Zhang, G. (2018). Registration for Optical Multimodal Remote Sensing Images Based on FAST Detection, Window Selection, and Histogram Specification. Remote Sensing, 10(5). (DOI: https://doi.org/10.3390/rs10050663)
  • Lu, J., Ehsani, R., Shi, Y., Castro, A. I., & Wang, S. (2018). Detection of multi-tomato leaf diseases (late blight, target and bacterial spots) in different stages by using a spectral-based sensor. Scientific reports, 8(1), 2793. (DOI: https://doi.org/10.1038/s41598-018-21191-6)
  • Zhang, J., Yang, C., Zhao, B., Song, H., Clint Hoffmann, W., Shi, Y., Zhang, D., & Zhang, G. (2017). Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras. Remote Sensing, 9(10), 1054. (DOI: https://doi.org/10.3390/rs9101054)
  • Lu, J., Ehsani, R., Shi, Y., Abdulridha, J., de Castro, A. I., & Xu, Y. (2017). Field detection of anthracnose crown rot in strawberry using spectroscopy technology. Computers and Electronics in Agriculture, 135, 289-299. (DOI: https://doi.org/10.1016/j.compag.2017.01.017)
  • Shi, Y., Thomasson, J. A., Murray, S. C., Pugh, N. A., Rooney, W. L., Shafian, S., ... & Rana, A. (2016). Unmanned aerial vehicles for high-throughput phenotyping and agronomic research. PloS one, 11(7), e0159781.
  • Defterli, S. G., Shi, Y., Xu, Y., & Ehsani, R. (2016). Review of robotic technology for strawberry production. Applied Engineering in Agriculture, 32(3), 301-318.
  • Navid, H., Taylor, R. K., Yazgi, A., Wang, N., Shi, Y., & Weckler, P. (2015). Detecting Grain Flow Rate Using a Laser Scanner. Transactions of the ASABE, 58(5), 1185-1190.
  • Shi, Y., Wang, N., Taylor, R. K., & Raun, W. R. (2015). Improvement of a ground-LiDAR-based corn plant population and spacing measurement system. Computers and Electronics in Agriculture, 112, 92-101.
  • Yuan, L., Zhang, J., Shi, Y., Nie, C., Wei, L., & Wang, J. (2014). Damage mapping of powdery mildew in winter wheat with high-resolution satellite image. Remote sensing, 6(5), 3611-3623.
  • Shi, Y., Wang, N., Taylor, R. K., Raun, W. R., & Hardin, J. A. (2013). Automatic corn plant location and spacing measurement using laser line-scan technique. Precision Agriculture, 14(5), 478-494.