Yang leads the Agricultural Robotics Lab. The lab's mission is to apply advanced ideas of robotics, remote sensing, data mining and information technology into precision agriculture. The core techniques we use include multispectral/hyperspectral imaging, spectroscopy, machine learning, geographic information system (GIS), digital mapping, biochemical sensing, etc. The tools available in the lab are unmanned aerial vehicle, unmanned ground vehicle, video camera, multispectral camera, hyperspectral camera, DGPS, and various electrical and optical sensors.

Yang's current research includes field corn nutrient deficiency detection; wheat scab and rust disease detection for high-throughput phenotyping; turf grass winter damage prediction; industrial hemp health monitoring in greenhouse; estimation of soil carbon sequestration in perennial crop fields.

Yang Research Lab

Visit Ce Yang's Experts@Minnesota profile page

Yang received her Ph.D. degree in the area of agricultural engineering from the University of Florida in 2013. She also holds a M.S. degree in computer science to facilitate her interdisciplinary research.

Education

Currently Teaching Courses

• BBE2003
• BBE4023W/5023
• LAAS 5416

Honors and Awards

• 2018 Nigon, T., Mulla, D. and Yang, C., Agronomy and Crops International Annual Meetings. Best Poster Award
• 2017 Moghimi, A., Yang, C. and Marchetto, P., SPIE ‘Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II. Second Best Paper Award
• 2017 Nigon, T., Kaiser, D., Yang, C., and Mulla, D., North Central Extension-Industry Soil Fertility Conference. Second Best Poster Award
• 2017 Nigon, T., Laacouri, A., Yang, C., and Mulla, D., Agronomy, Crops, and Soils International Annual Meetings. Third Best Poster Award

Selected Publications

• Su, W.-H.; Zhang, J.; Yang, C.*; Page, R.; Szinyei, T.; Hirsch, C.D.; Steffenson, B.J. Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision. Remote Sens. 2021, 13, 26.
• Mahdavian, A., Minaei, S.*, Marchetto, P.M., Almasganj, F., Rahimi, S. and Yang, C., 2021. Acoustic features of vocalization signal in poultry health monitoring. Applied Acoustics, 175, p.107756.
• Xie, C. and Yang, C.*, 2020. A review on plant high-throughput phenotyping traits using UAV-based sensors. Computers and Electronics in Agriculture, 178, p.105731.
• Wen-Hao Su, Ce Yang*, Yanhong Dong, Ryan Johnson, Rae Page, Tamas Szinyei, Cory D. Hirsch, Brian J. Steffenson, 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, Volume 343, 128507, ISSN 0308-8146, https://doi.org/10.1016/j.foodchem.2020.128507.
• Tyler J. Nigon, Ce Yang*, Gabriel Dias Paiao, David J. Mulla, Joseph F. Knight and Fabián G. Fernández, 2020. Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery. Remote Sens. 12, 1234; doi:10.3390/rs12081234
• Moghimi, A., Yang, C.* and Anderson, J.A., 2020. Aerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat. Computers and Electronics in Agriculture, 172, p.105299.
• Mahdavian, A.*, Minaei, S., Yang, C., Almasganj, F., Rahimi, S. and Marchetto, P.M., 2020. Ability evaluation of a voice activity detection algorithm in bioacoustics: A case study on poultry calls. Computers and Electronics in Agriculture, 168, p.105100.
• Nigon, T.J., Yang, C.*, Mulla, D.J. and Kaiser, D.E. "Computing uncertainty in the optimum nitrogen rate using a generalized cost function." Computers and Electronics in Agriculture (2019), 167, p.105030.
• Qiu, R., Yang, C.*, Moghimi, A., Zhang, M., Steffenson, B.J. and Hirsch, C.D., 2019. Detection of Fusarium Head Blight in Wheat Using a Deep Neural Network and Color Imaging. Remote Sensing, 11(22), p.2658.