Research Interests
High-throughput Phenotyping; Precision Agriculture; UAV; Image processing; Machine learning
Publications
- Zhou, J., Sarkar, S., Vieira, C. C., Tian, F., Zhou, J., & Chen, P. (2021). Classification of soybean tolerance to off-target Dicamba damage using UAV-based imagery and deep learning methods. Manuscript under preparation.
- Vieira, C. C., Zhou, J., Usovsky, M., Vuong, T., Howland, A. D., Lee, D., . . . Chen, P. (2021). Exploring Machine Learning Algorithms to Unveil Soybean Genomic Regions Associated with Resistance to Southern Root-knot Nematode. Manuscript submitted for publication.
- Zhou, J., Beche, E., Vieira, C. C., Yungbluth, D., Zhou, J., Scaboo, A., & Chen, P. (2021). Improve soybean variety selection accuracy using UAV-based high-throughput phenotyping technology. Manuscript accepted for publication.
- Zhou, J., Mou, H., Zhou, J., Ali, M. L., Ye, H., Chen, P., & Nguyen, H. T. (2021). Qualification of soybean responses to flooding stress using UAV-based imagery and deep learning. Plant Phenomics, 2021, 9892570. https://doi.org/10.34133/2021/9892570
- Zhou, S., Mou, H., Zhou, J., Zhou, J., Ye, H., & Nguyen, H. T. (2021). Development of an automated plant phenotyping system for evaluation of salt tolerance in soybean. Computers and Electronics in Agriculture, 182, 106001. https://doi.org/10.1016/j.compag.2021.106001
- Zhou, J., Zhou, J., Ye, H., Ali, M. L., Chen, P., & Nguyen, H. T. (2021). Yield estimation of soybean breeding lines under drought stress using unmanned aerial vehicle-based imagery and convolutional neural network. Biosystems Engineering, 204, 90-103.
- Zhou, J., Zhou, J., Ye, H., Ali, M. L., Nguyen, H. T., & Chen, P. (2020). Classification of soybean leaf wilting due to drought stress using UAV-based imagery. Computers and Electronics in Agriculture, 175, 105576. https://doi.org/10.1016/j.compag.2020.105576
- Zhou, J., Yungbluth, D., Vong, C. N., Scaboo, A., & Zhou, J. (2019). Estimation of the maturity date of soybean breeding lines using UAV-based multispectral imagery. Remote Sensing 11(18): 2075. https://doi.org/10.3390/rs11182075
- Cao, W., Zhou, J., Yuan, Y., Ye, H., Nguyen, H. T., Chen, J., & Zhou, J. (2019). Quantifying variation in soybean due to flood using a low-cost 3D imaging system. Sensors, 19(12), 2682. https://doi.org/10.3390/s19122682
- Zhou, J., Fu, X., Zhou, S., Zhou, J., Ye, H., & Nguyen, H. T. (2019). Automated segmentation of soybean plants from 3D point cloud using machine learning. Computers and Electronics in Agriculture, 162, 143-153. https://doi.org/10.1016/j.compag.2019.04.014
- Zhou, J., Fu, X., Schumacher, L., & Zhou, J. (2018). Evaluating geometric measurement accuracy based on 3D reconstruction of automated imagery in a greenhouse. Sensors, 18(7), 2270. https://doi.org/10.3390/s18072270
- Zhou, J., Chen, H., Zhou, J., Fu, X., Ye, H., & Nguyen, H. T. (2018). Development of an automated phenotyping platform for quantifying soybean dynamic responses to salinity stress in greenhouse environment. Computers and Electronics in Agriculture, 151, 319-330. https://doi.org/10.1016/j.compag.2018.06.016