Lirong Xiang
Assistant Professor
- 919.515.6740
- lxiang3@ncsu.edu
- 151 Weaver Labs
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Dr. Xiang is the Principal Investigator of the Automation and Robotics Lab in the Biological and Agricultural Engineering Department at North Carolina State University and affiliated to N.C. Plant Sciences Initiative. She received her Ph.D. degree in Agricultural and Biosystems Engineering from Iowa State University and her B.S. degree in Biosystems Engineering from Zhejiang University. Dr. Xiang works on agricultural robotics, 2D & 3D computer vision, and machine learning. During her Ph.D. program, she has developed robotic and automated systems for both indoor and in-field plant phenotyping applications. Dr. Xiang joined BAE in August 2022.
Education
Biosystems Engineering
Zhejiang University, China
Agricultural and Biosystems Engineering
Iowa State University, USA
Research Description
Dr. Xiang's research mainly focuses on developing smart cyber-physical systems that integrate cutting-edge robotics, machine vision, and machine learning technologies to automate labor-intensive tasks in agricultural systems. The research topics include but are not limited to: developing robotic platforms for weeding, transplanting, and selective harvesting; adopting AI and robotics tools for precision livestock management; and combining aerial and ground robots for in-situ and non-invasive crop sensing.
Publications
- Technical note: ShinyAnimalCV: open-source cloud-based web application for object detection, segmentation, and three-dimensional visualization of animals using computer vision
- Wang, J., Hu, Y., Xiang, L., Morota, G., Brooks, S. A., Wickens, C. L., … Yu, H. (2024), JOURNAL OF ANIMAL SCIENCE, 102. https://doi.org/10.1093/jas/skad416
- A review of three-dimensional vision techniques in food and agriculture applications
- Xiang, L., & Wang, D. (2023). [Review of , ]. SMART AGRICULTURAL TECHNOLOGY, 5. https://doi.org/10.1016/j.atech.2023.100259
- Early Detection of Rice Blast Using a Semi-Supervised Contrastive Unpaired Translation Iterative Network Based on UAV Images
- Lin, S., Li, J., Huang, D., Cheng, Z., Xiang, L., Ye, D., & Weng, H. (2023), PLANTS-BASEL, 12(21). https://doi.org/10.3390/plants12213675
- Field-based robotic leaf angle detection and characterization of maize plants using stereo vision and deep convolutional neural networks
- Xiang, L., Gai, J., Bao, Y., Yu, J., Schnable, P. S. S., & Tang, L. (2023, February 27), JOURNAL OF FIELD ROBOTICS. https://doi.org/10.1002/rob.22166
- Shinyanimalcv: Interactive Web Application for Object Detection and Three-Dimensional Visualization of Animals Using Computer Vision
- Wang, J., Xiang, L., Morota, G., Wickens, C., Cushon, E., Brooks, S., & Yu, H. (2023, November 6), JOURNAL OF ANIMAL SCIENCE, Vol. 101, pp. 244–245. https://doi.org/10.1093/jas/skad281.294
- Spectroscopic determination of chlorophyll content in sugarcane leaves for drought stress detection
- Gai, J., Wang, J., Xie, S., Xiang, L., & Wang, Z. (2023, November 13), PRECISION AGRICULTURE, Vol. 11. https://doi.org/10.1007/s11119-023-10082-0
- Synchronously Predicting Tea Polyphenol and Epigallocatechin Gallate in Tea Leaves Using Fourier Transform-Near-Infrared Spectroscopy and Machine Learning
- Ye, S., Weng, H., Xiang, L., Jia, L., & Xu, J. (2023), MOLECULES, 28(14). https://doi.org/10.3390/molecules28145379
- Detection and characterization of maize plant architectural traits in the field using stereo vision and deep convolutional neural networks
- Xiang, L., Liu, X., Raj, A., & Tang, L. (2022), 2022 ASABE Annual International Meeting. Presented at the 2022 ASABE Annual International Meeting, Houston, TX.
- In-field soybean seed pod phenotyping on harvest stocks using 3D imaging and deep learning
- Liu, X., Xiang, L., Raj, A., & Tang, L. (2022), 2022 ASABE Annual International Meeting. Presented at the 2022 ASABE Annual International Meeting, Houston, TX.
- Robotic Field-based Plant Architectural Traits Characterization Using Stereo Vision and Deep Neural Networks
- Xiang, L., Liu, X., Raj, A., Yu, J., Schnable, P. S., & Tang, L. (2022), Fourth International Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS2022). Presented at the Fourth International Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS2022), Ames, IA.