WU Na
Education Background
University of Science and Technology of China , Doctor of Control Science and Engineering, Sep. 2012--Apr. 2018
Work Background
1. May .2018--Aug. 2022 Zhejiang University, Agricultural Engineering(Post-doctor)
2. Sep. 2022--Present Zhejiang University of Science and Technology, Electronic Information Engineering(Master Tutor)
Email:nwu@zust.edu.cn
Main Research Interests
Plant Phenotyping; UAV Remote Sensing; Multimodal learning; Computer Vision; AI+Agriculture
Main Research Projects
Young Scientists Fund Project of the National Natural Science Foundation of China(32401708)
Main Published Papers
1. Na Wu; Pan Gao; Jie Wu, et. Al; Rapid detection and visualization of physiological signatures in cotton leaves under Verticillium wilt stress, Artificial Intelligence in Agriculture, 2025, 15: 757-769
2. Na Wu; Jie Wu, Zhecheng Wang, et. al; Maturity detection and counting of blueberries in real orchards using a novel STF-YOLO model integrated with ByteTrack algorithm, Frontiers in Plant Science, 2025, 16:1682024
3. Na Wu; Shizhuang Weng; Qinlin Xiao, et. al; Rapid and accurate identification of bakanae pathogens carried by rice seeds based on hyperspectral imaging and deep transfer learning, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2024, 311: 123889
4. Na Wu; Shizhuang Weng; Jinxin Chen, et. al; Deep convolution neural network with weighted loss to detect rice seeds vigor based on hyperspectral imaging under the sample-imbalanced condition, Computers and Electronics in Agriculture, 2022, 196: 106850
5. Qinlin Xiao; Wentan Tang; Chu Zhang; et. al; Na Wu*;Spectral preprocessing combined with deep transfer learning to evaluate chlorophyll content in cotton leaves, Plant Phenomics, 2022: 9813841
6. Na Wu; Fei Liu; Fanjia Meng, et. al; Rapid and accurate varieties classification of different crop seeds under sample-limited condition based on hyperspectral imaging and deep transfer learning, Frontiers in Bioengineering and Biotechnology, 2021, 9: 696292
7. Na Wu#; Hubiao Jiang#; Yidan Bao, et. al ; Practicability investigation of using near-infrared hyperspectral imaging to detect rice kernels infected with rice false smut in different conditions, Sensors and Actuators B: Chemical, 2020, 308: 127696
