CV

Education

• B.S. in China, Huazhong Agricultural University, 2017
• Ph.D in China, Zhejiang University, 2022

Reasearch interests

• Smart agriculture
• Plant phenotyping
• Deep learning
• Hyperspectral/multispectral image processing

Work experience

• 2024 - current: Associate Professor at Shandong Agricultural University
• 2022 - 2024: Post-doctoral Associate at Purdue University
  • Duties included:
    • Exploring novel methods for processing hyperspectral images
    • Designing and managing greenhouse experiments and field experiments
    • Establishing data analysis models using deep learning for plant phenotyping applications
    • Writing scientific papers
  • Supervisor: Professor Jian Jin

Skills

• Scientific research skills:
  • Experimental design and implementation
  • Data analysis and interpretation
  • Use and maintenance of experimental equipment
  • Scientific literature evaluation
• Programming and data science skills:
  • Python programming
  • Deep learning and machine learning algorithms
  • Data visualization and cleaning
  • Version control (such as Git)
• Communication and writing skills:
  • Scientific writing and editing (papers, reports, etc.)
  • Oral reporting and presentations
  • Cross-disciplinary and team communication
• Critical and problem-solving thinking:
  • Analytical and critical thinking
  • Problem-solving and decision-making
• Continuous Learning and Self-improvement:
  • Staying up to date with the latest technology and research trends
  • Learning new tools and techniques

Publications

Identification of hybrid okra seeds based on near-infrared hyperspectral imaging technology

Zhang, Jinnuo, et al. "Identification of hybrid okra seeds based on near-infrared hyperspectral imaging technology." Applied Sciences 8.10 (2018): 1793.

Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning

Nie, Pengcheng, et al. "Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning." Sensors and Actuators B: Chemical 296 (2019): 126630.

Identification of bacterial blight resistant rice seeds using terahertz imaging and hyperspectral imaging combined with convolutional neural network

Zhang, Jinnuo, et al. "Identification of bacterial blight resistant rice seeds using terahertz imaging and hyperspectral imaging combined with convolutional neural network." Frontiers in Plant Science 11 (2020): 821.

Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning

Yang, Yong, et al. "Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning." RSC advances 10.72 (2020): 44149-44158.

Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning

Zhang, Jinnuo, et al. "Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning." Plant Methods 18.1 (2022): 1-16.

Visible and Near-Infrared Hyperspectral Diurnal Variation Calibration for Corn Phenotyping Using Remote Sensing

Zhang, Jinnuo, et al. "Visible and Near-Infrared Hyperspectral Diurnal Variation Calibration for Corn Phenotyping Using Remote Sensing." Remote Sensing 15.12 (2023): 3057.

Concise Cascade Methods for Transgenic Rice Seed Discrimination using Spectral Phenotyping

Zhang, Jinnuo, et al. "Concise Cascade Methods for Transgenic Rice Seed Discrimination using Spectral Phenotyping." Plant Phenomics 5 (2023): 0071.