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Jingye Han

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I am a postdoctoral of AIN group in Wageningen University & Research.
Email: jingye.han@wur.nl
Research Interest: crop model, phenotyping, deep learning, remote sensing

Education

  • 2017.9-2024.6, Water Resources and Hydropower Engineering, Wuhan University. MSc& PhD.
  • 2022.5-2023.9, Laboratory of Geo-information Science and Remote Sensing , Wageningen University & Research. Guest PhD.
  • 2013.6-2017.9 , Agricultural Water Conservancy Engineering, Wuhan University. BSc.

Skills

  • Agro-hydrology Modelling
    Experienced ORYZA, SWAP and DSSAT model user with an in-depth knowledge of the plant and soil water processes represented in the model
  • Data Assimilation Frameworks
    Experienced Ensemble Kalman Filter (EnKF) user and implementation of EnKF algorithm with the ORYZA2000 model
  • Deep Learning
    Experienced DL user (packages: Pytorch, Tensorflow, & Caffe) with an in-depth knowledge of the algorithms for Image data (i.e., RGB image) and time-series data (i.e., soil water)
  • Programming
    Proficient in Python, MATLAB and Fortran

Publication

Accepted

  1. Han, J., Shi, L., Yang, Q., Chen, Z., Jin, Y., Zha, Y. (2022). Rice yield estimation using a CNN-based image-driven data assimilation framework. Field Crops Research, 288.
  2. Han, J., Shi, L., Yang, Q., Huang, K., Zha, Y., Yu, J., (2021). Real-time detection of rice phenology through convolutional neural network using handheld camera images Precision Agriculture. 22, 154–178.
  3. Han, J., Shi, L., Pylianidis, C., Yang, Q., & Athanasiadis, I. N. (2023). Deeporyza: a knowledge guided machine learning model for rice growth simulation In 2nd AAAI Workshop on AI for Agriculture and Food Systems.
  4. Yang Q, Shi L, Han J., et al. A VI-based phenology adaptation approach for rice crop monitoring using UAV multispectral images[J]. Field Crops Research, 2022, 277: 108419.
  5. Yang, Q., Shi, L., Han, J., Yu, J., & Huang, K. (2020). A near real-time deep learning approach for detecting rice phenology based on UAV images. Agricultural and Forest Meteorology, 287, 107938.
  6. Yang, Q., Shi, L., Han, J., Zha, Y., & Zhu, P. (2019). Deep convolutional neural networks for rice grain yield estimation at the ripening stage using UAV-based remotely sensed images. Field Crops Research, 235, 142-153
  7. Yu J, Shi L, Han J, et al. Assessing parametric and nitrogen fertilizer input uncertainties in the ORYZA_V3 model predictions[J]. Agronomy Journal, 2021, 113(6): 4965-4981.

In preparing

  1. Knowledge-guided machine learning with multivariate sparse data for crop growth modelling

Project

  1. Jiahe Assistant APP
  2. Field level yield prediction

Tools

  • Easy weather: Get weather data as the input of crop grow model.
  • Easy DOY: Transfer the date to day of year.
  • Easy NDVI: Get time-series NDVI data from Landsat.
  • Rice Diagnose: Use CNN to extract rice growth information from near-surface RGB image.

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