Professor

교수 소개

스마트팜 AI 및 정밀농업 시스템 연구를 선도하는 AGIS 지도교수를 소개합니다.

Dae-Hyun Jung, Ph.D.

Assistant Professor

E-mail daehyun@khu.ac.kr

학교 Kyung Hee University, Department of Smart Farm Science

위치 College of Life Sciences Building #406

Tel 031-201-2657

정대현 조교수

Education

  • 2020

    Ph.D. Seoul National University, College of Agriculture and Life Sciences, Bio-systems Engineering (August 2020)

  • 2014

    M.S. Seoul National University, College of Agriculture and Life Sciences, Bio-systems Engineering (August 2014)

  • 2012

    B.S. Chungbuk National University, College of Agriculture and Life Sciences, Bio-systems Engineering (August 2012)

Professional Experience

  • Sep 2022 - Present Professor, Kyung Hee University, Department of Smart Farm Science
  • 2020 - 2022 Postdoctoral Researcher, Smart Farm Convergence Research Center, Korea Institute of Science and Technology

Areas of Expertise

  • Research on smart agricultural machinery and automation robots
  • Development of AI-based control systems for complex smart greenhouse environments
  • Research on precision sensing for crop growth and life monitoring
  • Development of big data collection and analysis platforms for smart farms

Major Publication

  1. Jung, D. H., Kim, H. Y., Won, J. H., & Park, S. H. (2023). Development of a classification model for Cynanchum wilfordii and Cynanchum auriculatum using convolutional neural network and local interpretable model-agnostic explanation technology. Frontiers in Plant Science, 14, Article 1169709. https://doi.org/10.3389/fpls.2023.1169709
  2. Jung, D. H., Lee, T. S., Kim, K. G., & Park, S. H. (2022). A Deep Learning Model to Predict Evapotranspiration and Relative Humidity for Moisture Control in Tomato Greenhouses. Agronomy, 12(9), Article 2169. https://doi.org/10.3390/agronomy12092169
  3. Jung, D. H., Kim, J. D., Kim, H. Y., Lee, T. S., Kim, H. S., & Park, S. H. (2022). A Hyperspectral Data 3D Convolutional Neural Network Classification Model for Diagnosis of Gray Mold Disease in Strawberry Leaves. Frontiers in Plant Science, 13, Article 837020. https://doi.org/10.3389/fpls.2022.837020
  4. Jung, D. H., Kim, C. Y., Lee, T. S., & Park, S. H. (2022). Depth image conversion model based on CycleGAN for growing tomato truss identification. Plant Methods, 18(1), Article 83. https://doi.org/10.1186/s13007-022-00911-0
  5. Jung, D. H., Kim, N. Y., Moon, S. H., Kim, H. S., Lee, T. S., Yang, J. S., Lee, J. Y., Han, X., & Park, S. H. (2021). Classification of Vocalization Recordings of Laying Hens and Cattle Using Convolutional Neural Network Models. Journal of Biosystems Engineering, 46(3), 217-224. https://doi.org/10.1007/s42853-021-00101-1
  6. Jung, D. H., Kim, N. Y., Moon, S. H., Jhin, C., Kim, H. J., Yang, J. S., Kim, H. S., Lee, T. S., Lee, J. Y., & Park, S. H. (2021). Deep learning-based cattle vocal classification model and real-time livestock monitoring system with noise filtering. Animals, 11(2), 1-16, Article 357. https://doi.org/10.3390/ani11020357
  7. Jung, D. H., Kim, H. J., Kim, J. Y., Park, S. H., & Cho, W. J. (2021). Water nitrate remote monitoring system with self-diagnostic function for ion-selective electrodes. Sensors, 21(8), Article 2703. https://doi.org/10.3390/s21082703
  8. Jung, D. H., Kim, H. J., Kim, J. Y., Lee, T. S., & Park, S. H. (2020). Design optimization of proportional plus derivative band parameters used in greenhouse ventilation by response surface methodology. Korean Journal of Horticultural Science and Technology, 38(2), 187-200. https://doi.org/10.7235/HORT.20200018
  9. Jung, D. H., Kim, H. J., Kim, J. Y., Lee, T. S., & Park, S. H. (2020). Model predictive control via output feedback neural network for improved multi-window greenhouse ventilation control. Sensors, 20(6), Article 1756. https://doi.org/10.3390/s20061756
  10. Jung, D. H., Kim, H. S., Jhin, C., Kim, H. J., & Park, S. H. (2020). Time-serial analysis of deep neural network models for prediction of climatic conditions inside a greenhouse. Computers and Electronics in Agriculture, 173, Article 105402. https://doi.org/10.1016/j.compag.2020.105402
  11. Jung, D. H., Park, S. H., Kim, H. J., Jhin, C., & Lee, T. S. (2019). Deep neural network based control algorithm for maintaining electrical conductivity and water content of substrate in closed-soilless cultivation. Paper presented at 2019 ASABE Annual International Meeting, Boston, United States. https://doi.org/10.13031/aim.201900951
  12. Jung, D. H., Kim, H. J., Kim, H. S., Choi, J., Kim, J. D., & Park, S. H. (2019). Fusion of spectroscopy and cobalt electrochemistry data for estimating phosphate concentration in hydroponic solution. Sensors, 19(11), Article 2596. https://doi.org/10.3390/s19112596
  13. Jung, D. H., Kim, H. J., Cho, W. J., Park, S. H., & Yang, S. H. (2019). Validation testing of an ion-specific sensing and control system for precision hydroponic macronutrient management. Computers and Electronics in Agriculture, 156, 660-668. https://doi.org/10.1016/j.compag.2018.12.025
  14. Jung, D. H., Kim, H. J., Choi, G. L., & Kang, C. I. (2014). Closed lettuce cultivation system using an array of ion-selective electrodes. In American Society of Agricultural and Biological Engineers Annual International Meeting 2014, ASABE 2014 (pp. 4323-4329). American Society of Agricultural and Biological Engineers.
  15. Jung, D. H., Kim, H. J., Kim, W. K., Choi, G. L., & Kang, C. I. (2014). Real-time control of hydroponic macronutrients for closed growing system. In International Symposium on New Technologies for Environment Control, Energy-Saving and Crop Production in Greenhouse and Plant Factory - Greensys 2013 (pp. 657-662). Acta Horticulturae, 1037.
  16. Jung, D. H., Kim, H. J., Kim, W. K., Kang, C. I., & Choi, G. L. (2013). Automated sensing and control of hydroponic macronutrients using a computer-controlled system. In American Society of Agricultural and Biological Engineers Annual International Meeting 2013, ASABE 2013 (pp. 1479-1484). American Society of Agricultural and Biological Engineers.