2025

Jeon, Y.-J., Hong, S., Lee, T. S., Park, S. H., Song, G., Seo, M.-G., Lee, J., Lim, Y., An, J.-T., Lee, S., Jeong, H.-Y., Park, S. J., Lee, C., Jung, D.-H., & Kwon, C.-T. (2025). Volumetric Deep Learning-Based Precision Phenotyping of Gene-Edited Tomato for Vertical Farming. Plant Phenomics, 7(3), 100095. https://doi.org/10.1016/j.plaphe.2025.100095

Park, S. J., Lee, H., Jeon, Y.-J., Woo, D. H., Kim, H.-Y., Kim, J.-O., & Jung, D.-H. (2025). Development of an RGB-GE Data Generation and XAI-Based On-Site Classification System for Differentiating Zizyphus jujuba and Zizyphus mauritiana in Herbal Medicine Applications. Agriculture, 15(10), 1022. https://doi.org/10.3390/agriculture15101022

Jeon, Y.-J., Hong, M. J., Ko, C. S., Park, S. J., Lee, H., Lee, W.-G., & Jung, D.-H. (2025). A hybrid CNN-Transformer model for identification of wheat varieties and growth stages using high-throughput phenotyping. Computers and Electronics in Agriculture, 230, 109882. https://doi.org/10.1016/j.compag.2024.109882

Jeon, Y., Lee, S., Jeon, Y.-J., Kim, D., Ham, J.-H., Jung, D.-H., Kim, H.-Y., & You, J. (2025). Rapid identification of pathogenic bacteria using data preprocessing and machine learning-augmented label-free surface-enhanced Raman scattering. Sensors and Actuators B: Chemical, 425, 136963. https://doi.org/10.1016/j.snb.2024.136963

Kim, E., Yang, S.-M., Ham, J.-H., Lee, W., Jung, D.-H., & Kim, H.-Y. (2025). Integration of MALDI-TOF MS and machine learning to classify enterococci: A comparative analysis of supervised learning algorithms for species prediction. Food Chemistry, 462, 140931. https://doi.org/10.1016/j.foodchem.2024.140931

2024

Jeon, Y.-J.; Kim, J.Y.; Hwang, K.-S.; Cho, W.-J.; Kim, H.-J.; Jung, D.-H.(2024). Machine Learning-Powered Forecasting of Climate Conditions in Smart Greenhouse Containing Netted Melons. Agronomy 2024, 14, 1070. https://doi.org/10.3390/agronomy14051070 

Jeon, Y.-J., Park, S. J., Lee, H., Kim, H.-Y., & Jung, D.-H. (2024). Deep Learning-Based Model for Effective Classification of Ziziphus jujuba Using RGB Images. AgriEngineering, 6(4), 4604–4619. https://doi.org/10.3390/agriengineering6040263

Eiseul Kim, Seung-Min Yang, Jae-Eun Cha, Dae-Hyun Jung, Hae-Yeong Kim. (2024). Deep learning-based phenotype classification of three ark shells: Anadara kagoshimensis, Tegillarca granosa, and Anadara broughtonii. Frontiers in Marine Science. 2024; 11 ():1356356. 

2023

Kuswidiyanto, L. W., Wang, P., Noh, H. H., Jung, H. Y., Jung, D. H., & Han, X. (2023). Airborne hyperspectral imaging for early diagnosis of kimchi cabbage downy mildew using 3D-ResNet and leaf segmentation. Computers and Electronics in Agriculture, 214, Article 108312. https://doi.org/10.1016/j.compag.2023.108312 

Cho, W. J., Gang, M. S., Kim, D. W., Kim, J. S., Jung, D. H., & Kim, H. J. (2023). Decision-tree-based ion-specific dosing algorithm for enhancing closed hydroponic efficiency and reducing carbon emissions. Frontiers in Plant Science, 14, Article 1301490. https://doi.org/10.3389/fpls.2023.1301490 

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 

Lee, T. S., Jung, D. H., Kim, J. Y., Lee, J. Y., Park, J. E., Kim, H. S., Jung, J. H., & Park, S. H. (2023). Development of a Low-Cost Plant Growth Chamber for Improved Phenotyping Research. Journal of Biosystems Engineering, 48(3), 355-363. https://doi.org/10.1007/s42853-023-00197-7 

Kim, E., Yang, S. M., Jung, D. H., & Kim, H. Y. (2023). Differentiation between Weissella cibaria and Weissella confusa Using Machine-Learning-Combined MALDI-TOF MS. International Journal of Molecular Sciences, 24(13), Article 11009. https://doi.org/10.3390/ijms241311009 

Cho, S., Kim, T., Jung, D. H., Park, S. H., Na, Y., Ihn, Y. S., & Kim, K. G. (2023). Plant growth information measurement based on object detection and image fusion using a smart farm robot. Computers and Electronics in Agriculture, 207, Article 107703. https://doi.org/10.1016/j.compag.2023.107703 

Yoon, H. I., Lee, H., Yang, J. S., Choi, J. H., Jung, D. H., Park, Y. J., Park, J. E., Kim, S. M., & Park, S. H. (2023). Predicting Models for Plant Metabolites Based on PLSR, AdaBoost, XGBoost, and LightGBM Algorithms Using Hyperspectral Imaging of Brassica juncea. Agriculture (Switzerland), 13(8), Article 1477. https://doi.org/10.3390/agriculture13081477 

2022

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 

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 

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 

Choi, J. H., Park, S. H., Jung, D. H., Park, Y. J., Yang, J. S., Park, J. E., Lee, H., & Kim, S. M. (2022). Hyperspectral Imaging-Based Multiple Predicting Models for Functional Component Contents in Brassica juncea. Agriculture (Switzerland), 12(10), Article 1515. https://doi.org/10.3390/agriculture12101515 

Cho, W. J., Kim, H. J., Jung, D. H., Kim, D. W., Yi, Y. H., & Jang, G. J. (2022). VARIABLE-RATE FERTIGATION CONTROL for HYDROPONICS USING AN ON-THE-GO TRANSPIRATION MONITORING SYSTEM. Journal of the ASABE, 65(3), 515-530. https://doi.org/10.13031/JA.14832 

2021

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 

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 

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 

2020

Han, H. J., Jung, D. H., Kim, H. J., Lee, T. S., Kim, H. S., Kim, H. Y., & Park, S. H. (2020). Application of a Spectroscopic Analysis-Based Portable Sensor for Phosphate Quantitation in Hydroponic Solutions. Journal of Sensors, 2020, Article 9251416. https://doi.org/10.1155/2020/9251416 

Park, S. H., Park, T., Park, H. D., Jung, D. H., & Kim, J. Y. (2020). Correction to: Development of Wireless Sensor Node and Controller Complying with Communication Interface Standard for Smart Farming (Journal of Biosystems Engineering, (2019), 44, 1, (41-45), 10.1007/s42853-019-00001-5). Journal of Biosystems Engineering, 45(2), 117. https://doi.org/10.1007/s42853-020-00048-9 

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 

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 

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 

2019

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 

Park, S. H., Jung, D. H., Moon, S. H., Kim, N. Y., Kim, H. S., & Kim, H. J. (2019). Development of vocal recording and analysis system for laying hens and cow based-on cloud-computing. Paper presented at 2019 ASABE Annual International Meeting, Boston, United States. https://doi.org/10.13031/aim.201900952 

Park, S. H., Park, T., Park, H. D., Jung, D. H., & Kim, J. Y. (2019). Development of Wireless Sensor Node and Controller Complying with Communication Interface Standard for Smart Farming. Journal of Biosystems Engineering, 44(1), 41-45. https://doi.org/10.1007/s42853-019-00001-5 

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 

Cho, W. J., Kim, H. J., Jung, D. H., Han, H. J., & Cho, Y. Y. (2019). Hybrid signal-processing method based on neural network for prediction of NO3, K, Ca, and Mg ions in hydroponic solutions using an array of ion-selective electrodes. Sensors, 19(24), Article 5508. https://doi.org/10.3390/s19245508 

Cho, W. J., Kim, H. J., Jung, D. H., Han, H. J., & Cho, Y. Y. (2019). Prediction of NO3, K, Ca, and mg ions in hydroponic solutions using neural network model with an array of ion-selective electrodes. Paper presented at 2019 ASABE Annual International Meeting, Boston, United States. https://doi.org/10.13031/aim.201901039