HyungJun Jin | Precision Agriculture | Best Researcher Award

Published on by Computer Vision Awards

Mr. HyungJun Jin | Precision Agriculture | Best Researcher Award

Deputy General Manager (R&D) | CC Ventures | South Korea

Mr. HyungJun Jin is a dedicated researcher in computer vision and robotics with strong expertise in precision agriculture and intelligent robotic systems. He holds a background in Mechanical Design Engineering and Electronics and Information Engineering with excellent academic records, further strengthened by a master’s thesis focused on deep learning-based semantic segmentation for crop–weed identification. His professional experience includes contributing to multi-sensor monitoring systems for livestock housing, intelligent weeding robots for farmland, and AI-based environmental sensing and disease detection systems in crops and livestock. His research interests span computer vision, deep learning, autonomous robotics, agricultural intelligence, and smart farming technologies aimed at improving efficiency and sustainability. He has been recognized with the President’s Award for Academic Excellence and the Outstanding Paper Award at ICROS, highlighting his research excellence and innovation. Skilled in deep learning frameworks such as TensorFlow, Keras, and PyTorch, as well as robotics systems like ROS2, Jetson Nano, and Arduino, he combines theoretical knowledge with practical implementation in various interdisciplinary projects. He also possesses strong programming and simulation skills, along with experience in datasets, model construction, and sensor integration for real-world applications. His contributions reflect a commitment to advancing AI-driven robotics for agriculture and livestock industries. He has 37 citations, 3 documents, and an h-index of 3.

Profiles: Google Scholar | Scopus 

Featured Publications

Ilyas, T., Jin, H., Siddique, M. I., Lee, S. J., Kim, H., & Chua, L. (2022). DIANA: A deep learning-based paprika plant disease and pest phenotyping system with disease severity analysis. Frontiers in Plant Science, 13, 983625.

Lee, J., Ilyas, T., Jin, H., Lee, J., Won, O., Kim, H., & Lee, S. J. (2022). A pixel-level coarse-to-fine image segmentation labelling algorithm. Scientific Reports, 12(1), 8672.

Jin, H. J., & Kim, H. S. (2021). A study on paprika disease detection with YOLOv4 model using a customed pre-training method. In Proceedings of the 2021 21st International Conference on Control, Automation and Systems (ICCAS) (pp. xxx–xxx). IEEE.

Jin, H., & Kim, H. (2021). Weed label data generation using image thresholding method. Proceedings of the Institute of Control, Robotics and Systems Regional Conference, 74.

Jin, H., Kim, S., & Kim, H. (2020). Comparison of accuracy on CIFAR-10 datasets according to depth of ResNet network. Proceedings of the Institute of Control, Robotics and Systems National Conference, 108–109.

Huaiqu Feng | Agricultural Robotics | Best Researcher Award

Published on by Computer Vision Awards

Dr. Huaiqu Feng | Agricultural Robotics | Best Researcher Award

Huaiqu Feng | Zhejiang University | China

Huaiqu Feng is a skilled researcher with expertise in robotics and electromechanical intelligent equipment, focusing on computer vision, deep learning, and image processing for agricultural automation. He holds a Master of Engineering in Agricultural Mechanization Engineering from Northeast Agricultural University and a Bachelor of Engineering in Automation from Hubei Normal University. Throughout his academic and professional career, he has participated in multiple research projects, including provincial science and technology programs and industrial transformation initiatives, demonstrating strong capability in applying AI and robotics to practical agricultural problems. He has contributed to several high-impact publications, patents, and software developments, showcasing his innovative approach and technical proficiency. His professional experience includes leading research teams, mentoring students, and managing projects that integrate advanced technologies into real-world applications. His research interests span robotics, precision agriculture, intelligent equipment, and AI-based image analysis. He is proficient in Matlab for algorithm development, microcontroller programming with STM32, and 3D modeling and simulation using Creo and Pro/E. Huaiqu Feng also actively engages in community and leadership roles through student organizations, innovation competitions, and volunteer initiatives, highlighting his commitment to fostering collaboration and advancing the research community. 426 Citations, 20 Documents, 8 h-index.

Profiles: Google Scholar | Scopus | ORCID

Featured Publications

  1. Quan, L., Feng, H., Lv, Y., Wang, Q., Zhang, C., Liu, J., & Yuan, Z. (2019). Maize seedling detection under different growth stages and complex field environments based on an improved Faster R–CNN. Biosystems Engineering, 184, 1-23.

  2. Zhao, G., Quan, L., Li, H., Feng, H., Li, S., Zhang, S., & Liu, R. (2021). Real-time recognition system of soybean seed full-surface defects based on deep learning. Computers and Electronics in Agriculture, 187, 106230.

  3. Li, D., Li, B., Long, S., Feng, H., Xi, T., Kang, S., & Wang, J. (2023). Rice seedling row detection based on morphological anchor points of rice stems. Biosystems Engineering, 226, 71-85.

  4. Wei, C., Li, H., Shi, J., Zhao, G., Feng, H., & Quan, L. (2022). Row anchor selection classification method for early-stage crop row-following. Computers and Electronics in Agriculture, 192, 106577.

  5. Li, D., Li, B., Long, S., Feng, H., Wang, Y., & Wang, J. (2023). Robust detection of headland boundary in paddy fields from continuous RGB-D images using hybrid deep neural networks. Computers and Electronics in Agriculture, 207, 107713.