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
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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.
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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.
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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.
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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.
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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.