HyungJun Jin | Precision Agriculture | Best Researcher Award

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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

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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.

Mona Maze | Land Classification | Best Researcher Award

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Assoc. Prof. Dr. Mona Maze | Land Classification | Best Researcher Award

Senior Researcher | Agricultural Research Center | Egypt

Dr. Mona Maze is a dedicated researcher specializing in agricultural climate, plant nutrition, and digital agriculture, with a strong focus on developing climate change adaptation strategies, precision farming approaches, and the use of remote sensing and machine learning in agriculture. She earned her PhD in Plant Nutrition from the Technical University of Munich, where her doctoral work addressed crop growth and yield modeling under water scarcity and changing climatic conditions. Over her professional career, she has actively contributed to national and international research projects in collaboration with institutions such as the European Commission, USAID, UNDP, and FAO, while also leading initiatives like the Digital Dynamic Agricultural Map of Egypt and Early Warning Systems for farmers. Her teaching experience and supervision of graduate students reflect her commitment to academic development, while her publication record in reputed journals such as Scientific Reports, ISPRS Journal of Photogrammetry and Remote Sensing, Agronomy, and Energies highlights her strong scientific contributions. Her research interests span climate-smart agriculture, soil fertility, plant nutrition, digital transformation in agriculture, and data-driven solutions for food security. She possesses advanced research skills in machine learning, deep learning, geospatial data analysis, crop modeling, and experimental design, complemented by professional certifications in business management, spatial data science, and AI-based systems. She has 54 citations by 54 documents, 11 publications, and an h-index of 4, reflecting her growing impact in the scientific community.

Profiles: Scopus | ORCID

Featured Publications

  1. Maze, M., Attaher, S., Taqi, M. O., Elsawy, R., Gad El-Moula, M. M. H., Hashem, F. A., & Moussa, A. S. (2025). Enhanced agricultural land use/land cover classification in the Nile Delta using Sentinel-1 and Sentinel-2 data and machine learning. ISPRS Journal of Photogrammetry and Remote Sensing.

  2. Salah, M., Maze, M., & Tonbol, K. (2024). Intersecting vulnerabilities: Climate justice, gender inequality, and COVID-19’s impact on rural women in Egypt. Multidisciplinary Adaptive Climate Insights.

  3. Maze, M., Taqi, M. O., Tolba, R., Abdel-Wareth, A. A. A., & Lohakare, J. (2024). Estimation of methane greenhouse gas emissions from livestock in Egypt during 1989 to 2021. Scientific Reports.

  4. El-Beltagi, H. S., Hashem, F. A., Maze, M., Shalaby, T. A., Shehata, W. F., & Taha, N. M. (2022). Control of gas emissions (N₂O and CO₂) associated with applied different rates of nitrogen and their influences on growth, productivity, and physio-biochemical attributes of green bean plants grown under different irrigation methods. Agronomy, 12(2), 249.

  5. Abd El-Fattah, D. A., Maze, M., Ali, B. A. A., & Awed, N. M. (2022). Role of mycorrhizae in enhancing the economic revenue of water and phosphorus use efficiency in sweet corn (Zea mays L. var. saccharata) plants. Journal of the Saudi Society of Agricultural Sciences.

Yoliswa-Mkhize-Precision Agriculture-Best Researcher Award

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MsYoliswa-Mkhize-Precision Agriculture-Best Researcher Award 

Council for Scientific and Industrial Research-South Africa 

Author Profile

Early Academic Pursuits

Ms. Yoliswa Mkhize's academic journey is a testament to her passion and dedication to environmental sciences and geospatial technologies. Her Bachelor's degree in Life and Environmental Sciences from the University of KwaZulu-Natal laid a strong foundation in various disciplines like GIS, environmental science, biology, and ecology. This multidisciplinary approach equipped her with a diverse skill set, crucial for her subsequent academic pursuits and professional endeavors.

During her undergraduate years, Ms. Yoliswa Mkhize's received the Dean's Commendation in her first year, highlighting her exceptional academic performance. Additionally, earning six Merits Certificates as one of the top three students further underscored her commitment to academic excellence and her capability to excel in challenging environments.

Professional Endeavors

Ms. Yoliswa Mkhize's professional journey is marked by her roles in precision agriculture, data science, and environmental management. Her work as a Precision Agriculture Innovative Researcher at the Council for Scientific and Industrial Research (CSIR) showcases her expertise in leveraging cutting-edge technologies to optimize agricultural practices. Her proficiency in data analysis, geoanalysis, and earth observation techniques has been instrumental in driving innovation in agricultural research and sustainability.

Apart from her research role, Yoliswa has also contributed significantly as a Demonstrator and Tutor at the University of KwaZulu-Natal. Her responsibilities included tutoring courses in geography and environmental sciences, demonstrating practical applications of geocoding, wetlands management, and GIS technologies. This hands-on experience not only enriched her teaching skills but also enhanced her understanding of real-world applications of environmental science concepts.

Contributions and Research Focus

Ms. Yoliswa Mkhize's research focus spans across several domains, including geoanalysis, wetlands management, precision agriculture, and earth observation. Her Master's degree thesis on geocoding and wetlands at the University of KwaZulu-Natal reflects her expertise in spatial analysis and environmental conservation. Additionally, her ongoing postgraduate studies in Geoinformatics at the University of Pretoria signify her commitment to advancing her knowledge in geospatial technologies and their applications in diverse sectors.

One of Yoliswa's notable contributions is her involvement in African energy planning research during her vacation work at CSIR. This experience allowed her to work with SQL and Microsoft Access, gaining valuable insights into data management and analysis in the context of energy planning and environmental sustainability.

Accolades and Recognition

Ms. Yoliswa Mkhize's academic and professional achievements have earned her recognition, including membership in the Golden Key International Honour Society. This prestigious society acknowledges outstanding academic achievements and leadership qualities, reflecting Yoliswa's standing as a high-achieving and driven professional in her field.

Impact and Influence

Ms. Yoliswa Mkhize's work as a Geo-Data Scientist, Geoanalyst, and Environmental Scientist has a tangible impact on sustainable development, precision agriculture, and environmental conservation. Her research and contributions in geoanalysis, GIS technologies, and earth observation techniques contribute to informed decision-making processes in environmental management and agriculture. By integrating data science with environmental sciences, Yoliswa bridges the gap between technology and sustainability, paving the way for innovative solutions to complex environmental challenges.

Precision Agriculture Award recognizes groundbreaking advancements in the field of agricultural technology, aiming to enhance sustainability, productivity, and efficiency in farming practices. This prestigious accolade celebrates innovations that harness data analytics, IoT (Internet of Things), AI (Artificial Intelligence), and other cutting-edge technologies to optimize crop yields, minimize resource usage, and mitigate environmental impacts.

Legacy and Future Contributions

As Ms. Yoliswa Mkhize continues her academic and professional journey, her legacy lies in shaping the future of environmental science, data analytics, and geospatial technologies. Her expertise and insights will continue to influence research, policy-making, and industry practices, fostering a more sustainable and data-driven approach to environmental management. With a focus on precision agriculture, geoanalysis, and earth observation, Yoliswa's future contributions hold promise in driving impactful change and promoting environmental stewardship on a global scale.

 

Notable Publication