Mr. Zili Chen | Smart Agriculture | Best Researcher Award

Zili Chen is currently pursuing graduate studies at Henan Normal University, specializing in Agricultural Engineering and Information Technology. Before his master’s degree, he worked in software development at Zhejiang Jiuzhou Tuoxin Information Service Co., Ltd. His research focuses on applying computer vision and deep learning in agricultural remote sensing, particularly in disease monitoring. He has published academic papers in international journals and won the Best Graduate Oral Presentation Award at the 5th Remote Sensing Conference on Vegetation Diseases and Pests Graduate Forum. His ongoing projects include intelligent monitoring systems for tobacco production and digital twin systems for agricultural big data.

Mr. Zili Chen | Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences | China

Education

Zili Chen is currently pursuing a graduate degree in Agricultural Engineering and Information Technology at Henan Normal University. His academic journey has equipped him with expertise in computer vision and deep learning applications in agricultural remote sensing.

Experience

Before beginning his master’s studies, Zili Chen worked in software development at Zhejiang Jiuzhou Tuoxin Information Service Co., Ltd., where he gained practical experience in information technology and data processing. His industry background complements his research, allowing him to bridge the gap between theoretical advancements and real-world applications.

Awards and Achievements

Zili Chen has been recognized for his outstanding research contributions, winning the Best Graduate Oral Presentation Award at the 5th Remote Sensing Conference on Vegetation Diseases and Pests Graduate Forum. His work has also been published in reputable international journals, further demonstrating the impact of his research.

Skills and Certifications

Zili Chen specializes in deep learning algorithms, computer vision, and agricultural remote sensing. His expertise extends to software development, data analysis, and artificial intelligence applications in agriculture. He is currently applying for a national invention patent for a Method and System for Tobacco Leaf Disease Spot Segmentation Based on Multi-Scale Residual Dilated Convolution, reflecting his commitment to technological innovation in smart agriculture.

Research Focus

His primary research interests lie in the application of computer vision and deep learning for agricultural disease monitoring. He has contributed to improving segmentation accuracy for tobacco leaf lesions through advanced deep learning models, significantly enhancing recognition efficiency. His research has been applied in intelligent monitoring systems for high-end agricultural products and has been integrated into national key research projects.

Conclusion

Based on his research impact, technological innovations, industry collaborations, and academic recognition, Zili Chen is a strong candidate for the Research for Best Researcher Award. His expertise in agricultural remote sensing and deep learning not only contributes to scientific knowledge but also drives practical advancements in precision farming and smart agriculture solutions. His award-winning presentation skills, peer-reviewed publications, and contributions to national research projects make him a deserving nominee for this prestigious recognition.

Publications

MD-Unet for tobacco leaf disease spot segmentation based on multi-scale residual dilated convolutions
Authors: Zili Chen, Yilong Peng, Jiadong Jiao, Wei Lin, Yan Guo
Journal: Scientific Reports, 2025

Zili Chen | Smart Agriculture | Best Researcher Award