Yang, Guofeng

Member Profile

College of Biosystems Engineering and Food Science
E-mail: yangguofeng@zju.edu.cn

EDUCATION
Sept. 2021 - present, Ph.D. in Agricultural electrification and automation, Zhejiang University.

Sept. 2017 – Jun. 2020, M.S. in Information technology and digital agriculture, Chinese Academy of Agricultural Sciences.

Sept. 2013 – Jun. 2017, B.S. in Information management and information system. Henan University of Science and Technology.

RESEARCH INTERESTS
I am committed to research on farmland greenhouse gas emission reduction, UAV remote sensing monitoring of plant phenotypes, and agricultural system model development and application. I mainly explore the impact of agricultural precision control models and global climate change on crop production and the agricultural ecological environment.

RESEARCH AREAS
Carbon cycle, Carbon footprint, Climate change, Plant phenotyping, Remote sensing of environment

PUBLICATIONS
Yang, G., Yang, Y., He, Z., Zhang X., & He, Y. 2022. A rapid, low-cost deep learning system to classify strawberry disease based on cloud service. Journal of Integrative Agriculture, 21(2), 460-473.
Zhu, J., Yang, G.*, Feng, X., Li, X., Fang, H., Zhang, J., ... & He, Y. 2022. Detecting wheat heads from UAV low-altitude remote sensing images using Deep Learning based on transformer. Remote Sensing, 14(20), 5141.

Yang, G., He, Y., Zhou, Z., Huang, L., Li, X., Yu, Z., ... & Feng, X. 2022. Field Monitoring of Fractional Vegetation Cover Based on UAV Low-altitude Remote Sensing and Machine Learning. International Conference on Agro-geoinformatics, pp. 1-6. IEEE.

Yang, G., Chen, G., Li, C., Fu, J., Guo, Y., & Liang, H. 2021. Convolutional rebalancing network for the classification of large imbalanced rice pest and disease datasets in the field. Frontiers in Plant Science, 12, 671134.

Yang, G., He, Y., Yang, Y., & Xu, B. 2020. Fine-grained image classification for crop disease based on attention mechanism. Frontiers in Plant Science, 11, 600854.

Yang, G., Chen, G., He, Y., Yan, Z., Guo, Y., & Ding, J. 2020. Self-supervised collaborative multi-network for fine-grained visual categorization of tomato diseases. IEEE Access, 8, 211912-211923.

Yang, G., & Yang, Y. 2020. Question classification of common crop disease question answering system based on BERT. Journal of Computer Applications, 40(6), 1580.

Yang, G., He, Y., Feng, X., Li, X., Zhang, J., & Yu, Z. 2022. Methods and new research progress of remote sensing monitoring of crop disease and pest stress using unmanned aerial vehicle. Smart Agriculture, 4(1), 1.

Feng, X., Yu, Z., Fang, H., Jiang, H., Yang, G., Chen, L., ... & Liu, F. 2023. Plantorganelle Hunter is an effective deep-learning-based method for plant organelle phenotyping in electron microscopy. Nature Plants, 1-16.

Li, X., Chen, J., He, Y., Yang, G., Li, Z., Tao, Y., ... & Feng, X. 2023. High-through counting of Chinese cabbage trichomes based on deep learning and trinocular stereo microscope. Computers and Electronics in Agriculture, 212, 108134.

Li, X., Feng, X., Fang, H., Yang, N., Yang, G., Yu, Z., ... & He, Y. 2023. Classification of multi-year and multi-variety pumpkin seeds using hyperspectral imaging technology and three-dimensional convolutional neural network. Plant Methods, 19(1), 1-18.

Zhang, J., Feng, X., Wu, Q., Yang, G., Tao, M., Yang, Y., & He, Y. 2022. Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning. Plant Methods, 18(1), 1-16.

Bai, X., Zhou, Y., Feng, X., Tao, M., Zhang, J., Deng, S., ... & He, Y. 2022. Evaluation of rice bacterial blight severity from lab to field with hyperspectral imaging technique. Frontiers in Plant Science, 13, 1037774.

Bai, X., Fang, H., He, Y., Zhang, J., Tao, M., Wu, Q., ... & Feng, X. 2023. Dynamic UAV Phenotyping for Rice Disease Resistance Analysis Based on Multisource Data. Plant Phenomics, 5, 0019.

He, Z., Yang, Y., Yang, G., & Zhang, X. 2020. Sentiment analysis of agricultural product ecommerce review data based on deep learning. In 2020 International Conference on Internet of Things and Intelligent Applications, pp. 1-7. IEEE.

He, Y., Li, X., Yang, G., Yu, Z., Yang, N., Feng, X., & Xu, L. 2022. Research progress and prospect of indoor high-throughput germplasm resource phenotyping platform. Journal of Agricultural Engineering, 38(17):127-141.

He, Z., Yang, Y., Yang, G., & Zhang, X. 2022. Research and Application Prospects of Named Entity Recognition of Agricultural Product Information Text Based on BERT-BiLSTM-CRF. Agricultural Outlook, 2022, 18(05): 105-111.

PRESENTATIONS
Oral: Field Study for High-Throughput Monitoring of Fractional Vegetation Cover. The 30th International Conference on Geoinformatics, University College London, London, UK (July 19-21, 2023)

Oral: Field Monitoring of Fractional Vegetation Cover Based on UAV Low-altitude Remote Sensing and Machine Learning. The 10th International Conference on Agro-Geoinformatics, International Society of Agromatics，Canadian Remote Sensing Society, Quebec City, Canada (July 25-28, 2022)