Main work
Prof. Wang Xuefeng primarily engaged in the research of image understanding and computer vision for forest management and intelligent monitoring of forest resources. I have several years of experience conducting remote machine-based monitoring research on plant growth and resource changes at institutions such as Kyoto University and the Institute of Intelligent Information in Japan.
Prof. Wang Xuefeng has led various national research projects, including the National Key Research and Development Program, the National High-Tech Research and Development Program (863), the Introduction of Advanced International Agricultural Science and Technology Program (948), and the National Natural Science Foundation of China.
Currently, Prof. Wang Xuefeng holds 10 authorized invention patents and have published five monographs, including "Introduction to Forestry Internet of Things Technology," "Fundamentals of Image Understanding," and "Modern Forest Surveying Methods." He has also published over 100 high-quality academic papers.
Key research projects
3D perspective detection and verification of forest vegetation, 2024-2027.
Decision support system for adaptive management of forest ecosystem, 2023-2026.
Accurate image judgment technology for the health status of precious tree species, 2021-2025.
Machine understanding method of forest nutrient and moisture requirement, 2021-2024.
Intelligent technology research on rare plants protection in tropical national forest parks, 2021-2023.
Study of tree health state by using machines independent diagnosis, 2017-2020.
Published articles & books
Wang Peng ,Wu Yi, Wang Xuefeng*. 2023. Multispectral Image Determination of Water Content in Aquilaria sinensis Based on Machine Learning. Forests, 13(12)1-20.
Yuan Ying, Wang Xuefeng*, Shi Mengmeng. 2022. Performance comparison of RGB and multispectral vegetation indices based on machine learning for estimating Hopea hainanensis SPAD values under different shade conditions, Frontiers in Plant Science.
Guo Ying, Gao Junjia, Wang Xuefeng*. 2022. Precious Tree Pest Identification with Improved Instance Segmentation Model in Real Complex Natural Environments, Forests, 13(2048):1-14.
Chen Zhulin, Wang Xuefeng*, Sun Shanshan, 2022. Estimating the total nitrogen content of Aquilaria sinensis leaves based on a hybrid feature selection algorithm and image data from a modified digital camera, Biosystems Engineering. 213(1):89-104.
Liu Jiazheng, Wang Xuefeng*, Wang Tian. 2019. Classification of Tree Species and Stock Volume estimation in ground forest images using Deep Learning. Computers and Electronics in Agriculture, 166(November 2019), 105012
Chen Zhulin, Wang Xuefeng*. 2019. Model for estimation of total nitrogen content in sandalwood leaves based on nonlinear mixed effects and dummy variables using multispectral images, Chemometrics and Intelligent Laboratory Systems, 195(15) 103874.
Chen Zhulin, Wang Xuefeng*, Wang huaijing. 2018. Preliminary research on total nitrogen content prediction of sandalwood using the error-in-variable models based on digital image processing, Plos One, 13(8):e0202649.
Wang Xuefeng, Wu Chunyan, Hirafuji Masayuki*. 2016. Visible Light Image-Based Method for Sugar ContentClassification of Citrus, PLoS ONE, 11(1):1-16.
Wang Xuefeng*, Hirafuji Masayuki, Li Xiaodong. 2013. Tree feature extraction using image data obtained through virtual Field Server, Computers and Electronics in Agriculture, 93:121-128.
Wang X F, Chen Z L, Wang T. Forest measurement, China forestry publishing, 2021
Wang X F, Bi Y H, Wu C Y. The basic of image understanding, China forestry publishing, 2015
Wang X F. Introduction to forestry internet of things technology, 2011
Other results
Wang X F. A method and device for measuring forest stand tightness, ZL201810666558.1, 2022.
Wang X F, Li X D. A method and device for measuring canopy closure, ZL201610304870.7, 2018.
Wang X F. A method and device for determining fruit sugar content, ZL201310472628.7, 2017.
