Phone: +86 27 87285011
Office: West wing 323
Employment
Since 2022 Professor at Huazhong Agricultural University, Wuhan, China
Since 2014 Associate Professor at Huazhong Agricultural University, Wuhan, China
2014-2016 Visiting Scholar associate in Aerial Application Technology Research Unit (AATRU) of USDA, College Station, USA
2009-2013 Assistant Professor at Huazhong Agricultural University, Wuhan, China
2003-2004 Lecturer at HeFei University of Technology, Hefei, China
Education
1999-2003 B.Sc. in Geographic Information Science, School of Remote Sensing and Information Engineering of Wuhan University, China
2004-2009 Ph.D. in Photogrammetry and Remote Sensing, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) of Wuhan University, China
Articles
1. Ziyue Guo, Chenghai Yang, Wangnen Yang, Guoxing Chen, Zhao Jiang, Botao Wang, Jian Zhang*. Panicle Ratio Network: Streamlining rice panicle measurement by deep learning with ultra-high-definition aerial images in fields [J]. Journal of Experimental Botany. (Accpted)
2. Jijun Li#, Tianjin Xie#, Yahui Chen, Yuting Zhang, Chufeng Wang, Zhao Jiang, Wanneng Yang, Guangsheng Zhou, Liang Guo*, Jian Zhang*. High-throughput UAV-based phenotyping provides insights into the dynamic process and genetic basis of rapeseed waterlogging response in the field [J]. Journal of Experimental Botany, 2022. (https://doi.org/10.1093/jxb/erac242)
3. Jian Zhang#, Bo Sun#, Chenghai Yang, Chunyun Wang, Yunhao You, Guangsheng Zhou, Bin Liu, Chufeng Wang, Jie Kuai*, Jing Xie*. A novel composite vegetation index with solar-induced chlorophyll fluorescence for seedling rapeseed net photosynthesis rate retrieval [J]. Computers and Electronics in Agriculture, 2022, 198: 107031.(https://doi.org/10.1016/j.compag.2022.107031)
4. Zhao Jiang#, Haifu Tu#, Baowei Bai, Chenghai Yang, Biquan Zhao, Ziyue Guo, Qian Liu, Hu Zhao, Wanneng Yang, Lizhong Xiong*, Jian Zhang*. Combining UAV-RGB high-throughput field phenotyping and genome-wide association study to reveal genetic variation of rice in dynamic response to drought stress [J]. New Phytologist, 2021, 232(1):440-455. (https://doi.org/10.1111/nph.17580)
5. Bo Sun#, Chufeng Wang#, Chenghai Yang, Baodong Xu, Guangsheng Zhou, Xiaoyong Li, Jing Xie,Shijie Xu, Bin Liu, Tianjin Xie, Jie Kuai*, Jian Zhang*. Retrieval of rapeseed leaf area index using the PROSAIL model with canopy coverage derived from UAV images as a correction parameter[J]. International Journal of Applied Earth Observation and Geoinformation, 2021,102: 102373. (https://doi.org/10.1016/j.jag.2021.102373)
6. Tianjin Xie#, Jijun Li#, Chenghai Yang, Zhao Jiang, Yahui Chen, Liang Guo*, Jian Zhang*. Crop height estimation based on UAV images: Methods, errors, and strategies[J]. Computers and Electronics in Agriculture, 2021, 185: 106155. ( https://doi.org/10.1016/j.compag.2021.106155)
7. Jian Zhang#, Chufeng Wang#, Chenghai Yang, Zhao Jiang, Guangsheng Zhou, Bo Wang, Yeyin Shi, Dongyan Zhang, Liangzhi You*, Jing Xie*. Evaluation of an UAV-mounted camera with different spectral modifications and two handheld spectral sensors for rapeseed growth monitoring: Performance and influencing factors [J]. Precision Agriculture. 2020, 10.1007/s11119-020-09710-w. (https://doi.org/10.1007/s11119-020-09710-w)
8. Jian Zhang#, Biquan Zhao#, Chenghai Yang, Yeyin Shi, Qingxi Liao, Guangsheng Zhou, Chufeng Wang, Tianjin Xie, Zhao Jiang, Dongyan Zhang, Wanneng Yang, Chenglong Huang* and Jing Xie*. Rapeseed Stand Count Estimation at Leaf Development Stages With UAV Imagery and Convolutional Neural Networks [J]. Frontiers in Plant Science. 2020, 11, 617. ( https://doi.org/10.3389/fpls.2020.00617)
9. Jian Zhang#, Tianjin Xie#, Chenghai Yang, Huaibo Song, Zhao Jiang, Guangsheng Zhou, Dongyan Zhang, Hui Feng, Jing Xie*. Segmenting Purple Rapeseed Leaves in the Field from UAV RGB Imagery Using Deep Learning as an Auxiliary Means for Nitrogen Stress Detection [J]. Remote Sensing. 2020, 12, 1403. (https://doi.org/10.3390/rs12091403)
10. Jian Zhang#, Chufeng Wang#, Chenghai Yang, Tianjin Xie, Zhao Jiang, Tao Hu, Zhibang Luo, Guangsheng Zhou, Jing Xie*. Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring [J]. Remote Sensing. 2020, 12, 1207. (https://doi.org/10.3390/rs12071207)
11. Biquan Zhao, Jiating Li, P. Stephen Baenziger, Vikas Belamkar, Yufeng Ge, Jian Zhang*, Yeyin Shi*. Automatic Wheat Lodging Detection and Mapping in Aerial Imagery to Support High-Throughput Phenotyping and In-Season Crop Management [J]. Agronomy 2020, 10, 1762. ( https://doi.org/10.3390/agronomy10111762)
12. Xiaoyang Zhao, Jian Zhang*, Dongyan Zhang, Xingen Zhou, Xiaohui Liu, Jing Xie*. Comparison between the Effects of Visible Light and Multispectral Sensor Based on Low-Altitude Remote Sensing Platform in the Evaluation of Rice Sheath Blight[J]. Spectroscopy and Spectral Analysis, 2019, 39(4): 1192-1198. (http://www.gpxygpfx.com/EN/Y2019/V39/I04/1192)
13. Zhao X, Zhang J*, Zhang D, Zhou X, Liu X, Xie J*. (2018). Comparison between the effects of visible light and multispectral sensor based on low-altitude remote sensing platform in the evaluation of rice sheath blight. Spectroscopy and Spectral Analysis. (accepted)
14. Zhao B, Zhang J, Yang C, Zhou G, Ding Y, Shi Y, Zhang D, Xie J and Liao Q (2018) Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery. Front. Plant Sci. 9:1362. doi: 10.3389/fpls.2018.01362
15.Zhang D, Zhou X, Zhang J, Lan Y, Xu C, Liang D (2018) Detection of rice sheath blight using an unmanned aerial system with high-resolution color and multispectral imaging. PLoS ONE. 13(5): e0187470.
16. Zhao X, Zhang J*, Yang C, Song H, Shi Y, Zhou X, Zhang D, Zhang G. (2018). Registration for Optical Multimodal Remote Sensing Images Based on FAST Detection, Window Selection, and Histogram Specification. Remote Sensing. 10(5), 663.
17. Zhang J, Meng J, Zhao B, Zhang D, Xie J. (2018). Research on the Chlorophyll Content (SPAD) Distribution Based on the Consumer-Grade Modified Near-Infrared Camera, Spectroscopy and Spectral Analysis. 38(3):737-744.
18. Zhang, J., Wang, X., Yang, C., Zhang, J., He, D. and Song, H. Image dehazing based on dark channel prior and brightness enhancement for agricultural remote sensing images from consumer-grade cameras. Computers and Electronics in Agriculture. 2018, 151, pp.196-206.
19. Wang, X., Yang, C., Zhang, J., & Song, H. Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring. International Journal of Agricultural and Biological Engineering. 2018,11(2), 170-176.
20. Zhang J, Yang C, Zhao B, Song H, Hoffmann, W C, Shi Y, Zhang D, Zhang G. (2017). Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras, Remote Sensing. 9(10): 1054.
21. Zhang J, Li Y, Xie J, Li Z. (2017). Research on Optimal Near-Infrared Band Selection of Chlorophyll (SPAD) 3D Distribution about Rice Plant. Spectroscopy and Spectral Analysis. 37(12):3749-3757.
22. Zhang J., Yang, C., Song, H., Hoffmann, W. C., Zhang, D., Zhang, G (2016). Evaluation of an airborne remote sensing platform consisting of two consumer-grade cameras for crop identification, Remote Sensing, 2016, 8(3):257.
23. Song, H., Yang, C., Zhang J., Hoffmann, W. C., He, D., & Thomasson, J. A. (2016). Comparison of mosaicking techniques for airborne images from consumer-grade cameras. Journal of Applied Remote Sensing, 10(1), 016030-016030.
24. Song, H., Yang, C., Zhang J., He, D., & Thomasson, J. A. (2015). Combining fuzzy set theory and nonlinear stretching enhancement for unsupervised classification of cotton root rot. Journal of Applied Remote Sensing, 9(1), 096013-096013.
25. Li, Y., Li, L., Wang, J., Liu, M., Lu, X., Guo, Z., & Zhang J*. (2014, August). Prediction of SPAD value and distribution of rape leaf based on hyperspectral imaging technology. Agro-geoinformatics (Agro-geoinformatics 2014), Third International Conference on (pp. 1-5). IEEE.
26. Li, Z. N., Xie, J., Zhang J*. (2014, November). Three-dimensional (3D) distribution calculation of chlorophyll in rice based on infrared imaging technique. International Symposium on Optoelectronic Technology and Application 2014 (pp. 93002H-93002H). International Society for Optics and Photonics.
27. Xie, J., Chen, S.,Wang, J.,Li, Y., Liu, M., Zhang J*. (2014). Research on SPAD value prediction and distribution of the rice leaf based on Hyperspectral imaging technique. Journal of Huazhong Normal University, 48(2). (In chinese)
28. Zhang J*., Li, Z., Zhang, N. (2013). Advances in 3D information collection and reconstruction of crop based on the measured data. Journal of Huazhong Agricultural University. 32(4): 126-134. (In chinese)
29. Zhang, R., Ba, J., Ma, Y., Wang, S., Zhang J*., Li, W. (2012, August). A comparative study on wheat leaf area index by different measurement methods. In Agro-Geoinformatics (Agro-Geoinformatics), 2012 First International Conference on (pp. 1-5). IEEE.
30. Zhao, H., Chen, X., Zhang J., & Yin, Z. (2012). Land-use/-cover change spatial patterns and their impacts on sediment charge in the Longchuan River catchment, south-western China. International journal of remote sensing, 33(14), 4527-4552.
31. Zhang J., Chen, X., Gan, W., Yin, S., Wu, H. (2010, July). Exploring some issues of sub-pixel mapping based on directly spatial attraction. In Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International (pp. 339-342). IEEE.
32. Gan, W., Chen, X., Cai, X., Zhang J., Feng, L., & Xie, X. (2010, July). Spatial interpolation of precipitation considering geographic and topographic influences-A case study in the Poyang Lake Watershed, China. In Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International (pp. 3972-3975). IEEE.
33. Hu, Q., Zhang J*., Xu, B., & Li, Z. (2013). A comparison of Google Earth imagery and the homologous Quick Bird imagery being used in land-use classification. Journal of Huazhong Normal University (Natural Sciences), 33(2). (In chinese)
34. Zhang J., Chen, X., Zhong, C., Wu, H., & Duan, S. (2008, July). Application of Geo-Spatial Information Technology in the Engineering Manage of Roller Compaction Construction. In Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International (Vol. 3, pp. III-1312). IEEE.
35. Lu, J., Chen, X., Zhang J*., Sun, Y., & Bao, S. (2007). Spatial Data Management and Analysis System for Flood Hazard Mitigation of Poyang Lake Watershed, China. Geographic Information Sciences, 13(1-2), 10-17.
Research Grants
1.“Physiological basis and regulation of high quality and high yield of cash crop in field”, Project supported by “National Key R&D Program of China”, (Grant No. 2018YFD1000900), PI (subtask), 2018-2022.
2. “Research and implementation of key information acquisition method for whole-process mechanization of rapeseed production by low-altitude remote sensing technology”, Project supported by “The Fundamental Research Funds for the Central Universities”, (Grant No. 2662017JC038), PI, 2017-2019.
3. “Assessing crop growth and pest conditions using airborne imaging systems and ground-based sensors for precision agriculture and aerial application”, Project supported by “China Scholarship Council”, (Grant No. 201308420447), PI, 2014-2015.
4. “Research on the nondestructive acquisition method of crop physical and chemical information based on the hyper-spectral imaging technology”, Project supported by “The Fundamental Research Funds for the Central Universities”, (Grant No. 2014JC008), PI, 2014-2015.
5. “Research on Sub-Pixel Mapping with Fractal Dimension Constrains”, Project supported by “National Natural Science Foundation of China”, (Grant No. 41201364), PI, 2013-2015.
6. “Research on the acquisition methods of the crop growth information with noncontact”, Project supported by “The Fundamental Research Funds for the Central Universities”, (Grant No. 2011QC040), PI, 2011-2013.
7. “Research on the scaling-down methods of imagery based on the spatial variability theory”, Project supported by “Hubei Provincial Natural Science Foundation of China”, (Grant No. 2010CDB099), PI, 2010-2012.
8. “Research on Sub-Pixel Mapping Methods by the Spatial Structure Information”, Project supported by “the Scientific Research Staring Foundation”, (Grant No. 2011QC040), PI, 2009-2011.
9. “Research on prediction method for the 3D distribution of overground part rice nutrition based on super-resolution hyperspectral imagery”, Project supported by “National Natural Science Foundation of China”, (Grant No. 31501222), 2015-2018.
10. “Northwest non-cultivated land agriculture utilization technology and industrialization”, Project supported by “Special Fund for Agro-scientific Research in the Public Interest”, (Grant No. 201203005), 2012-2017.
Research Interests
1. Agricultural remote sensing based on UAV imaging platform
Assembling small UAV platform with multiple spectral and thermal sensors for crop identification and crop growth monitoring. Using ENVI, ERDAS, Pix4D Mapper, eCognition, and ArcGIS for imagery processing and analysis.
2. Field high-throughput plant phenotyping and analysis based on UAV platform
Multi-feature extracting and analyzing big data from imagery and field. Using spatial modeling of ArcGIS, python for ArcGIS, and SPSS.
3. Evaluating the performance of different imaging system based on consumer grade camera
Comparing and evaluating the performance of consumer grade camera with the scientific grade imaging platform.
4. Exchanging mechanism of crop physicochemical parameter from different observation platforms (Ground, UAS, Aircraft, and Satellite)
Comparing the data with information from different observation platforms (Ground, UAV, Aircraft, and Satellite) in order to make clear the changing mechanism of crop physicochemical parameter.
Research Themes
Smart Agriculture
Agricultural Production and Provision of Ecosystem Services
