Phone: +86 27 87285011
Office: TCB A703
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
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
1. Zhang J#, Zhao B#, Yang C, Shi Y, Liao Q, Zhou G, Wang C, Xie T, Jiang Z, Zhang D, Yang W, Huang C, Xie J. Rapeseed Stand Count Estimation at Leaf Development Stages With UAV Imagery and Convolutional Neural Networks. Front. Plant Sci. 2020, 11, 617.
2.Zhang J#, Xie T#, Yang C, Song H, Jiang Z, Zhou G, Zhang D, Feng H, Xie J*. Segmenting Purple Rapeseed Leaves in the Field from UAV RGB Imagery Using Deep Learning as an Auxiliary Means for Nitrogen Stress Detection. Remote Sensing. 2020, 12, 1403.
3.Zhang J#, Wang C#, Yang C, Xie T, Jiang Z, Hu T, Luo Z, Zhou G, Xie J*. Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring. Remote Sensing. 2020, 12, 1207.
4.Zhang J#, Wang C#, Yang C, Jiang Z, Zhou G, Wang B, Shi Y, Zhang D, You L*, Xie J*. Evaluation of an UAV-mounted camera with different spectral modifications and two handheld spectral sensors for rapeseed growth monitoring: Performance and influencing factors. Precision Agriculture. 2020, 10.1007/s11119-020-09710-w.
5.Yang W#*, Feng H#, Zhang X#, Zhang J, Doonan J H, Batchelor W D, Xiong L, Yan J. Crop Phenomics and High-throughput Phenotyping: Past Decades, Current Challenges and Future Perspectives. Molecular Plant. 2020,13(2):187-214.
6. 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)
7. 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
8.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.
9. 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.
10. 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.
11. 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.
12. 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.
13. 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.
14. 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.
15. 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.
16. 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.
17. 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.
18. 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.
19. 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.
20. 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)
21. 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)
22. 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.
23. 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.
24. 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.
25. 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.
26. 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)
27. 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.
28. 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.
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.
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. Changing 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.
Agricultural Production and Provision of Ecosystem Services