教授
姚霞
发布人: 姚霞 发布日期: 2020-09-14 浏览次数:
姚霞，教授，博导
电话：0086-025-84396565
研究领域：农情遥感监测；作物表型监测
个人简介：
姚霞，南京农业大学农学院教授，博导。现任智慧农业教育部工程中心副主任、智慧农业系主任，长期从事农情遥感监测研究。近五年来先后主持国家重点研发课题（3项）、国家自然科学基金（3项）及国家863计划课题、国家科技支撑计划课题等省部级项目20项，已发表核心期刊论文70多篇，合作出版专著（教材）6部；授权国家发明专利22件；登记国家计算机软件著作权6项。指导研究生近30名（其中1名获省优秀硕士论文）。获2023年测绘科学技术奖（排名第7）、2022年南京农业大学研究生教育“优秀导师团队”（排名第2）、2022年地理信息科技进步奖(排名第3）；2021年日内瓦国际发明展银奖（排名第5）、2021年神农中华农业科技奖优秀创新团队奖（排名第4）；2014年江苏省科技进步一等奖和2015年国家科技进步二等奖（排名第4）。获2016年江苏省高校“青蓝工程”中青年学术带头人称号，2021年江苏省第六期“333高层次人才培养工程”，2023年入选南京农业大学钟山青年骨干。
现任智慧农业系主任，配合学院建设智慧农业本科-硕士-博士专业和学科；作为秘书，配合学科负责人建设“农业信息学”省优势学科一期和二期项目；协助建设“国家信息农业工程技术中心”，参加“现代作物生产协同创新中心”的工作。任《Remote Sensing》编委，国际地球科学与遥感分会和江苏省遥感和地理信息系统成员。任国际期刊Remote Sensing of Environment, ISPRS Journal of Photogrammetry and Remote Sensing, International Journal of Applied Earth Observation and Geoinformation, Remote Sensing, Field Crop Research的审稿人。
欢迎具有遥感、GIS、计算机或测绘工程等背景的硕/博士研究生和博士后加入，尤其欢迎对高/多光谱影像、日光诱导叶绿素荧光、LiDAR、图像处理等感兴趣的同仁加盟！优先考虑积极向上、刻苦钻研、勇于探索、对新技术有好奇心的同学，男女不限！
近期主要成果
专著和教材
1. 专著：作物生长光谱监测. 科学出版社. 2020.（主编）
2. 专著： Hyperspectral remote sensing of leaf nitrogen concentration in cereal crops. Cheng, T., Zhu, Y., Li, D., Yao, X, & Zhou, K. (2018). In P. S. Thenkabail, J. Lyon, & A. Huete (Eds.), Hyperspectral Remote Sensing of Vegetation, Second Edition, Four Volume Set, Volume 2. Boca Raton, FL: CRC Press.
3. 专著：Estimating leaf nitrogen concentration of cereal crops with hyperspectral data. In: Prasad ST, John GL, Alfredo H. (eds.) Hyperspectral Remote Sensing of Vegetation. CRC Press, FL, USA. 2011.187-206.（参编）
4. 专著：物联网与食品质量安全. 科学出版社. 2014（参编）。
5. 专著：数字农作技术. 科学出版社. 2008.（参编）
6. 教材：农业信息化技术导论.中国农业科学技术出版社. 2009.（参编）
1. Wang Y., Gu Y., Tang J., Timothy A. Warner., Guo C., Zheng H., Fumiki Hosoi, Cheng T., Zhu Y., Cao W., Yao X.*. Quantify wheat canopy leaf angle distribution using terrestrial laser scanning data. IEEE Transactions on Geoscience and Remote Sensing. 2024, 62: 1-15. https://ieeexplore. ieee.org/document/10398251 (IF=8.2)
2. Gu, Y., Ai, H., Guo, T., Liu, P., Wang, Y., Zheng, H., Cheng, T., Zhu, Y.*, Cao, W., Yao, X.*. Comparison of two novel methods for counting wheat ears in the field with terrestrial LiDAR. Plant Methods. 2023, 19:134. https://doi.org/ 10.1186/s13007-023-01093-z (IF=5.1)
3. Zhu J., Yin YM., Lu JS., Timothy A. Warner., Xu XW., Lyu MY., Wang X., Guo CL., Cheng T., Zhu Y., Cao WX., Yao X.*, Zhang YG., Liu LY. The relationship between wheat yield and sun-induced chlorophyll fluorescence from continuous measurements over the growing season. Remote Sensing of Environment. 2023, 298:113791. https://doi.org/10.1016/j.rse.2023.113791 (IF=13.6)
4. Li W., Li D.,Liu S.,Frédéric Baret., Ma Z., He C., Timothy A. Warner.,Guo C., Cheng T., Zhu Y.*, Cao W., Yao X.*. RSARE: A physically-based vegetation index for estimating wheat green LAI to mitigate the impact of leaf chlorophyll content and residue-soil background. ISPRS Journal of Photogrammetry and RemoteSensing.2023,200:138-152. https://doi.org/10.1016/j.isprsjprs.2023.05.012 (IF=12.4)
5. Zhu J., Lu J., Li W., Wang Y., Jiang J., Cheng T., Zhu Y., Cao W., Yao X*. Estimation of canopy water content for wheat through combining radiative transfer model and machine learning. Field Crop Research.2023,302:109077. https://doi.org/10.1016/j.fcr.2023.109077
6. Ma ZY., Li W., Timothy A. Warner., He C., Wang X., Zhang Y., Guo C., Cheng T., Zhu Y., Cao W., Yao X.*. A framework combined stacking ensemble algorithm to classify crop in complex agricultural landscape of high altitude regions with Gaofen-6 imagery and elevation data. International Journal of Applied Earth Observation and Geoinformation.2023,122:103386. https://doi.org/10.1016/j.jag. 2023.103386
7. Mustafa G., Zheng H., Li W., Yin Y., Wang Y., Zhou M., Liu P., Bilal M., Jia H., Li.G, Cheng T., Tian Y., Cao W., Zhu Y.*, Yao X.*. Fusarium head blight monitoring in wheat ears using machine learning and multimodal data from asymptomatic to symptomatic periods. Frontiers in Plant Science. 2023,13:1102341. https://doi.org/10.3389/fpls.2022.1102341
8. Zhou M., Zheng H., He C., Liu P., Mustafa G., Wang X., Cheng T., Zhu Y., Cao W., Yao X.*. Wheat phenology detection with the methodology of classification based on
the time-series UAV images. Field Crops Research. 2023, 292:108798. https://doi.org/10.1016/j.fcr.2022.108798
9. Wang K., Zhu J., Xu X., Li T., Wang X., Timothy A. Warner, Cheng T., Zhu Y*., Cao W., Yao X*. Zhang Z., Quantitative monitoring of salt stress in rice with solar-induced chlorophyll fluorescence. European Journal of Agronomy, 2023. 150, 126954.
10. Mustafa G., Zheng H., Khan I., Tian L., Jia H., Li G., Cheng T., Tian Y., Cao W., Zhu Y.*, Yao X.*. Hyperspectral reflectance proxies to diagnose in-field fusarium head blight in wheat with machine learning. Remote Sensing. 2022, 14(12): 2784. https://doi.org/10.3390/rs14122784
11. Jiang J., Liu H., Zhao C., He C., Ma J., Cheng T., Zhu Y., Cao W., Yao X.*. Evaluation of diverse convolutional neural networks and training strategies for wheat leaf disease identification with field-acquired photographs. Remote Sensing. 2022, 14(14): 3446. https://doi.org/10.3390/rs14143446
12. Khan, I.H., Liu, H., Li, W., Cao, A., Wang, X., Liu, H., Cheng, T., Tian, Y., Zhu, Y., Cao, W., Yao, X*. Early detection of powdery mildew disease and accurate quantification of its severity using hyperspectral images in Wheat. Remote Sensing. 2021, 13, 3612. https://doi.org/10.3390/rs13183612
13. Jia, M., Colombo, R., Rossini, M., Celesti, M., Zhu, J., Cogliati, S., Cheng, T., Tian, Y., Zhu, Y., Cao, W., Yao, X*. Remote estimation of nitrogen content and photosynthetic nitrogen use efficiency in wheat leaf using sun-induced chlorophyll fluorescence at the leaf and canopy scales. European Journal of Agronomy. 2021.12:14. https://doi.org/10.1016/j.eja.2020.126192
14. Jiang, J.; Zhu, J.; Wang, X.; Cheng, T.; Tian, Y.; Zhu, Y.; Cao, W.; Yao, X*. Estimating the leaf nitrogen content with a new feature extracted from the ultra-high spectral and spatial resolution images in wheat. Remote Sensing. 2021, 13, 739. https://doi.org/10.3390/rs13040739
15. Fang Y, Qiu X, Guo T, Wang Y, Cheng T, Zhu Y, Chen Q, Cao W, Yao X*, Niu Q, Hu Y, Gui L. An automatic method for counting wheat tiller number in the field with terrestrial LiDAR. Plant Methods. 2020, 16(1): 132. https://doi.org/ 10.1186/s13007-020-00672-8
16. Zhou M, Ma X, Wang K, Cheng T, Tian Y, Wang J, Zhu Y, Hu Y, Niu Q, Gui L, Yue C, Yao X*. Detection of phenology using an improved shape model on time-series vegetation index in wheat. Computers and Electronics in Agriculture. 2020, 173: 105398. https://doi.org/10.1016/j.compag.2020.105398
17. Jia M, Li D, Colombo R, Wang Y, Wang X, Cheng T, Zhu Y, Yao X*, Xu C, Ouer G, Li H, Zhang C. Quantifying chlorophyll fluorescence parameters from hyperspectral reflectance at the leaf scale under various nitrogen treatment regimes in winter wheat. Remote Sensing. 2019, 11: 2838. https://doi.org/10.3390/rs11232838
18. Jia M, Li W, Wang K, Zhou C, Cheng T, Tian Y, Zhu Y, Cao W, Yao X*. A newly developed method to extract the optimal hyperspectral feature for monitoring leaf biomass in wheat. Computers and Electronics in Agriculture. 2019, 165: 104942. https://doi.org/10.1016/j.compag.2019.104942.
19. Li W, Jiang J, Guo T, Zhou M, Tang Y, Wang Y, Zhang Y, Cheng T, Zhu Y, Cao W, Yao X*. Generating Red-Edge images at 3 M spatial resolution by fusing Sentinel-2 and Planet satellite products. Remote Sensing. 2019, 11(12):1422. https://doi.org/10.3390/rs11121422
20. Jiang J, Cai W, Zheng H, Cheng T, Tian Y, Zhu Y, Ehsani R, Hu Y, Niu Q, Gui L, Yao X*. Using digital cameras on an unmanned aerial vehicle to derive optimum color vegetation indices for leaf nitrogen concentration monitoring in winter wheat. Remote Sensing. 2019, 11: 2667. https://doi.org/10.3390/rs11222667
21. Jiang J, Zheng H, Ji X, Cheng T, Tian Y, Zhu Y, Cao W, Ehsani R, Yao X*. Analysis and evaluation of the image preprocessing process of a six-band multispectral camera mounted on an unmanned aerial vehicle for winter wheat monitoring. Sensors. 2019, 19, 747. https://doi.org/10.3390/s19030747
22. Guo T, Fang Y, Cheng T, Tian Y, Zhu Y, Chen Q, Qiu X, Yao X*. Detection of wheat height using optimized multi-scan mode of LiDAR during the entire growth stages. Computers and Electronics in Agriculture. 2019, 165: 104959. https://doi.org/10.1016/j.compag.2019.104959
23. Cao Z, Yao X, Liu H, Liu B, Cheng T, Tian Y, Cao W, Zhu Y*. Comparison of the abilities of vegetation indices and photosynthetic parameters to detect heat stress in wheat. Agricultural and Forest Meteorology. 2019. 65:121-136. https://doi.org/10.1016/j.agrformet.2018.11.009
24. Zheng H, Li W, Jiang J, Liu Y, Cheng T, Tian Y, Zhu Y, Cao W, Zhang Y, Yao X *. A comparative assessment of different modeling algorithms for estimating leaf nitrogen content in winter wheat using multispectral images from an unmanned aerial vehicle. Remote Sensing. 2018. 10, 2026. https://doi.org/10.339 0/rs10122026
25. Jia M, Zhu J, Ma C, Alonso L, Li D, Cheng T, Tian Y, Zhu Y, Yao X*, Cao W*. Difference and potential of the upward and downward sun-induced chlorophyll fluorescence on detecting leaf nitrogen concentration in wheat. Remote Sensing. 2018, 10(8):1315. https://doi.org/10.3390/rs10081315
26. Yao X, Si HY, Cheng T, Liu Y, Jia M, Tian YC, Chen CY, Liu SY, Chen Q, Zhu Y*. Spectroscopic estimation of leaf dry weight per ground area using vegetation indices and continuous wavelet analysis in wheat. Frontiers in Plant Science. 2018.01,360
27. Yao X, Wang N, Liu Y, Cheng T, Tian YC, Chen Q, Zhu Y. Accurate estimation of LAI with multispectral imagery on unmanned aerial vehicle (UAV) in wheat. Remote sensing, 2017,9,1304. https://doi.org/10.3390/rs9121304
28. Cao Z, Cheng T, Ma X, Tian Y, Zhu Y, Yao X*, Chen Q, Liu S, Guo Z, Zhen Q. A new three-band spectral index for mitigating the saturation in the estimation of leaf area index in wheat. International Journal of Remote Sensing. 2017, 38(13): 3865-3885. https://doi.org/10.1080/01431161.2017.1306141
29. Yao X, Huang Y, Shang G, Zhou C, Cheng T, Tian YC, Cao WX, Zhu Y. Evaluation of six algorithms to monitor wheat leaf nitrogen concentration. Remote Sensing, 2015, 7: 14939-14966. https://doi.org/10.3390/rs71114939
30. Yao X, Ren H, Cao ZH, Tian YC, Cao WX, Zhu Y, Chen T. Monitoring leaf nitrogen content in wheat with canopy hyperspectrum as influenced by soil background. International Journal of Applied Earth Observation and Geoinformation. 2014. 32, 114-124 . https://doi.org/10.1016/j.jag.2014.03.014
31. Yao X, Jia WQ, Si HY, Guo ZQ, Tian YC, Liu XJ, Cao WX, Zhu Y. Monitoring leaf equivalent water thickness based on hyperspectrum in wheat under different water and nitrogen treatments. PLOS ONE. 2014. 9(6):1-11
32. Yao X, Ata-Ul-Karim ST, Zhu Y, Tian YC, Liu XJ, Cao WX. Development of critical nitrogen dilution curve in rice based on leaf dry matter. European Journal of Agronomy. 2014. 55: 20- 28. (SCI) https://doi.org/10.1016/j.eja.2013.12.004
33. Yao X, Zhao B, Tian YC, Liu XJ, Ni J, Cao WX, Zhu Y. Using leaf dry matter to quantify the critical nitrogen dilution curve for winter wheat in eastern China. Field Crops Research. 2014. 159: 33-42. (SCI) https://doi.org/10.1 016/j.fcr.2013.12.007
34. Yao X, Zhu Y, Tian YC, Liu XJ, and Cao WX. Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat. International Journal of Applied Earth Observation and Geoinformation. 2010.12(2): 89-100. (SCI) https://doi.org/10.1016/j.jag.2009.11.008
35. Yao X, Feng W, Zhu Y, Tian YC, and Cao WX. A non-destructive and real-time method of monitoring leaf nitrogen status in wheat. New Zealand of Agricultural Research. 2007. 50: 935-942. (SCI) https://doi.org/10.1080/00288230709510370
36. Zhao B, Yao X, Tian YC, Liu XJ, Ata-UI-Karim ST, Ni J, Cao WX, Zhu Y*. New critical nitrogen curve based on leaf area index for winter wheat. Agronomy Journal. 2014. 106(2):379-389. (SCI) https://doi.org/10.2134/agronj2013.0213
37. Ata-Ul-Karim ST, Yao X, Liu XJ, Cao WX, Zhu Y*.Development of critical nitrogen dilution curve of Japonica rice in Yangtze River Reaches. Field Crops Research. 2013.149:149-158. (SCI) https://doi.org/10.1016/j.fcr.2013.03.012
38. Yao XF, Yao X, Jia WQ, Tian YC, Ni J, Cao WX, Zhu Y*. Comparison and intercalibration of vegetation indices from different sensors for monitoring plant nitrogen uptake in wheat. Sensors.2013.13(3):3109-3130 (SCI) https://doi. org/10.3390/s130303109
39. Yao XF, Yao X, Tian YC, Ni J, Cao WX, Zhu Y*. A new method to determine central wavelength and optimal bandwidth for predicting plant nitrogen uptake in wheat. Journal of Integrative Agriculture. 2013. 12(5): 101-115. (SCI) https://doi.org/10.1016/S2095-3119(13)60300-7
40. Wang W, Yao X, Yao XF, Tian YC, Liu XJ, Ni J, Cao WX and Zhu Y. Estimating leaf nitrogen concentration with three-band vegetation indices in rice and wheat. Field Crops Research. 2012. 129: 90-98. (SCI) https://doi.org/10.1016/j.fc r.2012.01.014
41. Wang W, Yao X, Liu XJ, Tian YC, Ni J, Cao WX and Zhu Y*. Common spectral bands and optimum vegetation indices for monitoring leaf nitrogen accumulation in rice and wheat. Journal of Integrative Agriculture. 2012.11(12): 101-108. (SCI) https://doi.org/10.1016/S2095-3119(12)60457-2
42. Tian YC, Yao X, Yang J, Cao WX, Hannaway DB, Zhu Y. 2011. Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance. Field Crops Research, 120: 299-310. (SCI) https://doi.org/10.1016/j.fcr.2010.11.002
43. Feng W, Yao X, Zhu Y, Tian YC, Cao WX. 2008. Monitoring leaf nitrogen status with hyperspectral reflectance in wheat. European Journal of Agronomy. (28): 394-404. (SCI) https://doi.org/10.1016/j.eja.2007.11.005
44. Feng W, Yao X, Tian YC, Cao WX, and Zhu Y. 2008. Monitoring leaf pigment status with hyperspectral remote sensing in wheat. Australian Journal of Agricultural Research. (59): 748-760. (SCI) DOI:10.1071/AR07282
45. Zhu Y, Yao X, Tian YC, Liu XJ, Cao WX. 2008. Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice. International Journal of Applied Earth Observation and Geoinformation. (10): 1-10. (SCI) https://doi.org/10.1016/j.jag.2007.02.006
中文期刊
46. 蔡苇荻，张羽，刘海燕，郑恒彪，程涛，田永超，朱艳，曹卫星，姚霞*. 基于成像高光谱的小麦冠层白粉病早期监测方法.中国农业科学，2022，55(06):1110-1126.Doi: 10.3864/j.issn.0578-1752.2022.06.005
47. 张羽，杨涛，马吉锋，黄宇，郑恒彪，程涛，田永超，朱艳，姚霞*. 数学形态学辅助下基于光谱指数的作物冠层组分分类.农业工程学报，2022，38(07):163-170. DOI: 10.11975/j.issn.1002-6819.2022.07.018
48. 邱小雷，方圆，郭泰，程涛，朱艳，姚霞*. 基于地基LiDAR高度指标的小麦生物量监测研究.农业机械学报，2019，50(10):159-166
49. 姚霞, 王雪, 黄宇, 汤守鹏, 田永超, 朱艳*, 曹卫星. 应用近红外光谱法估测小麦叶片糖氮比. 应用生态学报, 2015，26(8): 2371-2378.
50. 姚霞 ,刘小军, 田永超, 曹卫星, 朱艳*, 张羽. 基于星载通道光谱指数与小麦冠层叶片氮素营养指标的定量关系. 应用生态学报, 2013，24(2): 431-437.
51. 姚霞，田永超，倪军，张玉森，曹卫星，朱艳．水稻叶片色素含量近红外光谱估测模型研究.分析化学. 2012，40(4). 589-595. (SCI) DOI: 10.3724/SP.J.1096.2012.10325
52. 姚霞，刘小军，王薇，倪军，曹卫星，朱艳．小麦氮素无损监测仪敏感波长的最佳波段宽度研究．农业机械学报．2011，42（2）:162-167. (EI)
53. 姚霞，汤守鹏，田永超，曹卫星，朱艳．应用近红外光谱估测小麦叶片氮含量. 植物生态学报. 2011，35 (8): 844-852. DOI: 10.3724/SP.J.1258.2011.00844
54. 姚霞，田永超，刘小军，曹卫星，朱艳．不同算法红边位置监测小麦冠层氮素营养指标的比较．中国农业科学．2010，43(13)：2661-2667．DOI: 10.3864/j.issn.0578-1752.2010.13.005
55. 姚霞，刘小军，王薇，田永超，曹卫星，朱艳.基于减量精细采样法探究估算小麦叶片氮积累量的最佳归一化光谱指数．应用生态学报．2010，21(12)：3175-3182．DOI: http://www.cjae.net/CN/abstract/abstract3937.shtml
56. 姚霞，朱艳，冯伟，田永超，曹卫星．监测小麦叶片氮积累量的新高光谱特 征波段及比值植被指数．光谱学与光谱分析．2009，29(8)：2191-2195. (SCI/EI) 10.3964/j.issn.1000-0593(2009)08-2191-05
57. 姚霞，朱艳，田永超，冯伟，曹卫星．小麦叶层氮含量估测的最佳高光谱参数研究．中国农业科学．2009，42(8)：2716-2725.DOI: 10.3864/j.issn.0578-1752.2009.08.010
58. 姚霞，吴华兵，朱艳，田永超，周治国，曹卫星．棉花功能叶片色素含量与高光谱参数的相关性研究．棉花学报．2007，19(4)：267-272．
59. 冯伟，姚霞，田永超，朱艳，李映雪，曹卫星．基于高光谱遥感的小麦叶片糖氮比监测．中国农业科学．2008，41(6)：1630-1639．DOI: 10.3864/j.issn.0578-1752.2008.06.008
60. 冯伟，姚霞，田永超，朱艳，刘小军，曹卫星．小麦籽粒蛋白质含量高光谱预测模型研究．作物学报．2007，33(12)：1935-1942．
61. 张玉森，姚霞，田永超，曹卫星，朱艳．应用近红外光谱预测水稻叶片氮含量．植物生态学报．2010，34(6)：704-712．DOI: 10.3773/j.issn.1005-264x.2010.06.010
国家发明专利
1. 一种缓解LCC与秸秆-土壤背景影响的小麦LAI估算方法, ZL20231 0400044.2
2. 一种基于全卷积神经网络的田间小区自动分割方法，202310400046.1
3. 一种适用于棉花出苗早期的快速、高效计数方法，202311454183.X
4. 一种基于三维点云耦合法向量夹角和阈值分割法的田间小麦穗数提取方法，202211499179.0
5. 一种基于高分影像和机器学习的高海拔地区作物分类识别的方法，202210267472.8
6. 一种基于日光诱导叶绿素荧光指数的水稻盐胁迫早期定量监测方法，202211270765.8
7. 一种基于体素插值的均值漂移算法的田间小麦茎蘖数提取方法，202211499194.5
8. 一种基于无人机RGB影像的小麦物候期实时分类方法，202210414686.3
9. 基于RGB图像融合特征的小麦叶层氮含量估测方法，ZL202011303935.9，
10. 一种基于Sentinel-2红边区域多光谱信息改善小麦前期叶面积指数估算的方法，ZL 2021101720495，
11. 一种田间小麦茎蘖数提取方法，ZL201910223270.1
12. 一种基于三波段植被指数的小麦叶面积指数估算模型的构建方法， ZL201610803703.7
13. 一种基于连续小波分析建立小麦叶干重定量模型的方法， ZL201611116173.5
14. 一种面向田块尺度作物生长监测的遥感影像时空融合方法， ZL201811312555.4
15. 一种基于无人机多光谱影像的水稻地上部生物量估测方法， ZL201811267697.3
16. 田间作物表型监测机器人，ZL201811273308.8,
17. Crop growth sensing apparatus and method supporting agricultural machinery variable quantity fertilization operations，US6540039
18. 一种不同PNC水平下小麦植株含水率的监测模型和方法， ZL2013104226074
19. 一种土壤背景干扰下小麦叶层氮含量光谱监测模型及建模方法， ZL201310227380.8
20. 一种小麦叶片等效水厚度高光谱监测方法，ZL201310382064.8
21. 一种基于三波段光谱指数估测植物氮含量的方法，ZL 201110278513.5
22. 一种基于光谱技术的小麦叶片糖氮比快速检测方法，ZL 201010543330.7
23. 一种稻麦叶片氮含量光谱监测模型建模方法，ZL 201110033113.8
24. 一种确定小麦植株吸氮量核心波段的方法，ZL201210109597.4
25. 一种基于冠层高光谱指数的小麦植株水分监测方法，ZL 201110368757.2
26. 一种不同植株氮含量水平下小麦植株含水率的监测方法，ZL 201310422607.4
27. 一种根据小麦植株吸氮量核心波长确定适宜带宽的方法，ZL201210109596.x
28. 基于MCMC的小麦品种特征参数估算方法，ZL2011112100035840
29. 一种田间作物生长信息无损快速检测装置及检测方法，ZL201110030031.8,
30. 机载作物氮素信息高密度无损采集方法，ZL200910034988.2
31. 便携式多通道作物叶片氮素营养指标无损监测，ZL 200710019340.9
表彰/奖励
1. 南京农业大学钟山青年骨干，2023年
2. 全球农情遥感监测关键技术与应用，测绘科学技术一等奖（第7），2023年
3. 南京农业大学研究生教育“优秀导师团队”（第2），2022年
4. 地理信息科技进步奖(本单位第1），2022年
5. 神农中华农业科技奖优秀创新团队奖（第4），2021年
6. Multi-rotor wing unmanned aerial vehicle platform-based crop growth monitoring method and device. 瑞士日内瓦国际发明奖银奖（第三），2021年
7. 稻麦生长指标光谱监测与定量诊断技术．国家科技进步二等奖，2015年
8. 稻麦生长指标光谱监测与定量诊断技术．江苏省科技进步一等奖，2014年
9. 基于模型的作物生长预测与精确管理技术．国家科技进步二等奖，2008年
10. 作物管理知识模型系统的构建与应用．中国高校科技进步一等奖，2007年
计算机软件著作权
1. 智慧农作管理系统Android版V2.0，2018SR170058， 2018年
2. 基于遥感的作物生长监测诊断系统,V1.0，2008SR36903.2008年
3. 高光谱数据分析与处理系统V1.0,2011R11L0310250．2011年
4. 基于消费级无人机的稻麦生长监测系统【简称：稻麦生长监测系统】V1.0,2019SR0082467,2019-01-23.
5. 基于嵌入式GIS的小麦精确播种施肥智能控制软件【简称：小麦智能控制软件】V1.0,2019SR0116074,2019-01-31.
毕业研究生及课题名称
博士研究生
1. 李 伟 秸秆还田背景下冬小麦主产区LAI时空分布遥感监测
2. 印玉明 基于日光诱导叶绿素荧光估算高温干旱胁迫下的小麦生产力研究
3. 谷洋洋 基于激光雷达的小麦关键光合表型参数及产量高通量估算研究
4. 朱 杰 基于日光诱导叶绿素荧光的小麦光合生产力估算研究
5. 郭 泰 基于地基激光雷达的小麦冠层生长参数估算方法研究
6. Haider Detection of wheat powdery mildew based on hyperspectral remote sensing at the leaf and canopy scales,2021年毕业
7. 曹中盛 高温胁迫下小麦花后功能叶片衰老的高光谱监测诊断技术研究
8. 贾 敏 基于日光诱导叶绿素荧光的小麦叶片氮素营养监测研究
硕士研究生
9. 蔡韦荻 中国近40年小麦主产区物候变化及气候响应研究
10. 韩晓旭 基于智能算法的种质资源小区边界提取算法研究
11. 方 圆 基于地基激光雷达的小麦茎蘖数和穗数估测研究
12. 刘海燕 基于RGB图像和深度学习的小麦叶部主要病害识别研究
13. 王 颖 基于Sentinel-1和Sentinel-2影像的小麦冠层含水量估算研究
14. 汪康康 基于日光诱导叶绿素荧光监测水稻盐胁迫研究
15. 周 萌 基于高时空分辨率的卫星和无人机影像的小麦生育期监测研究
16. 李 伟 融合Sentinel-2和Planet影像监测秸秆还田背景下的小麦LAI
17. 马春晨 基于日光诱导叶绿素荧光的水稻叶绿素含量和干旱胁迫监测研究
18. 刘红艳 基于成像高光谱的小麦白粉病监测研究
19. 翟苗苗 基于地面LiDAR结合的小麦群体结构和生长参数的反演
20. 马 雪 基于时序光谱信息的小麦生育期监测研究
21. 张 晓 基于地面激光雷达的小麦点云预处理技术及株高监测研究
22. 王文雁 基于高光谱的小麦白粉病监测研究
23. 刘 勇 基于无人机平台的小麦冠层叶片氮素营养监测研究
24. 周 陈 基于近地面成像高光谱的小麦叶片生物量敏感光谱特征选择和模型构建研究
25. 周晓双 基于高光谱的稻麦叶面积指数监测研究
26. 司海洋 基于水分处理下的小麦叶片衰老高光谱监测研究
27. 庞方荣 基于无线传感器网络的农田信息自动获取技术研究
28. 郭子卿 基于植气温度指标的小麦水分无损监测研究
专业学会
1. IEEE, Geoscience and Remote Sensing Society
2. Union of RS and GIS in Jiangsu province, China
其他社会职务
l 南京农业大学智慧农业研究院副院长
l 智慧农业教育部工程中心 副主任
l 全国农业专业学位研究生教育指导委员农业工程与信息技术领域协作组助理秘书
l 智慧农业系主任
Update:2024/03/01