JIANG Jie,ZHANG Zeyu,CAO Qiang,et al.Use of a digital camera mounted on a consumer-grade unmanned aerial vehicle to monitor the growth status of wheat[J].Journal of Nanjing Agricultural University,2019,42(4):622-631.[doi:10.7685/jnau.201904065]





Use of a digital camera mounted on a consumer-grade unmanned aerial vehicle to monitor the growth status of wheat
江杰 张泽宇 曹强 田永超 朱艳 曹卫星 刘小军
南京农业大学国家信息农业工程技术中心/农业农村部作物系统分析与决策重点实验室/江苏省信息农业重点实验室/江苏省现代作物生产协同创新中心, 江苏 南京 210095
JIANG Jie ZHANG Zeyu CAO Qiang TIAN Yongchao ZHU Yan CAO Weixing LIU Xiaojun
National Engineering and Technology Center for Information Agriculture/Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs/Jiangsu Key Laboratory for Information Agriculture/Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
wheatgrowth statusnitrogen nutritionunmanned aerial vehicledigital camera
[目的]本文旨在探究消费级无人机搭载数码相机更好地用于小麦长势快速监测。[方法]于2015—2017年开展涉及2个小麦品种和4个施氮水平处理的田间小区试验,在小麦关键生育期采用大疆精灵3专业版无人机自带的数码相机获取试验区数码影像,并提取6种颜色指数,同步取样并测定叶面积指数、叶片干物质量及叶片氮积累量等小麦长势信息,在小麦抽穗前、后及全生育期分别运用指数函数和随机森林算法定量分析长势信息与颜色指数的关系。[结果]在小麦各生长阶段,指数函数模型表现较好,可见光大气阻抗指数(visible atmospherically resistant index,VARI)、超红指数(excess red index,ExR)和归一化绿减红差值指数(normalized green minus red difference index,NGRDI)与叶面积指数、叶片干物质量和叶片氮积累量的相关性均表现较好,继而分别建立了基于VARI、ExR和NGRDI的叶面积指数(R2=0.71~0.82)、叶片干物质量(R2=0.42~0.71)和叶片氮积累量(R2=0.52~0.76)的指数函数监测模型。独立试验数据的检验结果表明:在抽穗前及全生育期,ExR(R2=0.45~0.70和0.42~0.62)监测模型估测的叶面积指数、叶片干物质量和叶片氮积累量与实测值拟合性更好,在抽穗后期,VARI(R2=0.68~0.72)监测模型估测效果更好。[结论]结合小麦各生长阶段指数函数监测模型,利用无人机搭载数码相机可以快速无损监测小麦长势状况。
[Objectives]This paper aims to explore better monitoring the growth status of wheat using consumer-grade unmanned aerial vehicle(UAV)mounted a digital camera.[Methods]Field plot experiments involving two wheat varieties and four nitrogen(N)rates were conducted during 2015-2017,the DJI Phantom 3 professional UAV built-in camera was used to collect the experimental field digital images at wheat key growth stages,six color indices were extracted from the digital images,and three growth parameters including leaf area index(LAI),leaf dry weight(LDW)and leaf N accumulation(LNA)were measured synchronously. The quantitative relationships between growth information and six color indices were systematically analyzed by using exponential function and random forest algorithm at pre- and post-heading and across all growth stages.[Results]The results indicated that the exponential model performed better than random forest model at different growth stages,visible atmospherically resistant index(VARI),excess red index(ExR),and normalized green minus red difference index(NGRDI)were highly correlated with LAI,LDW and LNA,and then monitoring models in exponential form on LAI (R2=0.71-0.82),LDW(R2=0.42-0.71),and LNA (R2=0.52-0.76)were constructed with VARI,ExR and NGRDI,respectively. The validation of the predicting models using independent dataset showed that the above linked models gave accurate LAI,LDW,and LNA estimation,while LAI,LDW,and LNA estimated by the ExR (R2=0.45-0.70 and 0.42-0.62)model matched better with measured values at pre-heading and across all growth stages,and by the VARI(R2=0.68-0.72)model matched better with measured values at post-heading stages.[Conclusions]Therefore,this study demonstrated that combining the exponential monitoring models at pre- and post-heading and across all growth stages,the UAV-mounted digital camera system is able to rapidly and nondestructively monitor the growth status of wheat.


[1] Singh B,Singh V,Singh Y S,et al. Site-specific fertilizer nitrogen management using optical sensor in irrigated wheat in the Northwestern India[J]. Agricultural Research,2017,6(2):1-10.
[2] 王来刚,田永超,朱艳,等. 不同时空分辨率遥感数据融合估算冬小麦叶面积指数[J]. 农业工程学报,2012,28(17):117-124. Wang L G,Tian Y C,Zhu Y,et al. Estimation of winter wheat leaf area index by fusing different spatial and temporal resolution remote sensing data[J]. Transactions of the Chinese Society of Agricultural Engineering,2012,28(17):117-124(in Chinese with English abstract).
[3] Lamb D W,Schneider D A,Stanley J N. Combination active optical and passive thermal infrared sensor for low-level airborne crop sensing[J]. Precision Agriculture,2014,15(5):523-531.
[4] Liu X J,Ferguson R B,Zheng H B,et al. Using an active-optical sensor to develop an optimal NDVI dynamic model for high-yield rice production(Yangtze,China)[J]. Sensors,2017,17(4):672.
[5] 周晓楠,黄正来,张文静,等. 基于双波段光谱仪CGMD-302的小麦叶面积指数和叶干重监测[J]. 中国农业大学学报,2017,22(1):102-111. Zhou X N,Huang Z L,Zhang W J,et al. Monitoring leaf area index and leaf dry weight of winter wheat with dual-wavebands spectrometer CGMD-302[J]. Journal of China Agricultural University,2017,22(1):102-111(in Chinese with English abstract).
[6] Zhou X,Zheng H B,Xu X Q,et al. Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2017,130:246-255.
[7] Liu H Y,Zhu H C,Wang P. Quantitative modelling for leaf nitrogen content of winter wheat using UAV-based hyperspectral data[J]. International Journal of Remote Sensing,2017,38(8/9/10):2117-2134.
[8] Krienke B,Ferguson R B,Schlemmer M,et al. Using an unmanned aerial vehicle to evaluate nitrogen variability and height effect with an active crop canopy sensor[J]. Precision Agriculture,2017,18(3):1-16.
[9] 贾彪,马富裕. 不同氮素处理棉花冠层图像颜色特征分析[J]. 江苏农业科学,2016,44(9):344-351. Jia B,Ma F Y. Analysis of color characteristics of cotton canopy images under different nitrogen rates[J]. Jiangsu Agricultural Sciences,2016,44(9):344-351(in Chinese).
[10] 李明,张长利,房俊龙. 基于图像处理技术的小麦叶面积指数的提取[J]. 农业工程学报,2010,26(1):205-209,386. Li M,Zhang C L,Fang J L. Extraction of leaf area index of wheat based on image processing technique[J]. Transactions of the Chinese Society of Agricultural Engineering,2010,26(1):205-209,386(in Chinese with English abstract).
[11] Zhang J,Yang C H,Zhao B Q,et al. Crop classification and LAI estimation using original and resolution-reduced images from two consumer-grade cameras[J]. Remote Sensing,2017,9(10):1054.
[12] Hunt E R,Jr,Cavigelli M,Daughtry C S T,et al. Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status[J]. Precision Agriculture,2005,6(4):359-378.
[13] Zhang J Y,Liu X,Liang Y,et al. Using a portable active sensor to monitor growth parameters and predict grain yield of winter wheat[J]. Sensors,2019,19(5):1108.
[14] Wang L A,Zhou X D,Zhu X K,et al. Estimation of biomass in wheat using random forest regression algorithm and remote sensing data[J]. The Crop Journal,2016,4(3):212-219.
[15] Yang S X,Feng Q S,Liang T G,et al. Modeling grassland above-ground biomass based on artificial neural network and remote sensing in the Three-River Headwaters Region[J]. Remote Sensing of Environment,2018,204:448-455.
[16] Woebbecke D M,Meyer G E,von Bargen K,et al. Color indices for weed identification under various soil,residue,and lighting conditions[J]. Transactions of the ASAE,1995,38:259-269.
[17] Meyer G E,Hindman T W,Laksmi K. Machine vision detection parameters for plant species identification[J]. Proceedings of SPIE:The International Society for Optical Engineering,1999,3543:327-335.
[18] Meyer G E,Neto J C. Verification of color vegetation indices for automated crop imaging applications[J]. Computers and Electronics in Agriculture,2008,63(2):282-293.
[19] Gitelson A A,Kaufman Y J,Stark R,et al. Novel algorithms for remote estimation of vegetation fraction[J]. Remote Sensing of Environment,2002,80(1):76-87.
[20] Louhaichi M,Borman M M,Johnson D E. Spatially located platform and aerial photography for documentation of grazing impacts on wheat[J]. Geocarto International,2001,16(1):65-70.
[21] Yuan H H,Yang G J,Li C C,et al. Retrieving soybean leaf area index from unmanned aerial vehicle hyperspectral remote sensing:analysis of RF,ANN,and SVM regression models[J]. Remote Sensing,2017,9(4):309.
[22] Li Z W,Wang J H,Tang H,et al. Predicting grassland leaf area index in the meadow steppes of northern China:a comparative study of regression approaches and hybrid geostatistical methods[J]. Remote Sensing,2016,8(8):632.
[23] 李亚兵,毛树春,韩迎春,等. 不同棉花群体冠层数字图像颜色变化特征研究[J]. 棉花学报,2012,24(6):541-547. Li Y B,Mao S C,Han Y C,et al. Study on the color characteristics variation of cotton canopy digital images[J]. Cotton Science,2012,24(6):541-547(in Chinese with English abstract).
[24] Pérez A J,López F,Benlloch J V,et al. Colour and shape analysis techniques for weed detection in cereal fields[J]. Computers and Electronics in Agriculture,2000,25(3):197-212.
[25] 丁雷龙,李强子,杜鑫,等. 基于无人机图像颜色指数的植被识别[J]. 国土资源遥感,2016,28(1):78-86. Ding L L,Li Q Z,Du X,et al. Vegetation extraction method based on color indices from UAV images[J]. Remote Sensing for Land & Resources,2016,28(1):78-86(in Chinese with English abstract).
[26] 牛庆林,冯海宽,杨贵军,等. 基于无人机数码影像的玉米育种材料株高和LAI监测[J]. 农业工程学报,2018,34(5):73-82. Niu Q L,Feng H K,Yang G J,et al. Monitoring plant height and leaf area index of maize breeding material based on UAV digital images[J]. Transactions of the Chinese Society of Agricultural Engineering,2018,34(5):73-82(in Chinese with English abstract).
[27] Shibayama M,Sakamoto T,Takada E,et al. Continuous monitoring of visible and near-infrared band reflectance from a rice paddy for determining nitrogen uptake using digital cameras[J]. Plant Production Science,2009,12(3):293-306.
[28] 马玥,姜琦刚,孟治国,等. 基于随机森林算法的农耕区土地利用分类研究[J]. 农业机械学报,2016,47(1):297-303. Ma Y,Jiang Q G,Meng Z G,et al. Classification of land use in farming area based on random forest algorithm[J]. Transactions of Chinese Society for Agricultural Machinery,2016,47(1):297-303(in Chinese with English abstract).
[29] 姚雄,余坤勇,杨玉洁,等. 基于随机森林模型的林地叶面积指数遥感估算[J]. 农业机械学报,2017,48(5):159-166. Yao X,Yu K Y,Yang Y J,et al. Estimation of forest leaf area index based on random forest model and remote sensing data[J]. Transactions of the Chinese Society for Agricultural Machinery,2017,48(5):159-166(in Chinese with English abstract).
[30] 王云飞,庞勇,舒清态. 基于随机森林算法的橡胶林地上生物量遥感反演研究——以景洪市为例[J]. 西南林业大学学报,2013,33(6):38-45,311. Wang Y F,Pang Y,Shu Q T. Counter-estimation on aboveground biomass of Hevea brasiliensis plantation by remote sensing with random forest algorithm:a case study of Jinghong[J]. Journal of Southwest Forestry University,2013,33(6):38-45,311(in Chinese with English abstract).
[31] 岳继博,杨贵军,冯海宽. 基于随机森林算法的冬小麦生物量遥感估算模型对比[J]. 农业工程学报,2016,32(18):175-182. Yue J B,Yang G J,Feng H K. Comparative of remote sensing estimation models of winter wheat biomass based on random forest algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering,2016,32(18):175-182(in Chinese with English abstract).
[32] 贺佳,刘冰锋,李军. 不同生育时期冬小麦叶面积指数高光谱遥感监测模型[J]. 农业工程学报,2014,30(24):141-150. He J,Liu B F,Li J. Monitoring model of leaf area index of winter wheat based on hyperspectral reflectance at different growth stages[J]. Transactions of the Chinese Society of Agricultural Engineering,2014,30(24):141-150(in Chinese with English abstract).
[33] Cao Q,Mao Y,Shen J,et al. Evaluating two crop circle active canopy sensors for in-season diagnosis of winter wheat nitrogen status[J]. Agronomy,2018,8:201.
[34] Sakamoto T,Shibayama M,Kimura A,et al. Assessment of digital camera-derived vegetation indices in quantitative monitoring of seasonal rice growth[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2011,66(6):872-882.
[35] 刘昌华,王哲,陈志超,等. 基于无人机遥感影像的冬小麦氮素监测[J]. 农业机械学报,2018,49(6):207-214. Liu C H,Wang Z,Chen Z C,et al. Nitrogen monitoring of winter wheat based on unmanned aerial vehicle remote sensing image[J]. Transactions of the Chinese Society for Agricultural Machinery,2018,49(6):207-214(in Chinese with English abstract).
[36] 田明璐,班松涛,常庆瑞,等. 基于低空无人机成像光谱仪影像估算棉花叶面积指数[J]. 农业工程学报,2016,32(21):102-108. Tian M L,Ban S T,Chang Q R,et al. Use of hyperspectral images from UAV-based imaging spectroradiometer to estimate cotton leaf area index[J]. Transactions of the Chinese Society of Agricultural Engineering,2016,32(21):102-108(in Chinese with English abstract).
[37] Zheng H B,Cheng T,Li D,et al. Combining unmanned aerial vehicle(UAV)-based multispectral imagery and ground-based hyperspectral data for plant nitrogen concentration estimation in rice[J]. Frontiers in Plant Science,2018,9:936.


 LI Pei-pei,ZHANG Xiao,PAN Xiao-mei,et al.His-tag fusion expression,purification and TMV resistance induction of truncated wheat cold shock protein gene fragment TA3-13[J].Journal of Nanjing Agricultural University,2010,33(4):54.[doi:10.7685/j.issn.1000-2030.2010.01.011]
 YANG Tie-gang,DAI Ting-bo,CAO Wei-xing.Effects of nitrogen rates on assimilation and translocation of carbon and nitrogen after anthesis in two wheat cultivars[J].Journal of Nanjing Agricultural University,2008,31(4):6.[doi:10.7685/j.issn.1000-2030.2008.02.002]
 ZHANG Min,DAI Ting-bo,JIANG Dong,et al.Effects of 6-BA on carbon and nitrogen assimilate translocation after anthesis and quality formation in wheat[J].Journal of Nanjing Agricultural University,2006,29(4):6.[doi:10.7685/j.issn.1000-2030.2006.04.002]
 WANG Li-fang,DAI Ting-bo,JING Qi,et al.Effect of sowing dates on pentosan content in wheat grains at different ecological sites[J].Journal of Nanjing Agricultural University,2006,29(4):11.[doi:10.7685/j.issn.1000-2030.2006.04.003]
 JING Qi,DAI Ting bo,JIANG Dong,et al.Characteristics of accumulation and translocation of dry matter and nitrogen in wheat genotypes under different environments[J].Journal of Nanjing Agricultural University,2004,27(4):1.[doi:10.7685/j.issn.1000-2030.2004.01.001]
[8]张江丽,蔡士宾,张光祥,等.马卡小麦耐湿性的RAPD 标记及定位研究[J].南京农业大学学报,2003,26(2):7.[doi:10.7685/j.issn.1000-2030.2003.02.002]
 ZHANG Jiang li,CAI Shi bin,ZHANG Guang xiang,et al.Genetic mapping of genes conferring waterlogging-tolerance in Triticum macha using RAPD markers[J].Journal of Nanjing Agricultural University,2003,26(4):7.[doi:10.7685/j.issn.1000-2030.2003.02.002]
 CHEN Pei du,ZHANG Shou zhong,WANG Xiu e,et al.New wheat variety Nannong 9918 with high yield and powdery mildew resistance[J].Journal of Nanjing Agricultural University,2002,25(4):105.[doi:10.7685/j.issn.1000-2030.2002.04.025]
 ZHOU Qin,JIANG Dong,DAI Ting bo,et al.Grain protein and starch accumulation and its relationship to remobilization of carbon and nitrogen in different wheat genotypes[J].Journal of Nanjing Agricultural University,2002,25(4):1.[doi:10.7685/j.issn.1000-2030.2002.03.001]


更新日期/Last Update: 1900-01-01