| 基于无人机数码影像的水稻叶面积指数监测 |
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| 引用本文： | 曹中盛,李艳大,黄俊宝,叶春,孙滨峰,舒时富,朱艳,何勇.基于无人机数码影像的水稻叶面积指数监测[J].中国水稻科学,2022,36(3):308-317. |
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| 作者姓名： | 曹中盛 李艳大 黄俊宝 叶春 孙滨峰 舒时富 朱艳 何勇 |
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| 作者单位： | 1.江西省农业科学院 农业工程研究所/江西省智能农机装备工程研究中心/江西省农业信息化工程技术研究中心，南昌 330200;2.南京农业大学/国家信息农业工程技术中心，南京 210095;3.浙江大学 生物系统工程与食品科学学院，杭州 310029 |
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| 基金项目： | 国家重点研发计划资助项目(2016YFD0300608);江西省“双千计划”资助项目;江西省科技计划资助项目(20202BBFL63044);江西省科技计划资助项目(20182BCB22015);江西省科技计划资助项目(20202BBFL63046);江西省科技计划资助项目(20192BBF60052);江西省农业科研协同创新项目(JXXTCXQN202110);江西省农业科学院创新基金博士启动项目(20182CBS001) |
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| 摘 要： | 目的]为探究无人机数码影像监测水稻叶面积指数(Leaf area index,LAI)的可行性,明确利用无人机数码影像监测水稻LAI的最佳时期,构建基于无人机数码影像的水稻LAI监测模型.方法]本研究基于不同品种和施氮量的水稻田间试验,于分蘖期、拔节期、孕穗期、抽穗期和灌浆期测定水稻LAI,同步使用无人机搭载数码相...
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| 关 键 词： | 水稻 叶面积指数 无人机 数码影像 纹理特征 监测模型 |
| 收稿时间： | 2021-07-29 |
| 修稿时间： | 2021-11-04 |
Monitoring Rice Leaf Area Index Based on Unmanned Aerial Vehicle (UAV) Digital Images |
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| CAO Zhongsheng,LI Yanda,HUANG Junbao,YE Chun,SUN Binfeng,SHU Shifu,ZHU Yan,HE Yong.Monitoring Rice Leaf Area Index Based on Unmanned Aerial Vehicle (UAV) Digital Images[J].Chinese Journal of Rice Science,2022,36(3):308-317. |
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| Authors: | CAO Zhongsheng LI Yanda HUANG Junbao YE Chun SUN Binfeng SHU Shifu ZHU Yan HE Yong |
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| Institution: | 1.Institute of Agricultural Engineering, Jiangxi Engineering Research Center of Intelligent Agricultural Machinery Equipment/Jiangxi Engineering Research Center of Information Technology in Agriculture, Jiangxi Academy of Agricultural Sciences, Nanchang 330200, China;2.National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China;3.College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China |
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| Abstract: | 【Obiective】Leaf area index (LAI) is a crucial variable for assessing rice growth, and unmanned aerial vehicle (UAV) digital images can serve as an efficient way to real-time, no-destructive monitoring of crop growth parameters. However, it remains unclear which parameter in digital images can be used to estimate rice LAI. In addition, the optimal growth stage for monitoring is also unknown. 【Method】In this study, the UAV digital images were initially collected from two field experiments encompassing variations over two years with four cultivars at four nitrogen application levels. Then, the relationship between UAV digital image parameters (nine color indices and eight texture features) and rice LAI at different growth stages (tillering stage, jointing stage, booting stage, heading stage and filling stage) were analyzed. 【Result】The results suggested that the early growth stages, including both tillering stage and jointing stage, were suitable for rice LAI monitoring through UAV digital images, and the texture feature variance (VAR) exhibits greatest accuracy in model calibration with a determination coefficient (R2) of 0.7980. In the validation based on independent experiment, this texture feature also performs well with relative root mean square error (RRMSE) of 0.1658 and bias(θ) of 0.1306. 【Conclusion】Taking the accuracy and convenience in application into consideration, we found that the texture feature VAR could be used to monitor rice LAI in early growth stage with estimation models of LAI = 1.1656×exp(0.0174×VAR). |
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| Keywords: | rice leaf area index unmanned aerial vehicle digital image texture feature monitoring model |
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