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基于無(wú)人機(jī)遙感影像的冬小麥氮素監(jiān)測(cè)
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國(guó)家自然科學(xué)基金項(xiàng)目(41371105)、河南省軟科學(xué)研究計(jì)劃項(xiàng)目(162400410058)、河南省高等學(xué)校重點(diǎn)科研項(xiàng)目(17A420001、18A420001)、河南省智慧中原地理信息技術(shù)協(xié)同創(chuàng)新中心開(kāi)放項(xiàng)目(2016A002)和河南省高??萍紕?chuàng)新團(tuán)隊(duì)支持計(jì)劃項(xiàng)目(18IRTSTHN008)


Nitrogen Monitoring of Winter Wheat Based on Unmanned Aerial Vehicle Remote Sensing Image
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    摘要:

    精準(zhǔn)氮素管理是一項(xiàng)提高作物氮肥利用效率的有效策略,利用無(wú)人機(jī)遙感技術(shù)精確估測(cè)小麥氮素狀況是必要的。試驗(yàn)在山東省樂(lè)陵市科技小院實(shí)驗(yàn)基地進(jìn)行,利用八旋翼無(wú)人機(jī)搭載Mini-MCA多光譜相機(jī)于2016年獲取冬小麥4個(gè)關(guān)鍵生育時(shí)期(返青期、拔節(jié)期、孕穗期、揚(yáng)花期)冠層多光譜數(shù)據(jù),同步獲取地上部植株樣品并測(cè)定其生物量、吸氮量、氮營(yíng)養(yǎng)指數(shù),及成熟期籽粒產(chǎn)量,根據(jù)各關(guān)鍵生育期與全生育期分別構(gòu)建植被指數(shù)與農(nóng)學(xué)參數(shù)回歸分析模型,評(píng)估基于無(wú)人機(jī)遙感影像的冬小麥氮素營(yíng)養(yǎng)診斷潛力。結(jié)果表明:基于無(wú)人機(jī)遙感影像能夠較好地估測(cè)冬小麥氮素指標(biāo)(R2為0.45~0.96),決定系數(shù)隨著生育期推移而逐漸增大。拔節(jié)期、孕穗期和揚(yáng)花期估產(chǎn)效果接近且具有很好的估測(cè)能力,揚(yáng)花期DATT冪函數(shù)模型對(duì)小麥氮營(yíng)養(yǎng)指數(shù)的解釋能力最強(qiáng)(R2=0.95)。因此,以多旋翼無(wú)人機(jī)為平臺(tái)同步搭載多光譜相機(jī)對(duì)冬小麥有較好的氮素診斷潛力,可利用估測(cè)結(jié)果指導(dǎo)精準(zhǔn)氮肥管理。

    Abstract:

    Accurate nitrogen (N) management is a promising strategy to improve crop N use efficiency. It is important to accurately estimate the state of wheat nitrogen by unmanned aerial vehicle (UAV) remote sensing. The experiment was arranged in science and technology yard base in Laoling City, Shandong Province. The eight-rotor UAV was used to carry a Mini-MCA multispectral camera and collect the wheat canopy spectral data about four key stages (returning green stage, elongation stage, booting stage and flowering stage) of growth and development in 2016. Meanwhile, winter wheat samples of biomass, nitrogen uptake and nitrogen nutrient index were collected and measured synchronously. Grain yield was measured in mature stage. In critical stages and whole stage of different vegetation, indexes and agronomy parameters regression analysis models were established to assess winter wheat nitrogen nutrition diagnostic potential based on UAV remote sensing image. The results showed that it had better estimation of winter wheat nitrogen index (R2 was 0.45~0.96) based on UAV remote sensing image and the decision coefficient was gradually increased with the elapse of growth period. And those in elongation stage, booting stage and flowering stage were similar and had better ability for yield estimation. DATT power function model was the most powerful to explain the wheat nitrogen nutrition index (R2 was 0.95) in flowering stage. Therefore, the platform for multiple UAV rotorcraft synchronization carrying multi-spectral camera had better nitrogen diagnosis potential for winter wheat and it can be used to guide the precise nitrogen fertilizer management.

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劉昌華,王哲,陳志超,周蘭,岳學(xué)智,苗宇新.基于無(wú)人機(jī)遙感影像的冬小麥氮素監(jiān)測(cè)[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2018,49(6):207-214. LIU Changhua, WANG Zhe, CHEN Zhichao, ZHOU Lan, YUE Xuezhi, MIAO Yuxin. 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.

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  • 收稿日期:2018-04-13
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  • 在線發(fā)布日期: 2018-06-10
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