本文選題：無人機(jī) + 遙感��； 參考：《農(nóng)業(yè)工程學(xué)報》2017年01期

【摘要】：為探討無人機(jī)載高光譜空間尺度對大豆產(chǎn)量預(yù)測精度的影響,該文以山東嘉祥圣豐大豆為研究對象,設(shè)計以多旋翼無人機(jī)為平臺搭載Cubert UHD185成像高光譜傳感器的無人機(jī)遙感農(nóng)情監(jiān)測系統(tǒng),獲取了大豆多個生育期的無人機(jī)高光譜數(shù)據(jù)。首先,該研究利用盛莢期-始粒期(R4-R5期)的高光譜影像,由21個不同光譜空間尺度提取的高光譜數(shù)據(jù)構(gòu)建植被指數(shù),通過植被指數(shù)方差分析結(jié)果可知所選冠層植被指數(shù)與不同品種大豆植株的生長狀況密切相關(guān),但是不同空間尺度下的F值仍存在較為明顯的差異;其次,采用偏最小二乘回歸建立產(chǎn)量與不同空間尺度的植被指數(shù)之間的回歸模型,通過模型方程估算精度的曲線變化趨勢進(jìn)一步將最優(yōu)空間尺度面積確認(rèn)至9.03~10.13 m2,即當(dāng)采樣空間尺度區(qū)域長、寬與小區(qū)總長、寬比例介于4.25:5和4.5:5時,所得到的冠層光譜能夠盡可能準(zhǔn)確地估測大豆產(chǎn)量,此時估算產(chǎn)量和實測產(chǎn)量呈極顯著相關(guān)(相關(guān)系數(shù)r=0.811 7,參與建模的樣本個數(shù)270)。該研究可為使用高、低空高光譜影像進(jìn)行作物表型信息解析和估產(chǎn)提供參考。
[Abstract]:In order to investigate the influence of the spatial scale of UAV on the prediction accuracy of soybean yield, this paper designed the UAV remote sensing monitoring system with Cubert UHD185 imaging hyperspectral sensor on the platform of multi rotor unmanned aerial vehicle (UAV) in Shandong, and obtained the high spectrum number of UAV in multiple soybean growth periods. First, the study uses hyperspectral images of the podding stage (R4-R5 phase) to construct vegetation index from hyperspectral data extracted from 21 different spectral spatial scales. Through the analysis of the vegetation index variance, it is found that the selected canopy vegetation index is closely related to the growth of different varieties of soybean plants, but at different spatial scales. There are still obvious differences in the F value. Secondly, the regression model between the yield and the vegetation index of different spatial scales is established by the partial least squares regression. The optimal spatial scale area is further confirmed to 9.03~10.13 M2 by the curve variation trend of the model equation, which is when the spatial scale region is long, wide and small. When the total length, with a wide ratio of 4.25:5 and 4.5:5, the obtained canopy spectrum can estimate the yield of soybean as accurately as possible. At this time, the estimated yield and the measured yield are extremely significant (the correlation coefficient r=0.811 7, the number of samples involved in the modeling is 270). This study can be used to analyze and estimate crop phenotypic information for using high, low altitude hyperspectral images. Provide reference.

【作者單位】： 北京農(nóng)業(yè)信息技術(shù)研究中心;國家農(nóng)業(yè)信息化工程技術(shù)研究中心;南京農(nóng)業(yè)大學(xué)大豆研究所/國家大豆改良中心;
【基金】：國家自然科學(xué)基金項目(61661136003,41471285) 國家重點(diǎn)研發(fā)計劃(2016YFD0300602) 北京市農(nóng)林科學(xué)院科技創(chuàng)新能力建設(shè)項目(KJCX20170423)
【分類號】：S565.1;S127

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