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Estimation of carbon and nitrogen contents in citrus canopy by low-altitude remote sensing

机译:低空遥感估算柑橘冠层碳氮含量

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Abstract: The study aimed to investigate the fast and nondestructive method for detecting carbon and nitrogen content in citrus canopy. The multispectral imagery of Tarocco blood orange (Citrus sinensis L. Osbeck) plant canopy was obtained by a multispectral camera array mounted at an eight-rotor unmanned aerial vehicle (UAV) flying at an altitude of 100 m above the canopy in Wanzhou District of Chongqing Municipality, China. Average spectral reflectance data of the whole canopy, mature leaf areas and young leaves areas were extracted from the imagery. Two spectral pre-processing methods, multiplicative scatter correction (MSC) and standard normal variable (SNV), and two modeling methods, the partial least squares (PLS) and the least squares support vector machine (LS-SVM), were adopted and compared for their prediction accuracy of total content of nitrogen, soluble sugar and starch in the leaves. The results showed that, based on the spectral data extracted from the mature leaves in the multispectral imagery, the PLS model based on the original spectrum obtained a Rp (correlation coefficient) of 0.6469 and RMSEP (root mean squares error of prediction ) of 0.1296, suggested that it was the best for the prediction of total nitrogen content; the PLS model based on MSC (multiplicative scatter correction) spectrum pre-processing was the best for predicting total soluble sugar content (Rp=0.6398 and RMSEP=8.8891); and the LS-SVM model based on MSC was the best for the starch content prediction (Rp=0.6822 and RMSEP=14.9303). The prediction accuracy for carbon and nitrogen contents based on the spectral data extracted from the whole canopy and the young leaves were lower than that from the mature leaves. The results indicate that it is feasible to estimate the carbon and nitrogen contents by low-altitude airborne multispectral images. Keywords: citrus canopy, low-altitude remote sensing, carbon and nitrogen contents, soluble sugar, starch, estimation DOI: 10.3965/j.ijabe.20160905.2246 Citation: Liu X F, Lyu Q, He S L, Yi S L, Hu D Y, Wang Z T, et al. Estimation of carbon and nitrogen contents in citrus canopy by low-altitude remote sensing. Int J Agric & Biol Eng, 2016; 9(5): 149-157.
机译:摘要:本研究旨在探讨一种快速无损检测柑橘冠层中碳和氮含量的方法。塔罗科血橙(Citrus sinensis L.Osbeck)植物冠层的多光谱图像是通过多光谱相机阵列获得的,该阵列安装在八旋翼无人机(UAV)上,该无人机在重庆万州区的冠层上方飞行100 m中国直辖市。从图像中提取了整个冠层,成熟叶区域和幼叶区域的平均光谱反射率数据。比较了两种频谱预处理方法:乘积散射校正(MSC)和标准正态变量(SNV);两种建模方法:偏最小二乘(PLS)和最小二乘支持向量机(LS-SVM)可以预测叶片中氮,可溶性糖和淀粉的总含量。结果表明,基于从多光谱图像中的成熟叶片中提取的光谱数据,基于原始光谱的PLS模型获得的Rp(相关系数)为0.6469,RMSEP(预测的均方根误差)为0.1296,认为这是预测总氮含量的最佳方法;基于MSC(乘法散射校正)光谱预处理的PLS模型最能预测总可溶性糖含量(Rp = 0.6398和RMSEP = 8.8891);基于MSC的LS-SVM模型预测淀粉含量最佳(Rp = 0.6822,RMSEP = 14.9303)。基于全冠层和幼叶提取的光谱数据对碳和氮含量的预测精度低于成熟叶。结果表明,利用低空机载多光谱图像估算碳氮含量是可行的。关键词:柑桔冠层低空遥感碳氮含量可溶性糖淀粉估计DOI:10.3965 / j.ijabe.20160905.2246引文:刘新芳,吕庆,何SL,易SL,胡DY,王中腾等。通过低空遥感估算柑橘冠层中的碳和氮含量。国际农业与生物工程杂志,2016; 9(5):149-157。

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