Monitoring of Winter Wheat Aboveground Fresh Biomass Based on Multi-Information Fusion Technology

文献类型： 外文期刊

第一作者： Zheng Ling

作者： Zheng Ling;Dong Da-ming;Zhang Bao-hua;Wang Cheng;Zhao Chun-jiang;Zheng Ling;Zhu Da-zhou

作者机构：

关键词： Multi-information fusion;PLS;Canopy spectral;Machine vision;Winter wheat;Biomass

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2016 年 36 卷 6 期

页码：

收录情况： SCI

摘要： The aim was to find a nondestructive way to improve the accuracy of detecting the winter wheat aboveground fresh biomass(AGFB). In this study, data fusion technology of the spectroscopy technology and the machine vision technology were used to analyze the AGFB and solve the problem that the accuracy of the prediction model of a single technology is not high. In this experiment, canopy spectra and canopy pictures of 93 samples at seeding stage were collected. Canopy spectra and side images of 200 samples at medium and later growth stage were collected. Spectral reflectance as the spectral absorption parameter was used to construct the AGFB prediction models based on the spectra technology at different stages; The wheat coverage were extracted from canopy pictures and side images by using image processing technology to build the AGFB prediction models. Multivariate regression analysis (MRA) and Partial least-squares regression analysis (PLS) were implemented on the feature variables from the spectral information and image information. The results showed that, compared with the individual image model and spectral model, the AGFB prediction models of PLS based on multi-information at different stages shows better performance. At the seeding stage, the determination coefficient (R-2) of PT S models based on multi-information was 0. 881, and the RMSE was 0. 015 kg. The R-2 of PLS models based on multi-information was 0. 791, the RMSE was 0. 059 kg at middle and final stages. It demonstrated that the precision of model based on multi-information fusion technology, which increased utilization of image and spectral information, was improved for AGFB detecting, which is than the individual image model and spectral model.

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