| 基于小波技术的冬小麦植株组分含水率估测模型 |
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| 引用本文： | 李辰,王延仓,李旭青,杨秀峰,顾晓鹤.基于小波技术的冬小麦植株组分含水率估测模型[J].农业机械学报,2021,52(3):193-201. |
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| 作者姓名： | 李辰 王延仓 李旭青 杨秀峰 顾晓鹤 |
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| 作者单位： | 北京空间机电研究所;北华航天工业学院;北京农业信息技术研究中心 |
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| 基金项目： | 国家重点研发计划项目（2016YFD0300609）、北京市农林科学院科技创新能力建设专项项目（KJCX20170705）、国家自然科学基金项目（41401419）、河北省教育厅青年基金项目（QN2019213）和河北省青年科学基金项目（D2017409021） |
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| 摘 要： | 为研究光谱对冬小麦植株组分含水率的估测能力,分析小波技术对光谱信息的分离规律,以冬小麦冠层光谱数据与相应的冬小麦植株叶片、茎秆、麦穗含水率的测定值为数据源,先采用小波技术分离冬小麦冠层光谱信息,再将分离的光谱信息与冬小麦各植株组分的含水率进行相关性分析,并提取敏感波段;最后利用偏最小二乘算法构建冬小麦植株组分含水率的估测模型,并进行了验证与评价。研究表明:经小波技术分解后,冬小麦冠层光谱内的吸收特征逐分解水平分离至高频信息内,且各分解水平所代表的吸收特征按强度依次分布于高频信息的分解水平H1～H10内;冬小麦麦穗含水率估测模型的精度与稳定性较强,茎秆次之,叶片稳定性最差,说明扬花期后的冬小麦水分供给已不再适合只采用叶片含水率进行评定,应增加或替换检测指标。
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| 关 键 词： | 冬小麦 植株组分 含水率 小波技术 偏最小二乘 |
| 收稿时间： | 2020/5/21 0:00:00 |
Quantitative Inversion of Water Content of Plant Components in Winter Wheat Based on Wavelet Technology |
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| LI Chen,WANG Yancang,LI Xuqing,YANG Xiufeng,GU Xiaohe.Quantitative Inversion of Water Content of Plant Components in Winter Wheat Based on Wavelet Technology[J].Transactions of the Chinese Society of Agricultural Machinery,2021,52(3):193-201. |
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| Authors: | LI Chen WANG Yancang LI Xuqing YANG Xiufeng GU Xiaohe |
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| Institution: | Beijing Institute of Space Mechanics and Electricity;North China Institute of Aerospace Engineering; National Engineering Research Center for Information Technology in Agriculture |
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| Abstract: | In order to study the ability of spectrum to estimate the water content of winter wheat plant components and analyze the separation rule of spectral information by wavelet technology, the canopy spectral information of winter wheat and the corresponding measured values of water content of leaves, stems and ears of winter wheat were used as data sources. Then, the partial least squares (PLS) algorithm was used to construct the estimation model of winter wheat plant component water content, which was verified and evaluated. The results showed that after wavelet technology decomposition, the absorption characteristics of winter wheat canopy spectrum were separated into high frequency information by decomposition level, and the absorption characteristics represented by each decomposition level were distributed in the H1~H10 decomposition level of high-frequency information. The accuracy and stability of the estimation model of winter wheat ear water content was strong, that of the stem was the second, and the leaf stability was the worst. This showed that the current situation of water supply of winter wheat after poplar flowering stage was no longer suitable to use only leaf water content for evaluation, and the detection index should be added or replaced. |
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| Keywords: | winter wheat plant components water content wavelet technology partial least squares |
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