李志伟, 袁婧, 丁为民, 等. 基于高光谱成像技术识别水稻纹枯病[J]. 华南农业大学学报, 2018, 39(6): 97-103. DOI: 10.7671/j.issn.1001-411X.2018.06.015
    引用本文: 李志伟, 袁婧, 丁为民, 等. 基于高光谱成像技术识别水稻纹枯病[J]. 华南农业大学学报, 2018, 39(6): 97-103. DOI: 10.7671/j.issn.1001-411X.2018.06.015
    LI Zhiwei, YUAN Jing, DING Weimin, YANG Hongbing, SHEN Shaoqing, CUI Jialin. Identification of rice sheath blight based on hyperspectral imaging technique[J]. Journal of South China Agricultural University, 2018, 39(6): 97-103. DOI: 10.7671/j.issn.1001-411X.2018.06.015
    Citation: LI Zhiwei, YUAN Jing, DING Weimin, YANG Hongbing, SHEN Shaoqing, CUI Jialin. Identification of rice sheath blight based on hyperspectral imaging technique[J]. Journal of South China Agricultural University, 2018, 39(6): 97-103. DOI: 10.7671/j.issn.1001-411X.2018.06.015

    基于高光谱成像技术识别水稻纹枯病

    Identification of rice sheath blight based on hyperspectral imaging technique

    • 摘要:
      目的  利用高光谱成像技术对水稻纹枯病进行早期的快速无损识别,结合判别分析方法建立相应的鉴别模型。
      方法  以健康和感染纹枯病的水稻幼苗为研究对象,采集叶片和冠层各180个样本的380~1 030 nm波段的360条高光谱图像,剔除明显噪声部分后,以440~943 nm波段作为水稻样本的光谱范围,分别用不同的方法预处理获得水稻叶片的光谱曲线。采用偏最小二乘–判别分析(PLS-DA)对不同预处理的光谱建模。采用MNF算法对冠层的原始光谱数据进行特征信息提取,并基于特征信息建立线性判别分析(LDA)模型和误差反向传播神经网络(BPNN)判别模型。
      结果  标准正态变量变换(SNV)预处理后建立的PLS-DA模型的预测集判别正确率最高,为92.1%。基于特征信息的LAD和BPNN模型的判别结果优于基于全波段的PLS-DA判别模型。基于最小噪声分离变换特征信息提取的BPNN模型取得了最优效果,建模集和预测集正确率分别达99.1%和98.4%。
      结论  采用高光谱成像技术对水稻纹枯病生理特征进行无损鉴别是可行的,本研究为水稻纹枯病的识别提供了一种新方法。

       

      Abstract:
      Objective  To realize the rapid and nondestructive identification of rice sheath blight at the early stage using hyperspectral imaging technology, and establish the corresponding identification model.
      Method  Healthy rice seedlings and rice seedlings infected with sheath blight were used as the research samples. A total of 360 hyperspectral curves of 180 samples were collected separately from leaves and canopy at the wavelength of 380 to 1 030 nm. After eliminating the obvious noise part, the spectra of rice samples were reserved at the wavelength of 440 to 943 nm, and the spectral curves were preprocessed with different treatments. Partial least squares-discriminant analysis (PLS-DA) was used to model different preprocessed spectra. The feature information was extracted from the original canopy spectral data using the MNF algorithm, and a linear discriminant analysis (LDA) model and a back-propagation neural net-work (BPNN) discriminant model were established based on the feature information.
      Result  The prediction accuracy of PLS-DA model after preprocessing by standard normal variate transformation (SNV) was the highest (92.1%). The discriminant results of LAD and BPNN model based on feature information were superior to the PLS-DA discriminant model based on all bands. The BPNN model based on feature information from minimum noise fraction transformation had optimal results. The accuracies of the model set and prediction set were 99.1% and 98.4% respectively.
      Conclusion  It is feasible to identify nondestructively rice sheath blight using hyperspectral imaging technology. Our research provides a new method for identifying rice sheath blight.

       

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