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基于高光譜成像的玉米收獲后根茬行分割方法
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現(xiàn)代農(nóng)業(yè)產(chǎn)業(yè)技術(shù)體系建設(shè)項目(CARS-03)和中國農(nóng)業(yè)大學(xué)基本科研業(yè)務(wù)費專項資金項目(2020RC025)


Segmentation Method for Maize Stubble Row Based on Hyperspectral Imaging
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    摘要:

    在華北一年兩熟區(qū),利用聯(lián)合收獲機留茬收獲玉米后,玉米根茬行與行間秸稈及裸露地表顏色相近,采用傳統(tǒng)的圖像檢測方法對其進行分割比較困難。針對該問題,采集了利用聯(lián)合收獲機留茬收獲玉米后的根茬行高光譜圖像,以根茬頂端切口為目標(biāo),提出了一種玉米根茬行高光譜圖像的分割方法。首先,對黑白校正后的全波段圖像進行主成分分析,根據(jù)主成分圖像權(quán)重系數(shù)優(yōu)選出3個特征波長,分別為1260、1658、2131nm;然后,對3個特征波長處的圖像再次進行主成分分析,并對所得到的PC2圖像進行單閾值分割;最后,通過中值濾波、形態(tài)學(xué)開運算、根茬行區(qū)域外噪聲濾除對分割結(jié)果進行優(yōu)化。為驗證該分割方法的效果,利用采集的50幅玉米根茬行高光譜圖像進行試驗,并選取分割準(zhǔn)確率、召回率和F1值對分割結(jié)果進行定量評價。結(jié)果表明:該分割方法下的玉米根茬行圖像分割效果較好,分割準(zhǔn)確率、召回率和F1值分別為91.85%、90.49%和91.16%。研究結(jié)果表明基于高光譜成像技術(shù)可對玉米根茬行進行分割。

    Abstract:

    Traditional vision detection methods generally have better image segmentation effect when image foreground and background have obvious chromaticity difference. However, for the maize stubble field harvested by combine harvester, there are other backgrounds besides maize stubble row such as maize residues, naked land surface, and their color are very similar. Therefore, traditional image processing methods are not suitable for the segmentation of maize stubble row. In order to achieve precise and rapid segmentation of maize stubble row, a segmentation method for maize stubble row based on hyperspectral imaging technology was put forward. Firstly, the original hyperspectral image of maize stubble row was corrected by black and white correction algorithm. Then, the principal components analysis algorithm (PCA) was used to analyze the hyperspectral image. And the feature wavelengths (1260nm, 1658nm and 2131nm), which could maximum highlight the stubble tip incision and lighten the backgrounds, was selected according to the weight coefficient curve. In addition, the PCA algorithm was widely used in hyperspectral image analysis because of its effective dimension reduction effect and convenience. Secondly, images at three wavelengths were extracted and analyzed by PCA. After that, the PC2 image was convert into binarization image via single threshold method. Thirdly, the median filtering algorithm, morphological open operation, and edge noise removing algorithm were applied to ensure the precision and integrity of the maize stubble row. Totally 50 test images were collected to verify the segmentation effect of the presented method. At the same time, the segmentation precision rate, recall rate, and F1 value were calculated. The results revealed that the method proposed had good segmentation effect, and the segmentation precision rate, recall rate, and F1 value were 91.85%, 90.49%, and 91.16%, respectively. Therefore, the developed method realized good performance in maize stubble row segmentation and can provide great help for detection of navigation line in maize stubble cropland harvested by combine harvester.

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王春雷,陳婉芝,盧彩云,王慶杰.基于高光譜成像的玉米收獲后根茬行分割方法[J].農(nóng)業(yè)機械學(xué)報,2020,51(s2):421-426. WANG Chunlei, CHEN Wanzhi, LU Caiyun, WANG Qingjie. Segmentation Method for Maize Stubble Row Based on Hyperspectral Imaging[J]. Transactions of the Chinese Society for Agricultural Machinery,2020,51(s2):421-426.

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  • 收稿日期:2020-08-03
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  • 在線發(fā)布日期: 2020-12-10
  • 出版日期: 2020-12-10
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