| 光谱学与光谱分析 |
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| Research on Identification of Cabbages and Weeds Combining Spectral Imaging Technology and SAM Taxonomy |
| ZU Qin1,2,3, ZHANG Shui-fa2, CAO Yang3, ZHAO Hui-yi3*, DANG Chang-qing4 |
1. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Science Academy of State Grain Administration，Beijing 100037，China
4. The Electrical Engineering College of Guizhou University，Guiyang 550025，China |
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Abstract Weeds automatic identification is the key technique and also the bottleneck for implementation of variable spraying and precision pesticide. Therefore, accurate, rapid and non-destructive automatic identification of weeds has become a very important research direction for precision agriculture. Hyperspectral imaging system was used to capture the hyperspectral images of cabbage seedlings and five kinds of weeds such as pigweed, barnyard grass, goosegrass, crabgrass and setaria with the wavelength ranging from 1 000 to 2 500 nm. In ENVI, by utilizing the MNF rotation to implement the noise reduction and de-correlation of hyperspectral data and reduce the band dimensions from 256 to 11, and extracting the region of interest to get the spectral library as standard spectra, finally, using the SAM taxonomy to identify cabbages and weeds, the classification effect was good when the spectral angle threshold was set as 0.1 radians. In HSI Analyzer, after selecting the training pixels to obtain the standard spectrum, the SAM taxonomy was used to distinguish weeds from cabbages. Furthermore, in order to measure the recognition accuracy of weeds quantificationally, the statistical data of the weeds and non-weeds were obtained by comparing the SAM classification image with the best classification effects to the manual classification image. The experimental results demonstrated that, when the parameters were set as 5-point smoothing, 0-order derivative and 7-degree spectral angle, the best classification result was acquired and the recognition rate of weeds, non-weeds and overall samples was 80%, 97.3% and 96.8% respectively. The method that combined the spectral imaging technology and the SAM taxonomy together took full advantage of fusion information of spectrum and image. By applying the spatial classification algorithms to establishing training sets for spectral identification, checking the similarity among spectral vectors in the pixel level, integrating the advantages of spectra and images meanwhile considering their accuracy and rapidity and improving weeds detection range in the full range that could detect weeds between and within crop rows, the above method contributes relevant analysis tools and means to the application field requiring the accurate information of plants in agricultural precision management.
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Received: 2013-11-29
Accepted: 2014-03-10
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Corresponding Authors:
ZHAO Hui-yi
E-mail: zhhy@chinagrain.org
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[1] ZU Qin，CHEN Xiang-ping，DENG Wei(祖 琴，陈湘萍，邓 巍). Journal of Agricultural Mechanization Research(农机化研究)，2013，35(3)：19.
[2] ZU Qin，DENG Wei，WANG Xiu，et al(祖 琴，邓 巍，王 秀，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2013，33(10)：2745.
[3] Burgos-Artizzu X P，Ribeiro A，Guijarro M，et al. Computers and Electronics in Agriculture，2011，75(2)：337.
[4] Gómez-Casero M T，Castillejo-González I L，García-Ferrer A，et al. Agronomy for Sustainable Development，2010，30(3)：689.
[5] ZHANG Xiao-long，XIE Zheng-chun，ZHANG Nian-sheng，et al(张小龙，谢正春，张念生，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报)，2012，43(011)：220.
[6] TANG Jing-lei，HE Dong-jian，JING Xu，et al(唐晶磊，何东健，景 旭，等). Journal of Infrared and Millimeter Waves(红外与毫米波学报)，2011，30(2)：97.
[7] Yan L，Jones G，Villette S，et al. International Society for Optics and Photonics，2012：83000E-83000E-8.
[8] HAN Yang，LI Qian，ZHAO Yun-sheng，et al(韩 阳，李 潜，赵云升，等). Journal of Infrared and Millimeter Waves(红外与毫米波学报)，2010，29(4)：316.
[9] LI Hai-tao，GU Hai-yan，ZHANG Bing，et al(李海涛，顾海燕，张 兵，等). Remote Sensing Information(遥感信息)，2007，5：12.
[10] LIN Na，YANG Wu-nian，WANG Bin(林 娜，杨武年，王 斌). Geomatics and Information Science of Wuhan University(武汉大学学报·信息科学版)，2013，38(8)：988.
[11] WANG Zhi-gang，ZHU Zhen-hai，WANG Hong-mei，et al(王志刚，朱振海，王红梅，等). Journal of Remote Sensing(遥感学报)，1999，3(1)：60. |
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