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
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.
祖 琴1,2,3，张水发2，曹 阳3，赵会义3*，党长青4 . 结合光谱图像技术和SAM分类法的甘蓝中杂草识别研究 [J]. 光谱学与光谱分析, 2015, 35(02): 479-485.
ZU Qin1,2,3, ZHANG Shui-fa2, CAO Yang3, ZHAO Hui-yi3*, DANG Chang-qing4 . Research on Identification of Cabbages and Weeds Combining Spectral Imaging Technology and SAM Taxonomy . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(02): 479-485.
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