杂草自动识别技术是实现变量喷洒、精准施药的关键, 更是制约其实现的瓶颈, 因此, 准确、快速、无损地实现杂草自动识别已成为精准农业的一个重要研究方向。利用高光谱成像系统采集甘蓝幼苗及小藜、稗草、牛筋草、马唐和狗尾草等五种杂草在1 000～2 500 nm波长区间的高光谱图像数据, 在ENVI中经过MNF变换对数据降噪、去相关, 并将波段维数从256维降到11维, 通过提取感兴趣区域获得标准光谱, 最后利用SAM分类法识别甘蓝与杂草, 光谱角弧度阈值为0.1弧度时, 分类效果良好。在HSI Analyzer中选择训练像元获得标准光谱后, 利用SAM分类法识别甘蓝与杂草, 并利用人工分类图与SAM分类图比较定量度量杂草的识别正确率, 结果表明, 当参数设置为5点平滑、0阶导数和7度光谱角度时, 分类效果最佳, 杂草识别率为80.0%, 非杂草类识别率为97.3%, 总体识别率为96.8%。应用光谱图像技术与SAM分类法相结合的方法进行杂草检测, 充分利用了光谱和图像的融合信息, 该方法应用空间的分类算法来建立光谱判别方法的训练集, 在像素级别上考察光谱矢量之间的相似性, 融合了光谱和图像两者的优势, 同时兼顾了准确性和快速性, 并且在整场范围内(行间和行内)改善杂草检测范围, 为农业精确管理中需要植物精准信息的应用领域提供了相关的分析手段和方法。

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.