CAJ | 학술논문

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1000～2500 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。
잡초자동식별기술시실현변량분쇄、정준시약적관건，경시제약기실현적병경，인차，준학、쾌속、무손지실현잡초자동식별이성위정준농업적일개중요연구방향。이용고광보성상계통채집감람유묘급소려、패초、우근초、마당화구미초등오충잡초재1000～2500 nm파장구간적고광보도상수거，재ENVI중경과MNF변환대수거강조、거상관，병장파단유수종256유강도11유，통과제취감흥취구역획득표준광보，최후이용SAM분류법식별감람여잡초，광보각호도역치위0．1호도시，분류효과량호。재 HSI Analyzer중선택훈련상원획득표준광보후，이용SAM분류법식별감람여잡초，병이용인공분류도여SAM분류도비교정량도량잡초적식별정학솔，결과표명，당삼수설치위5점평활、0계도수화7도광보각도시，분류효과최가，잡초식별솔위80．0％，비잡초류식별솔위97．3％，총체식별솔위96．8％。응용광보도상기술여SAM분류법상결합적방법진행잡초검측，충분이용료광보화도상적융합신식，해방법응용공간적분류산법래건립광보판별방법적훈련집，재상소급별상고찰광보시량지간적상사성，융합료광보화도상량자적우세，동시겸고료준학성화쾌속성，병차재정장범위내（행간화행내）개선잡초검측범위，위농업정학관리중수요식물정준신식적응용영역제공료상관적분석수단화방법。
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 cab-bage 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 classi-fication 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 con-sidering 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.