作物和杂草叶片的可见-近红外反射光谱特性

为了进行快速实时的杂草识别,研究了作物和杂草叶片的可见-近红外反射光谱特性.选择了两种常见的 田间作物大豆(Glycine max)和玉米(Zea mays),以及铁苋菜(Acalypha australis L.)和田字草(Marsilea quadrifolia L.)两种杂草作为研究对象.每种各30个样本.共120个样本.采用ASD Fieldspec便携式光谱仪进行光谱采集.在对400～1000 nm的光谱数据进行平滑和-阶求导预处理、.通过主成份分析.去除了一个奇异样本.最后用79个样本组成的建模集进行偏最小二乘法建模.对剩余的40个样本进行预测.预测模型结果的相关性达到0.986,识别率达到100%.说明研究中选用的作物和杂草叶片的可见-近红外反射光谱特性之19有较大的区别,町以用于 进行杂草和作物的区分.

Visible-Near Infrared reflection Spectroscopy for Crop-Weed Discrimination

Visible and near infrared reflection spectroscopy (Vis-NIRS) was studied as a fast and promising techniques for weed detection. Two kinds of weeds threeseeded mercury (Acalypha australis L.) and quatrefoil duckweed (Marsilea quardrifolia L.)] and two kinds of crops soybean (Glycine mas) and corn (Zea mays)] were investigated. A total 120 samples (30 for each species) of the plant leaves were prepared. The spectra were acquired by an ASD Fieldspec handhel spectrometer. After data pre-processing and principle component analysis on spectra from 400 to 1000 nm, one influential outliner was detected. After full-cross calibration of the 79 samples using partial least squares regression, the remaining 40 samples were predicted by the established model. Correlation coefficient of prediction set was 0.987. 100% recognition rate was obtained based on artificial thresholds. So, Vis-NIR spectroscopy is an available alternative for recognizing crops from weeds.