中文摘要：

除草剂的精确喷施、物理方法精确除草皆依赖于杂草的自动识别。光合色素和结构差异导致作物、杂草的光谱反射率不同,因此不同植物可以利用光谱特性来区分。利用ASD光谱仪在室内分别测量了棉花、刺儿菜、水稻、稗草等四种植物在350-2500 nm波段范围内的光谱反射率。运用SAS统计软件的STEP-DISC过程筛选能够区分作物和杂草的波长;判别模型中加入筛选所得特征波长,利用Discrim过程进行判别分析。实验结果表明,利用3个特征波长385,415和435 nm有效地从双子叶植物棉花中识别出双子叶杂草刺儿菜,其识别率为100%,波长415和435 nm的组合对识别模型的贡献最大;利用5个特征波长375,465,585,705和1035 nm可有效地从单子叶植物水稻中识别出单子叶杂草稗草,其识别率也为100%,黄色到橙色的过渡波长585 nm和“红边”内的波长705 nm的组合对识别模型的贡献最大。

英文摘要：

Automatic detection of weeds is necessary for site--specific application of herbicides or precise physical weed control. Leaf reflectance is mainly determined by photosynthetic pigments, leaf structural properties and water content, so spectral reflectance characteristics can be used for weed discrimination. The spectral reflectance of cotton, rice and weeds was determined in the range from 350 to 2 500 nm using the Analytical Spectral Device Full Range FieldSpec Pro （ASD） in laboratory. The discrimination analysis was done using the statistical software package SAS. The characteristic wavelengths were selected by using STEPDISC procedure. With the selected characteristic wavelengths, discriminant models were developed using the DISCRIM procedure in SAS. For distinguishing spine-greens from cotton, three characteristic wavelengths, 385, 415, and 435 nm, were selected, and good classification performance （100 % accuracy） was achieved. The combination of characteristic wavelengths 415 and 435 nm has the biggest contribution to discrimination model. For distinguishing barnyard-grass from rice, five characteristic wavelengths, 375, 465, 585, 705, and 1 035 nm, were selected, and also good classification performance （100% accuracy） was obtained. The transition point from yellow to orange wavelength （585 nm） and the wavelength 705 nm in the red edge contributed more to discrimination model.