基于成像光谱技术的作物杂草识别研究

杂草识别是变量喷雾和物理方法精确除草的前提。利用自主设计的地面成像光谱系统在自然环境下获取了胡萝卜幼苗以及马齿苋、牛筋草和地锦等杂草在380～760 nm波长区间的高光谱数据，通过对数据归一化消除光照条件的影响之后，运用逐步法进行波段选择，采用Fisher线性判别方法对杂草与胡萝卜幼苗进行了识别。结果表明，当把每种杂草都作为一类加以精细区分时，运用选择的8个波段建立模型对杂草和胡萝卜幼苗的识别率达85%左右;当把杂草整体作为一类与胡萝卜幼苗进行区分时，运用选择的7个波段识别率高于91%。同时为了设计低成本的杂草识别系统，通过穷举法选择最优的2和3波段组合，其中最优3波段组合对杂草胡萝卜幼苗的识别能力与逐步法选择的5个波段相当，整体识别率达89%。此外发现，红边波段对杂草有着显著的识别能力。

Research on Crop-Weed Discrimination Using a Field Imaging Spectrometer

Discrimination of weeds from crop is the first and important step for variable herbicides application and precise physical weed control. Using a new field imaging spectrometer developed by our group, hyperspectral images in the wavelength range 380-870 nm were taken in the wild for the investigation of crop-weed discrimination. After normalizing the data to reduce or eliminate the influence of varying illuminance, stepwise forward variable selection was employed to select the proper band sets and fisher linear discriminant analysis (LDA) was performed to discriminate crop and weeds. For the case of considering each species as a different class, classification accuracy reached 85% with eight selected bands while for the case of considering overall weed species as a class, classification accuracy was higher than 91% with seven selected bands. In order to develop a low-cost device and system in future, all combinations of two and three bands were evaluated to find the best combinations. The result showed that the best three bands can achieve a performance of 89% comparable to the performance achieved by five bands selected using stepwise selection. The authors also found that “red edge” could afford abundant information in the discrimination of weed and crop.