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首页> 外文会议>IFIP(International Federation for Information Processing) TC 12 International Conference on Computer and Computing Technologies in Agriculture(CCTA 2007); 20070818-20; Wuyishan(CN) >WEED DETECTION BASED ON THE OPTIMIZED SEGMENTATION LINE OF CROP AND WEED
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WEED DETECTION BASED ON THE OPTIMIZED SEGMENTATION LINE OF CROP AND WEED

机译：基于作物和杂草优化分界线的杂草检测

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摘要

Weed detection is a key problem of spot spraying that could reduce the herbicide usage. Spectral information of plants is very useful to detect weeds in real-time for the fast response time. However, the cost of an imaging spectrograph-based weed detection system is too high. Therefore, the main objective of this study was to explore a method to classify crop and weed plants using the spectral information in the visible light captured by a CCD camera. One approach to weed classification was to directly use of G and R component of RGB color space. Another was to utilize the spectral information among the green band that hue was regarded as wavelength, and saturation was represented as reflectance. The result of statistic analysis showed that both of them using the G-R and H-S optimized segmentation line of crop and weeds could be used to detect weed (lixweed tansymnustard) from wheat fields. Moreover, the method of using the H-S optimized model could avoid the affect of lighting.
机译：杂草检测是点喷的关键问题，可以减少除草剂的使用。植物的光谱信息对于快速检测杂草对于快速响应时间非常有用。然而，基于成像光谱仪的杂草检测系统的成本太高。因此，本研究的主要目的是探索一种利用CCD相机捕获的可见光中的光谱信息对农作物和杂草植物进行分类的方法。一种杂草分类方法是直接使用RGB颜色空间的G和R分量。另一个是利用绿色带中的光谱信息，其中色相被视为波长，饱和度被表示为反射率。统计分析结果表明，两者均采用G-R和H-S优化的农作物和杂草分割线，可用于检测麦田中的杂草（丹参）。而且，使用H-S优化模型的方法可以避免光照的影响。

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