基于颜色特征的棉田绿色杂草图像识别方法

    Image recognition of green weeds in cotton fields based on color feature

    • 摘要: 为实现棉田精确喷洒除草剂的自动化作业,该文基于颜色特征开展棉田中绿色杂草与棉苗的自动识别研究。利用苗期棉花茎秆呈暗红色的特点,首先使用Otsu法对所获图像的超红特征灰度图像和超绿特征灰度图像进行动态阈值分割,分别获取棉苗茎秆和绿色植物的二值图像。然后从棉苗茎秆二值图像中提取棉苗茎秆坐标,将棉苗茎秆与绿色植物二值图像进行位置信息融合,确定绿色植物二值图像中的棉苗区域,从而识别出各个绿色杂草区域并确定其区域质心和面积。通过15幅棉田绿色杂草图像进行试验表明,在棉苗茎秆不被叶片遮挡以及棉苗和杂草间不出现重叠的情况下,绿色杂草可以完全识别,棉苗的识别率可达到74%以上。

       

      Abstract: In order to realize automation of herbicide spraying precisely in cotton fields, the research on automatic recognition green weeds from cotton fields was developed based on color feature. The cotton seedling stem’s dark red feature was considered mainly. Firstly, the gray images of excess red feature and excess green feature were processed by Otsu’s threshold method, and cotton seedling stem’s and green plants’ binary images were gained respectively. Secondly, cotton seedling stem’s coordinates were extracted from its binary images, and the location information fusion was done between cotton seedling stem images and green plants’ binary images, then cotton seedling were obtained from green plants’ binary images. Finally, all weeds’ regions might be recognized and its image features, regions’ centroids and regions’ areas, were also calculated. The tests of 15 mixed images between cotton seedlings and green weeds showed that the green weeds could be recognized completely, and the recognition rate of the cotton seedling was 74% in the case that the cotton seedling stems were not blocked by their leaves and there were no overlap between cotton seedlings and green weeds.

       

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