基于HSV的烤烟叶片青杂检测研究

Green Impurity Detection of Flue-cured Tobacco Leaf Based on HSV

为研究烟叶收购过程中快速高效的青杂检测方法,以山东诸城烟叶收购中的青杂检测为研究背景,以提升青杂检测的自动化和高效能为目的,分别对含青烟叶样本、含杂烟叶样本和合格烟叶样本进行数据采集,结合图像识别技术,进行烤烟青杂检测的样本处理和主流检测手段优劣性剖析。利用256段波段高光谱相机获取数据信息,通过调取RGB波段映射RGB颜色空间,进而转换为HSV颜色空间进行叶片含青、含杂率检测。结果显示,通过大量实验测量,获得青杂的HSV颜色色域范围,精确给出青杂色的像素点数,进而给出烤烟叶片青含杂比例。待测烤烟的含青、含杂像素点的精确标注给出可视化的检测结果,结合烟叶RGB图像,使得算法的青杂检测具有较强的可解释性。研究发现,基于高光谱数据和HSV颜色空间的自动化烟叶青杂检测方法,青杂检测算法执行时延在4s左右,在青杂检测准确率方面已经满足烟叶收购需求。

Through the analysis of the advantages and disadvantages of the sample processing methods and mainstream detection methods for the detection of flue-cured tobacco leaf impurities,a set of detection schemes that fully reflect the superiority and high comprehensive performance is proposed.The 256-band hyperspectral camera was used to obtain data information,and the RGB color space was mapped by calling the RGB band,and then converted to the HSV color space for detection of green and impurity content in tobacco leaves.The HSV color gamut range of green impurity was obtained through amounts of real experimental measurements,and the number of green and impurity pixels of the tobacco leaves to be tested was accurately given,and the proportion of green and impurity pixels in the flue-cured tobacco leaves was given.The precise labeling of green and impurity pixels of the flue-cured tobacco to be tested provided a visual detection results.Combined with the RGB tobacco leaves,the algorithm of green and impurity detection had strong interpretability.Meanwhile,the execution delay of the proposed detection algorithm was about 4 s.The flue-cured tobacco leaf green impurity detection scheme not only meets the actual acquisition needs,but also has high visualization and interpretability.