CAJ | 학술논문

　　单位面积麦穗数是小麦产量预测的一个重要参数，如何从图像上自动识别出麦穗数是测产的关键。为此，使用Sobel 算子对麦穗图像进行边缘检测，使麦穗从混有少量杂草的模糊的背景中分割开，并与加权平均法、G分量法和最大值法处理后的图像进行了比较。随机选取麦穗无交叉的50幅图像样本，分别使用上述方法处理，Sobel 算子法与其他3种方法相比，图像分割的总体耗时至少减少了10％。实验结果表明，Sobel 算子对麦穗图像分割是有效的。
　　단위면적맥수수시소맥산량예측적일개중요삼수，여하종도상상자동식별출맥수수시측산적관건。위차，사용Sobel 산자대맥수도상진행변연검측，사맥수종혼유소량잡초적모호적배경중분할개，병여가권평균법、G분량법화최대치법처리후적도상진행료비교。수궤선취맥수무교차적50폭도상양본，분별사용상술방법처리，Sobel 산자법여기타3충방법상비，도상분할적총체모시지소감소료10％。실험결과표명，Sobel 산자대맥수도상분할시유효적。
The number of wheat panicle is an important parameter in the wheat yield forecast .How to identify the number of wheat from the image is the key of yield monitor.The Sobel operator edge detection image was used image segmentation of wheat panicle.Panicle were separated from the blurred and mixture a small amount of weed of background .Then com-pared this method with the weighted average method , the G component method and the maximum value method , the out-line of panicle were much clearer and more explicit than other three methods .Randomly selected 50 independent and o-verlapping panicle images as samples , using the above approach for processing ,the segmentation based on Sobel operator of the overall time-consuming compared with the other three methods were increased by at least 10%.The experimental results show that the Sobel operator on the wheat image segmentation is effective .