关键词: 小麦, 麦苗, Kmeans聚类分割, 图像分割, Harris角点检测, 识别

Abstract: To solve the problem of low efficiency and time-consuming for the field investigation methods of wheat seedling count,we systematically studied the influence of the images acquired from the 1—4 leaf stage of wheat at different shooting angles of mobile devices on the number of wheat seedlings identified.Markers were set to locate and segment the two-row 1-meter area to be identified,and image processing technology was used to precisely cut and correct the target area. On this basis,the effects of four image segmentation methods on wheat seedling image segmentation were compared,and the image was automatically segmented into different connected regions using image segmentation algorithm,then the connected domain was used extraced for inner cavity filling and adhesion of wheat leaf apex angular point block processing,and the Harris corner detection algorithm was adopted to process the wheat seedling stem endpoint for identification.According to the one-to-one relationship between wheat and wheat seedling stem,the basic number of wheat seedlings was calculated. The experimental results showed that different mobile devices did not affect the wheat seedling identification accuracy,the best shooting angle of wheat seedling image was 45°,and K-means clustering segmentation algorithm had the best segmentation effect on basic wheat seedlings. At different shooting times, with the increase of wheat leaves, the identification accuracy decreased gradually,and the identification accuracy was more than 0.97 in the 1—2 leaf stage,R²=0.99.The identification accuracy in the 3—4 leaf stage was more than 0.95,R²= 0.93,indicating that the application of image segmentation based on K-means fast segmentation combined with Harris corner detection method for quick,accurate and intelligent monitoring and identification was feasible.

Key words: Wheat, Wheat seedlings, K-means clustering, Image segmentation, Harris corner detection, Recognition