玉米苗期杂草的计算机识别技术研究

利用计算机视觉技术和人工神经网络技术对识别玉米苗期田间杂草进行了研究。首先利用类间方差最大自动阈值法二值化杂草图像的超绿特征，再进行连续腐蚀与膨胀，然后根据长宽比、圆度、第一不变矩3个形状特征由BP网络识别出玉米幼苗，最后利用种子填充法从阈值分割结果中擦除玉米目标，剩余的就是杂草目标。研究表明，基于BP网络的杂草识别算法对玉米幼苗与杂草的正确识别率分别为87.5%和93.0%，处理一幅640×480像素的杂草图像平均耗时约为58 ms。

Weed identification from corn seedling based on computer vision

Computer vision and artificial neural network were used to identify weed from corn seedling. First, the Otsu's method for automatic threshold was applied to segment weed images based on the modified excess green feature to distinguish the plant objects from the background. Second, successive erosion and dilation were implemented on the binary image. Third, the rest objects were classified into corn and weed by back-propagation neural network according to their shape features: aspect ratio, circularity and first invariant moment. Finally, all the weed objects were obtained by erasing corn objects in the segmentation results using seed filling method. The results showed that the algorithm for weed identification based on back-propagation neural network correctly classified 87.5% corn objects and 93.0% weeds. The average processing time was about 58 ms for a 640×480 pixels weed image.