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The identification and location of wheat planting rows in the field environment is of great significance to the navigation operation of agricultural machinery such as pesticide spraying and weeding in the field. A method for detecting wheat planting row at multiple growth stages was proposed based on visible light remote sensing images of winter wheat at tillering stage and jointing stage obtained by unmanned aerial vehicle, combining with deep semantic segmentation and Hough transform linear detection. Firstly, wheat planting regions were extracted by SegNet deep semantic segmentation to overcome the sensitivity of traditional detection methods to light and improve detection accuracy. Secondly, based on the pre-detection results of wheat planting rows by Hough transform, dichotomy k-means clustering was proposed to further refine the detection results to identify the center line of wheat planting rows. Respectively, for winter wheat images at tillering stage and jointing stage, the mean absolute values of straight position deviation of planting row were 0.55cm and 0.11cm, and the mean absolute values of angle deviation were 0.0011 rad and 0.00037 rad. It was superior to the traditional method in detecting accuracy and line missing rate. The research results can provide a method for detecting the direction of crop planting in the navigation operation of intelligent agricultural machinery.