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The number of wheatears in each square meter is a main parameter of grain production estimate. In order to intelligently calculate the number of wheatears in certain parts, a in-field wheatear counting method based on image analysis technique was designed. Firstly, several color features such as normalized difference index were analyzed to get suitable features, which were used to extract wheatear from original image. Secondly, a comparison of the five texture features (energy, contrast, homogeneity, entropy and relation) was performed and the appropriate features were selected to segment wheat images. Finally, the number of ears was calculated. In this step, erosion and dilation operations in binary mathematical morphology were performed so as to clear impurities and awns. Hole filling algorithm and thinning algorithm were used to get unbroken wheatear and its skeleton. Corner detection algorithm was selected to get the corners of skeleton with the purpose of estimating the wheatear number of connected region. The advantages and disadvantages of the color segmentation and texture segmentation were deeply analyzed. Twenty images with 71×92 pixels were used to evaluate the run-time of color segmentation and texture segmentation. The former took 16.97 ms and the latter took 17.76 s. To validate the effectiveness of the designed method, 35 drilling wheat images and 35 broadcasting wheat images were tested, and the average counting accuracy data for drilling wheat and broadcasting wheat were 95.77% and 96.89%, respectively.The experimental results showed that the color feature and the texture feature could be used to extract wheatear from original wheat image, and the color segmentation was faster than texture segmentation but less environmental adaptability. The corners of skeleton had close relationship with the number of wheatears in connected region.