Abstract: Abstract: Weeds is one of the main harmful factors to the yield and quality of wheat and other main crops during seedling stage. Image processing technology is often used in weed recognition, but the method mainly cares about the weeds between different rows, which is always inefficient and wasteful for unmanned aerial vehicle (UAV) and machine spraying ways. In order to overcome the limitations above, this paper proposes a regional weed identification method, which takes advantage of properties of shearlets. Shearlets have attracted much attention in the field of image recognition because of its good sensitivity and fast computation in texture recognition. Meanwhile, it is a multi-scale analysis method with the characteristic of direction independence. Through the comparison of the regional images of the wheat and weed, it shows that the texture of the weed leaves is more complex while the wheat leaves are relatively regular. So we first choose 8 images including 4 wheat images and 4 weed images. Then we obtain shearlet transform coefficient (STC) at diverse scales and directions according to the different texture characteristics of wheat and weeds. In the STC images of different scales, the brightness from black to white represents different coefficient value. Moreover, the complexity of bright regional distribution represents the textural complexity, which can be used to distinguish wheat and weeds. Shearlets have self-adaptability because of different directions on these scales, so that obvious textural features in images taken from different angles can be detected. In our research, we take the self-adaptability of shearlets and the differences of STC images into account, and we choose the STC in the second scale of vertical cone to distinguish wheat weeds as experimental object. The result shows that the STC mean of wheat in the second scale is lower than that of weeds. Additionally, the fluctuation of STC mean of wheat is smaller than that of weeds. This study chooses 16 wheat images and 16 weeds images, aiming to distinguish weed and wheat more intuitively; we take a further statistical analysis on the mean and variance of coefficient matrixes of shearlets in the second scale of vertical cone. After normalization treatment, the distinction mean values and mean square error between wheat seedling and weeds are about 0.07 and 0.08 respectively. We randomly select 13 pictures of weeds and wheat seedling, and the recognition accuracy is 69.2%. The experimental results of contrast experiment show that the shearlet-transform method performs better than gray level co-occurrence matrix (GLCM) method to distinguish wheat seedling and weeds. We can get an explanation for the experimental results from the different theory of the shearlet-transform and GLCM. The theory of shearlet-transform shows that it can get different directions information adaptively. On the contrary, GLCM can only get the directions assigned, so the number of directions for image processing can't be changed. In addition, the method of splitting blocks of larger image gathered by UAV is used to realize the effective identification of non-wheat region. From the experimental results, we can see that the difference between wheat and weeds is based on effective shearlet-transform, and we can generalize our method to other image classification based on textural features. Furthermore, this method performs with high flexibility and stability and it has the potential for herbicide spraying in the field.