Abstract: Image segmentation is one of the fundamental problems in an automatic pest identification system. In the current research, algorithms based on thresholding or clustering are widely used. Despite the simplicity and efficiency of the traditional methods, their performances are not satisfactory because the gray intensity is overlapped among the background of plant leaves and pests in the field environment. In this paper, we propose a novel method to segment the whitefly in the field environment by the Discrete Cosine Transformation (DCT) and region growing methods. The images are assumed to be rightly taken and focused on the target objects. The low frequency of DCT represents the image contour, and the high frequency of DCT represents the image details. The high frequency of DCT is used to distinguish the blurred image from the clear image globally. On the other hand, the local intensity of the pests is changed gradually and the intensity between pests and the closed background or plant leaves is changed greatly, so region growing is adopted to take advantage of the local intensity of the objects and to extract complete targets locally. To be specific, first, the gray image is transformed by discrete cosine transformation, and the high frequency part is truncated. Then it is re-converted to a gray image by inverse discrete cosine transformation. Second, the transformed image and original image are differentiated. Through an adaptive thresholding and open-close operation, we obtained the clear foreground regions. Third, we marked each clear region and established the gray model. Finally, as the pests have good local polymerization degree, the region growing method was adopted to extract the complete target object. Pixels in the clear regions and conforming the region gray model are involved in the growing process with an 8-direction searching scale. As a result, each single connected component was taken as a target pest. The algorithm was implemented on a Visual Studio 2005 platform. The experiments were conducted on whitefly images by comparison with the methods based on thresholding and Gaussian Mixture Model (GMM). The average classification accuracy was 98.49%, which was higher than thresholding-based methods in space R, B, Y and GMM in space Y, respectively, by 2.96%, 3.28%, 3.24% and 9.65%. Experimental results show that our proposed method can effectively separate pests apart from normal part of leaves and background. Our method provides higher precision as well as the accurate and closed boundaries, which is beneficial in the processing of whitefly images.