The positioning of the apple growth cycle plays a very important role in predicting the development of apples and guiding fruit farmers in agricultural operations. The traditional method of manually positioning the apple growth cycle has problems such as low efficiency and poor accuracy. Pattern recognition provides support for continuous and rapid positioning during the apple growth process. Under the natural conditions of the orchard, due to the large differences in the individual colors of the apples during the growth process and the influence of factors such as light changes, the photographed apple images are more complex, which brings certain difficulties to the segmentation and recognition of the apples. In this paper, pattern recognition is used to automatically identify and extract the growth stages of apples, a hue intensity (HI) color segmentation algorithm based on a Gaussian distribution model based on prior knowledge is studied, and then an active shape model (ASM) is used to identify each period of apple growth based on pattern recognition. After a series of experimental verifications, the ASM-based automatic identification method proposed in this paper is feasible and can identify the various growth periods of apples, thereby serving the mechanized production of apples.