Aiming at the adhesion problem in the process of apple orchard pest identification, a pest adhesion image segmentation method was proposed based on shape and color screening. Firstly, the apple orchard pest images were collected, focusing on the feathered pests. Pests have completed most of their growth and development during the feathering process, and their external morphology, color, and texture are more stable and significant. Therefore, based on the analysis of the shape and color feature information of different kinds of pests, the pest HSV segmentation threshold and template outline were obtained. Secondly, the shape factor was used to determine the segmentation of adherent regions, and the segmentation of non-inter-species and inter-species adherent pests was achieved by the color segmentation method and the contour localization segmentation method. Finally, the collected pest images of apple orchard were experimentally analyzed, and the segmentation method based on shape-color screening was used to segment individual pests, and the results showed that the average segmentation rate, average segmentation error rate, and average segmentation efficiency of the proposed method were 101%, 3.14% and 96.86%, respectively, and the segmentation effect was superior to that of traditional image segmentation methods. In addition, with predefined color thresholds, the method achieved accurate classification of cotton bollworm, peach borer and corn borer, with average classification accuracies of 97.77%, 96.75% and 96.83%, respectively. At the same time, the Mask R-CNN model was used as the recognition model, and the average recognition accuracy was used as the evaluation index, and the recognition test was carried out on the pest images that were segmented by the proposed method and those that were not segmented by the proposed method, respectively. The results showed that the average recognition accuracies of cotton bollworm, peach borer and corn borer pest images that were segmented with the proposed method were 96.55%, 94.80% and 95.51%, respectively, and the average recognition accuracies were improved by 16.42, 16.59 and 16.46 percentage points, respectively, which indicated that the proposed method can provide a theoretical and methodological basis for accurate identification of orchard pests.