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In order to improve the efficiency and accuracy of pear leaf disease degree diagnosis, a pear leaf disease spot counting method was proposed based on global context Cascade region-based convolutional neural network (GC-Cascade R-CNN). The backbone feature extraction network of the model was embedded in a global context feature model (GC-Model), to establish effective longrange dependency and channel dependency for enhancing the feature information. The model fused shallow detail features and deep rich semantic features by feature pyramid networks (FPN). ROI Align was used to replace ROI Pooling for regional feature aggregation and enhance the target feature representation. Bounding box regression and classification of target regions were performed by using multilayer Cascade networks to complete the disease spot counting task. In the test of pear leaf disease images, the mean average precision (mAP) of the model reached 89.4% for all types of disease spots, and a single image processing average time of 0.347s, ensuring real-time operation while improving detection accuracy. The results showed that the model could effectively detect multiple types of disease spot targets from pear leaf disease images, especially for the detection of anthracnose spots;and the coefficient of determination R2 of the regression of disease spot counting values and true values of different kinds of pear leaf diseases were all greater than 0.92, indicating that the model had high accuracy of disease spot counting. This study solved the difficulty of pear leaves disease degree diagnosis, and provided a new idea for the diagnosis of pear disease conditions and symptoms in automated agricultural production.