In this study, we take a few kinds of leaf diseases of apple tree, such as Alternaria mali Roberts, as research objects, and a pathological identification method for apple tree leaf diseases based on depth-separable convolution is designed. The probability data enhancement is used to amplify the original dataset, a deep separable convolutional neural network is explored by using transductive transfer learning, and is applied to crop pathological recognition. An in-depth learning model for restricted equipment is designed to recognize and classify the apple tree leaf diseases, and the model is compressed, transformed, and transplanted to an embedded system for verification. The experimental results show that the proposed method has a good recognition effect, the recognition rate is up to 85.96% in the restricted equipment.