Objective A practical mobile APP recognition system based on Android was designed for the requirement of real-time detection of various apple bark diseases in orchards.

Method The images of ring rot, canker and dry rot were collected through network searching and field shooting. After amplification and labeling, the training set and test set were divided according to the ratio of 8 ∶ 2. The YOLOv5s algorithm was used to train the apple bark disease recognition network model. The lightweight network model trained was deployed on the Android end, and the corresponding APP interface was designed to realize the rapid diagnosis of ring rot, canker and dry rot.

Result The recognition effect of the deep learning network obtained after training is good, the accuracy rate is stable at 88.7%, the recall rate is stable at 85.8%, and the average accuracy value is stable at 87.2%. Among them, the accuracy of canker is 93.5%, dry rot is 88.2%, and ring rot is 84.3%. After it is deployed on the Android end, the processing time of each disease picture is less than 1s, and the detection confidence is 87.954%. The lightweight recognition system not only realizes the rapid detection of the three diseases, but also ensures high recognition accuracy.

Conclusion The YOLOv5s network weight model is small, which can be easily deployed on the Android. The APP designed based on YOLOv5s is simple to operate with high detection accuracy and fast recognition speed, which is helpful for precise management of orchards.