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| Design of Mobile App Recognition System for Apple Bark Disease Based on YOLOv5 s and Android |
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| DOI：10.16768/j.issn.1004-874X.2022.10.018 |
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| Abstract: |
| 【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. |
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