基于 YOLOv5s 和 Android 的苹果树皮病害识别系统设计

周逸博 1; 马毓涛 1; 赵艳茹 1; 2

文章摘要

周逸博 1 ，马毓涛 1 ，赵艳茹 1, 2.基于 YOLOv5s 和 Android 的苹果树皮病害识别系统设计[J].广东农业科学,2022,49(10):155-163

查看全文 HTML 基于 YOLOv5s 和 Android 的苹果树皮病害识别系统设计

Design of Mobile App Recognition System for Apple Bark Disease Based on YOLOv5 s and Android

DOI：10.16768/j.issn.1004-874X.2022.10.018

中文关键词: 苹果树皮病害 YOLOv5s 移动端 Android 多目标识别识别系统

英文关键词: apple bark disease YOLOv5s mobile end Android multi-object recognition recognition system

基金项目:国家自然科学基金（31901403）

作者	单位
周逸博 1 ，马毓涛 1 ，赵艳茹 1, 2	1. 西北农林科技大学机械与电子工程学院，陕西杨凌　712100； 2. 农业农村部农业物联网重点实验室 / 陕西省农业信息感知与智能服务重点实验室，陕西杨凌　712100

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中文摘要:

【目的】针对果园多种苹果树皮病害实时检测的需求，设计基于 Android 的苹果树皮病害识别 APP 以便进行果园精准管理。【方法】通过网络查找和实地拍摄收集轮纹病、腐烂病、干腐病 3 种病害的图片数据，经扩增和标注后按照 8 ∶ 2 比例进行训练集和测试集的划分。使用 YOLOv5s 算法训练苹果树皮病害识别网络模型，对训练得到的轻量级网络模型进行 Android 端部署，并设计相应 APP 界面，实现对轮纹病、腐烂病、干腐病的快速诊断。【结果】训练后得到的深度学习网络模型识别效果良好，准确率稳定在 88.7%，召回率稳定在 85.8%，平均精度值稳定在 87.2%。其中腐烂病准确率为 93.5%，干腐病准确率为 88.2%，轮纹病准确率为 84.3%。将其在 Android 端部署后，每张病害图片处理时间均小于 1 s，检测置信度为 87.954%。该轻量级识别系统不仅实现了 3 种病害的快速检测，也保证了较高的识别精度。【结论】YOLOv5s 网络权重模型小，能够轻松实现 Android 端的部署，且基于 YOLOv5s 设计的 APP 操作简单、检测精度高、识别速度快，可以有效辅助果园精准管理。

英文摘要:

【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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