| 基于迁移学习的园艺作物叶部病害识别及应用 |
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| 引用本文： | 李博,江朝晖,谢军,饶元,张武.基于迁移学习的园艺作物叶部病害识别及应用[J].中国农学通报,2021,37(7):138-143. |
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| 作者姓名： | 李博 江朝晖 谢军 饶元 张武 |
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| 作者单位： | 1.安徽农业大学信息与计算机学院,合肥 230036;2.智慧农业技术与装备安徽省重点实验室,合肥 230036 |
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| 基金项目： | 智慧农业技术与装备安徽省重点实验室自主创新研究基金“基于边缘智能的病虫害在线检测”(APKLSATE2019X002);安徽高校自然科学研究重大项目“视频感知+迁移学习的茶树病害智能监测”(KJ2019ZD20);安徽省科技攻关项目“面向皖南茶园的水肥精准调控关键技术研发与应用”(1804a07020108);安徽省科技攻关项目“大别山区云农场智慧服务平台关键技术研发与集成示范”(201904a06020056) |
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| 摘 要： | 为给农户提供质优价廉的园艺作物叶部病害识别服务,提出基于迁移学习的模型训练及基于Flask的Web部署方法。对PlantVillage数据集进行预处理,分别使用ResNet18、ResNet50和ResNet152 3种模型进行迁移学习训练,得到3种识别模型。利用Flask将模型部署到服务器上。3种识别模型对苹果等14类园艺作物26种叶部病害的平均识别准确率分别是95.61%、96.63%和97.33%,识别单张图像的时间分别是10.9、17.9、33.7 ms。综合考虑模型特点和用户期望,设计快速、标准和准确3种识别模式,实现深度模型在服务器中稳定运行,具有一定的实用价值。
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| 关 键 词： | 园艺作物 病害识别 ResNet 迁移学习 Web部署 |
| 收稿时间： | 2020-09-07 |
Leaf Disease Recognition of Horticultural Crops Based on Transfer Learning and Its Application |
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| Li Bo,Jiang Zhaohui,Xie Jun,Rao Yuan,Zhang Wu.Leaf Disease Recognition of Horticultural Crops Based on Transfer Learning and Its Application[J].Chinese Agricultural Science Bulletin,2021,37(7):138-143. |
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| Authors: | Li Bo Jiang Zhaohui Xie Jun Rao Yuan Zhang Wu |
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| Institution: | 1.College of Information and Computer Science, Anhui Agricultural University, Hefei 230036;2.Anhui Key Laboratory of Intelligent Agricultural Technology and Equipment, Hefei 230036 |
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| Abstract: | To provide farmers with recognition services for horticultural crop leaf disease more conveniently and economically, a model training method based on transfer learning and a Web deployment method based on Flask are proposed. The PlantVillage dataset is preprocessed, and three recognition models are obtained by transfer learning using ResNet18, ResNet50 and ResNet152 models. Then, these three models are deployed to the server using Flask. The average recognition accuracy of the three models for 26 leaf diseases of 14 horticultural crops, such as apple, is 95.61%, 96.63% and 97.33%, respectively, and the recognition time of a single image is 10.9, 17.9 and 33.7 ms, respectively. Considering the characteristics of the model and users’ expectation, three fast, standard and accurate recognition patterns are designed to realize the stable operation of the deep model in the server, which has some practical value. |
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| Keywords: | horticultural crop disease recognition ResNet transfer learning web deployment |
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