基于迁移学习的苹果树叶片病虫害识别方法研究*

doi:10.13733/j.jcam.issn.20955553.2021.11.23

中国农机化学报 ›› 2021, Vol. 42 ›› Issue (11): 151-158.DOI: 10.13733/j.jcam.issn.20955553.2021.11.23

基于迁移学习的苹果树叶片病虫害识别方法研究^*

周宏威¹, 沈恒宇¹, 袁新佩¹, 李晓冬²

1.东北林业大学机电工程学院,哈尔滨市,150000;
2.国家林业和草原局森林和草原病虫害防治总站,沈阳市,110034

收稿日期:2020-11-11 修回日期:2021-09-15 出版日期:2021-11-15 发布日期:2021-11-15
作者简介:周宏威,男,1982年生,满族,吉林集安人,博士,高级工程师,博导;研究方向为林业病虫害检测。E-mail: easyid@163.com
基金资助:
*黑龙江省自然科学基金项目(YQ2020C018);中央高校基本科研业务费(2572019BF08);林业科技创新平台运行补助项目(2020132304);黑龙江省博士后启动金项目(LBH—Q20002)

Research on identification method of apple leaf diseases based on transfer learning

Zhou Hongwei¹, Shen Hengyu¹, Yuan Xinpei¹, Li Xiaodong²

1. College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, 150000, China;
2. Forest and Grassland Pest Control Station of State Forestry and Grassland Administration, Shenyang, 110034, China

Received:2020-11-11 Revised:2021-09-15 Online:2021-11-15 Published:2021-11-15

摘要/Abstract

摘要： 为实现苹果树叶片病虫害快速且准确地识别与分类,研究基于迁移学习的多种神经网络模型,对比不同模型在苹果树叶片病虫害识别上的准确度。构建VGG16,ResNet50,Inception V3三种神经网络模型,利用从PlantVillage上获取的4种不同的苹果树叶片图片,分别为苹果黑星病叶片,苹果黑腐病叶片,苹果锈病叶片以及健康苹果叶片。按照8∶1∶1的比例将图片分为训练集,测试集和验证集对模型进行训练。在相同的试验条件下对比分析VGG16,ResNet50和Inception V3的试验结果。三种模型在使用迁移学习技术的情况下对苹果树叶片病虫害识别准确率分别达到97.67%,95.34%和100%。与不使用迁移学习的模型相比,使用迁移学习能够明显提升模型的收敛速度以及准确率,为常见的苹果树病虫害识别提供了新的方法。

关键词: 迁移学习, 苹果树病虫害, 图像识别, 神经网络

Abstract: In order to realize the rapid and accurate identification and classification of apple leaf diseases, a variety of neural network models based on transfer learning were studied, and the accuracy of different models in apple leaf diseases identification was compared. VGG16, ResNet50, and Inception V3 neural network models are constructed. Four different apple leaf pictures were obtained from the PlantVillage, which are apple scab leaves, apple black rot leaves, apple rust leaves, and healthy apple leaves. The pictures are divided into a training set, a test set, and a verification set to train the model with a ratio of 8∶1∶1. The test results of VGG16, ResNet50, and Inception V3 were compared and analyzed under the same test conditions. The test accuracy of the three models in identifying apple leaf diseases using transfer learning technology reached 97.67%, 95.34%, and 100%, respectively. Compared with the model without transfer learning, transfer learning can significantly improve the convergence speed and accuracy of the model and provide a new method for common apple tree diseases.

Key words: transfer learning, apple tree diseases, image recognition, neural network

中图分类号:

S436.611

周宏威, 沈恒宇, 袁新佩, 李晓冬. 基于迁移学习的苹果树叶片病虫害识别方法研究^*[J]. 中国农机化学报, 2021, 42(11): 151-158.

Zhou Hongwei, Shen Hengyu, Yuan Xinpei, Li Xiaodong. Research on identification method of apple leaf diseases based on transfer learning[J]. Journal of Chinese Agricultural Mechanization, 2021, 42(11): 151-158.

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基于迁移学习的苹果树叶片病虫害识别方法研究^*

Research on identification method of apple leaf diseases based on transfer learning

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