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中国农学通报

中国农学通报 ›› 2017, Vol. 33 ›› Issue (21): 134-137.doi: 10.11924/j.issn.1000-6850.casb16090110

所属专题： 园艺

• 植物保护 农药 • 上一篇    下一篇

基于机器视觉技术的黄瓜叶片白粉病识别

张 鹏,朱育强,王丽莉,周胜军   

  1. (浙江省农业科学院蔬菜所,杭州 310021）
  • 收稿日期:2016-09-23 修回日期:2016-12-01 接受日期:2016-12-07 出版日期:2017-07-27 发布日期:2017-07-27
  • 通讯作者: 张 鹏
  • 基金资助:
    浙江省公益技术研究农业项目“黄瓜白粉病抗性基因精细定位及分子标记开发”(2016C32097)；浙江省农业科学院青年人才培养项目“耐热、抗病黄瓜新品种的选育”(2016R23R08E03)。

Recognition of Cucumber Leaf Powdery Mildew Based on Machine Vision

Zhang Peng, Zhu Yuqiang, Wang Lili, Zhou Shengjun   

  1. (Institute of Vegetable, Zhejiang Academy of Agriculture Sciences, Hangzhou 310021)
  • Received:2016-09-23 Revised:2016-12-01 Accepted:2016-12-07 Online:2017-07-27 Published:2017-07-27

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摘要： 黄瓜叶片白粉病染病程度的判定，对于确定病灾预防措施意义重大。笔者采用机器视觉技术对叶片病斑进行有效识别，借助人工神经网络完成对叶片染病状态的模式分类。根据病斑的规模及面积分布提出了白斑区域面积比、平均白斑面积、白斑覆盖率等特征参数，借助这3个特征参数实现了叶片染病程度的定量分析，并借助人工神经网络完成了对叶片染病状态的模式分类。4类白斑叶片的正确识别率分别为88%、91%、92%、94%。

关键词: 林业专题图, 林业专题图, 数字水印, 版权保护, DCT变换, 鲁棒性

Abstract: The determination of the degree of cucumber leaf powdery mildew is of great significance for taking measure to protect the disease. In the study, leaf scab was identified effectively by the machine vision technology, as characteristic parameters, white spot area ratio, average white spot area and white spot coverage were presented based on the scale and area distribution of the scab. The level of leaf disease was quantitative analyzed with the three of characteristic parameters, and the pattern classification of leaf powdery mildew situation was accomplished by the artificial neural network. The correct recognition rate of the four classes white spot leaf was 88%, 91%, 92%, 94%, respectively.

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