基于机器视觉技术的黄瓜叶片白粉病识别

doi:10.11924/j.issn.1000-6850.casb16090110

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

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

(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

(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

摘要/Abstract

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.

中图分类号:

TP391.41

张鹏,朱育强,王丽莉,周胜军. 基于机器视觉技术的黄瓜叶片白粉病识别[J]. 中国农学通报, 2017, 33(21): 134-137.

Zhang Peng,Zhu Yuqiang,Wang Lili and Zhou Shengjun. Recognition of Cucumber Leaf Powdery Mildew Based on Machine Vision[J]. Chinese Agricultural Science Bulletin, 2017, 33(21): 134-137.

参考文献

[1] 黄仲生,张芝莉.黄瓜病虫害识别与防治[M].北京:中国农业出版社,2002:1-4.
[2] 刘志华,程鹏飞.黄瓜侵染性病害图像处理及特征值提取方法的研究[J].山西农业大学学报,2006,26(3):351-354.
[3] 陈步英.基于Web的黄瓜病虫害专家系统的开发与应用[J].农机化研究,2007(3):159-161.
[4] 成丽君,张宇波,杨文杰.机器视觉在植物叶片叶面积测算中的应用[J].山西农业大学学报:自然科学版,2014,34(3):277-280.
[5] 杨思思,周泓,虞侠挺.基于机器视觉的人工光植物工厂植物生长状况监测系统[J].传感器与微系统,2014,33(12):88-90.
[6] 翟鹏.基于机器视觉的葡萄表型特征测量[M].上海:上海交通大学,2014:2.
[7] 张新,赵燕东,郑力嘉,等.基于三维机器视觉的植物叶片萎蔫预测模型[J].农业机械学报,2014,45(9):260-26.
[8] Li J H, Gao L W, Shen Z R. Extraction and analysis of digital images feature of three kinds of wheat disease[A]. 2010 3rd International Congress on Image and Signal Processing(CISP2010)[C]. IEEE Transations, 2010:2543-2548.
[9] 谭峰,马晓丹.基于叶片的植物病虫害识别方法[J].农机化研究,2009,31(6):41-43.
[10] Yutaka S, Massto S. Construction of the Automatic Diagnosis System of Plant Disease Using Genetic Programming Which Paid Its Attention to Variety[A]. 2003 ASAE Annual International Meeting Sponsored by ASAE[C]. Riviera Hotel and Convention Center Las Vegas, Nevada, USA,2003:1-13.
[11] Qin J W, Thomas F B, Mark A R, et al. Detecting Citrus Canker by Hyperspectral Reflectance Imaging and Spectral Information Divergence[A]. 2008 ASABE Annual International Meeting Sponsored by ASABE[C]. Rhode Island Convention Center Providence, Rhode Island,2008:1-15.
[12] 关辉,张长利,张春媛.基于图像处理的黄瓜叶片病斑分级方法的研究[J].农机化研究,2010(3):94-97.
[13] 陈慧楠.玉米、黄瓜叶部病害的图像处理技术研究[D].沈阳:沈阳理工大学,2008.
[14] 张毓晋.图像分割[M].北京:科学出版社,2001:21-27.
[15] 吴健辉,杨坤涛.数字图像处理中边缘检测算法的实验对比研究[J].湖南理工学院学报:自然科学版,2007,20(2):25-30.
[16] 权龙哲,祝荣欣,雷溥,等.基于K-L变换与LS-SVM的玉米籽粒品种识别方法[J].农业机械学报,2010,41(4):168-172.
[17] Woebbecke D M, Meyer G E, VonBargen K, et al. Shape features for identifying young weeds using image analysis[J]. Transactions of the ASAE,1995,38(1):271-281.
[18] 王晓峰,黄德双,杜吉祥,等.叶片图像特征提取与识别技术的研究[J].计算机工程与应用,2006(3):190-193.
[19] 侯铜,姚立红,阚江明.基于叶片外形特征的植物识别研究[J].湖南农业科学,2009(4):123-125,129.
[20] 朱静,田兴军,陈彬,等.植物叶形的计算机识别系统[J].植物学通报,2005,22(5):599-604.
[21] Gonzalez R C, Woods R E.数字图像处理[M].北京:电子工业出版社,2003:7-10.
[22] 张晓明,蒋大真,卢宋林.一个启发式的图像边缘检测方法[J].核技术,1996,19(3):35-40.
[23] Fathi F, Mohamed C A. Greenhouse control with feed-forward and recurrent neural net works[J]. Simulation Modelling Practice and Theory,2007,15:1016-1028.
[24] 权龙哲,祝荣欣,马小愚.基于傅里叶描述的小麦籽粒三维粒形研究[J].东北农业大学学报,2006,39(6):120-123.
[25] 鲁宏伟.黄瓜白粉病抗性基因挖掘[D].北京:中国农业科学院,2015:4.
[26] 罗晶晶,张仁英,齐晓花,等.黄瓜几丁质酶基因克隆及与白粉病抗性关系的初步研究[J].分子植物育种,2015(7):1584-1591.
[27] 聂京涛,李晓丹,姚永康,等.用于黄瓜白粉病抗性鉴定的InDel标记[J].中国蔬菜,2015,1(9):26-30.
[28] 崔洪宇,张桂华,杜胜利.用于黄瓜白粉病抗病性鉴定的SCAR标记[J].中国蔬菜,2009(20):35-39.

基于机器视觉技术的黄瓜叶片白粉病识别

Recognition of Cucumber Leaf Powdery Mildew Based on Machine Vision

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