基于朴素贝叶斯分类的柑橘叶片溃疡病诊断

doi:10.3785/j.issn.1008-9209.2021.04.011

浙江大学学报（农业与生命科学版）

2021, Vol. 47

Issue (4): 429-438 DOI: 10.3785/j.issn.1008-9209.2021.04.011

作物表型分析技术及应用专题

基于朴素贝叶斯分类的柑橘叶片溃疡病诊断

束美艳¹,魏家玺^1,^2,³,周也莹¹,董奇宙¹,陈浩翀¹,黄智刚²(

),马韫韬¹

^1.中国农业大学土地科学与技术学院，北京 100193
^2.广西大学农学院，南宁 530004
^3.北京市退役军人事务局，北京 100020

Diagnosis of citrus leaf canker disease based on naive Bayesian classification

Meiyan SHU¹,Jiaxi WEI^1,^2,³,Yeying ZHOU¹,Qizhou DONG¹,Haochong CHEN¹,Zhigang HUANG²(

),Yuntao MA¹

^1.College of Land Science and Technology, China Agricultural University, Beijing 100193, China
^2.College of Agriculture, Guangxi University, Nanning 530004, China
^3.Beijing Municipal Veterans Affairs Bureau, Beijing 100020, China

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摘要：

为实现准确、快速地识别柑橘叶片溃疡病，提出一种基于朴素贝叶斯分类的柑橘叶片溃疡病诊断方法。基于不同病害程度的叶片数码图像，根据颜色空间特征，构建基于朴素贝叶斯的柑橘叶片溃疡病斑识别模型，并对比分析朴素贝叶斯分类、固定阈值分割、自适应阈值分割、支持向量机分割对柑橘叶片溃疡病的诊断能力。结果表明：基于朴素贝叶斯分类的柑橘叶片溃疡病斑分割效果较好，误分割率仅为3.58%，远远优于阈值法和支持向量机。在运行效率方面，4种算法耗时排序为固定阈值法＜自适应阈值法＜朴素贝叶斯法＜支持向量机法，但均在较合理的范围内；结合前期准备时间，朴素贝叶斯法的运行效率最佳。综上所述，朴素贝叶斯分类算法在柑橘叶片溃疡病诊断方面具有快速、精准的应用能力，可以为果树从业者精确诊断果树病害严重度提供新思路。

关键词： 柑橘; 溃疡病; 朴素贝叶斯分类; 阈值分割

Abstract:

In order to recognize citrus leaf canker disease accurately and quickly, a diagnosis method of citrus leaf canker disease based on naive Bayesian classification was proposed. The digital images of leaves with different severities of citrus leaf canker disease were used as the data source. According to the characteristics of color space, a disease spot recognition model based on naive Bayesian classification was established for rapid diagnosis of citrus leaf canker disease, and the diagnostic abilities of naive Bayesian classification, fixed threshold, adaptive threshold and support vector machine for citrus leaf canker disease were compared. The results showed that the method based on naive Bayesian classification was effective in the segmentation of citrus leaf canker disease, and the incorrect segmentation rate was only 3.58%, which was far better than the threshold methods and support vector machine. In terms of performance efficiency, the time order of the four algorithms was fixed threshold method＜adaptive threshold＜naive Bayesian＜support vector machine, all of which were within a reasonable range. Combined with the preparation time, naive Bayesian method had the best performance efficiency. Therefore, the naive Bayesian classification algorithm has a rapid and accurate application ability in the diagnosis of citrus leaf canker disease, and can provide a new way for the accurate diagnosis of fruit tree disease severities.

Key words: citrus canker disease naive Bayesian classification threshold segmentation

收稿日期: 2021-04-01 出版日期: 2021-09-02

CLC:

TP 39

基金资助: 国家自然科学基金(41967006);广西自然科学基金(2018GXNSFDA281035);内蒙古自治区科学技术厅项目(2020GG0038)

通讯作者: 黄智刚 E-mail: hzg@gxu.edu.cn

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引用本文:

束美艳,魏家玺,周也莹,董奇宙,陈浩翀,黄智刚,马韫韬. 基于朴素贝叶斯分类的柑橘叶片溃疡病诊断[J]. 浙江大学学报（农业与生命科学版）, 2021, 47(4): 429-438.

Meiyan SHU,Jiaxi WEI,Yeying ZHOU,Qizhou DONG,Haochong CHEN,Zhigang HUANG,Yuntao MA. Diagnosis of citrus leaf canker disease based on naive Bayesian classification. Journal of Zhejiang University (Agriculture and Life Sciences), 2021, 47(4): 429-438.

链接本文:

http://www.zjujournals.com/agr/CN/10.3785/j.issn.1008-9209.2021.04.011 或 http://www.zjujournals.com/agr/CN/Y2021/V47/I4/429

图1 柑橘叶片溃疡病部分原始图片

图2 预处理前后柑橘叶片溃疡病样例图A.原图；B.中值滤波处理；C.中值滤波＋直方图均衡化处理。

图3 柑橘叶片溃疡病原图和HSV、LAB颜色空间的各通道图

图4 柑橘叶片溃疡病A、B、L通道直方图A. A通道直方图；B. B通道直方图；C. L通道直方图。

图5 基于固定阈值法和自适应阈值法的病斑识别结果A.预处理后的叶片样本图（红色为柑橘叶片溃疡病手动分割矢量结果）；B.固定阈值法的病斑识别结果；C.自适应阈值法的病斑识别结果。

图6 基于支持向量机和朴素贝叶斯法的病斑识别结果A.预处理后的叶片样本图（红色为柑橘叶片溃疡病手动分割矢量结果）；B.支持向量机法的病斑识别结果；C.朴素贝叶斯法的病斑识别结果。

图7 不同误分割率区间病害叶片样本量分布

表1 4种方法的病斑误分割率

表2 4种分割方法的平均运行时间

图8 病害原图与固定阈值漏分割图a～c.原图；A～C.分割图。

图9 病害原图与自适应阈值过度分割图a～c.原图；A～C.分割图。

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