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。
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.
作者
束美艳
魏家玺
周也莹
董奇宙
陈浩翀
黄智刚
马韫韬
SHU Meiyan;WEI Jiaxi;ZHOU Yeying;DONG Qizhou;CHEN Haochong;HUANG Zhigang;MA Yuntao(College of Land Science and Technology,China Agricultural University,Beijing 100193,China;College of Agriculture,Guangxi University,Nanning 530004,China;Beijing Municipal Veterans Affairs Bureau,Beijing 100020,China)
出处
《浙江大学学报（农业与生命科学版）》
CAS
CSCD
北大核心
2021年第4期429-438,共10页
Journal of Zhejiang University:Agriculture and Life Sciences
基金
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)。
关键词
柑橘
溃疡病
朴素贝叶斯分类
阈值分割
citrus
canker disease
naive Bayesian classification
threshold segmentation
