三种广翅蜡蝉前翅形态数值特征提取与分析 *

应用昆虫学报, 2020, 57(4): 980-987 doi: 10.7679/j.issn.2095-1353.2020.101

研究论文

三种广翅蜡蝉前翅形态数值特征提取与分析 *

潘鹏亮,1,2,**, 洪枫1,2, 陈俊华1,2, 刘红敏1,2, 尹新明,3,***, 熊建伟1,2

1. 信阳农林学院农学院,信阳 464000

2. 河南省豫南农作物有害生物绿色防控院士工作站,信阳 464000

3. 河南农业大学植物保护学院,郑州 450002

Extraction and analysis of numerical characteristics from forewings of three plant hopper species (Homoptera: Ricaniidae)

PAN Peng-Liang,1,2,**, HONG Feng1,2, CHEN Jun-Hua1,2, LIU Hong-Min1,2, YIN Xin-Ming,3,***, XIONG Jian-Wei1,2

1. College of Agronomy, Xinyang Agriculture and Forestry University, Xinyang 464000, China

2. Henan Provincial South Henan Crop Pest Green Prevention and Control Academician Workstation, Xinyang 464000, China

3. College of Plant Protection, Henan Agricultural University, Zhengzhou 450002, China

通讯作者: *** E-mail:xinmingyin@hotmail.com

**第一作者First author,E-mail: panpl@xyafu.edu.cn

收稿日期: 2019-03-15   接受日期: 2019-07-23   网络出版日期: 2020-07-27

基金资助: *国家重点研发计划项目.  2016YFD0200900
河南省高等学校重点科研项目计划.  19A210021
河南省产业技术体系(大宗水果).  S2014-11-G03

Corresponding authors: *** E-mail:xinmingyin@hotmail.com

Received: 2019-03-15   Accepted: 2019-07-23   Online: 2020-07-27

摘要

【目的】 广翅蜡蝉科昆虫是果园、茶园和园艺植物上的重要害虫之一,对其种类进行自动判别是实现其种群自动监测的基础。本研究拟通过获取3种广翅蜡蝉前翅轮廓特征探讨在其种类和性别鉴定中的作用。【方法】 采用图像处理与分析技术,对3种广翅蜡蝉前翅轮廓形态特征进行提取和分析,并使用SPSS v22.0对数据进行分析。【结果】 同一种广翅蜡蝉左右翅在轮廓形态上无显著差异,雌雄间各参数在不同种类广翅蜡蝉中差异性不同,其中5个实际测量参数在雌雄间差异达到显著或极显著水平,只有透明广翅蜡蝉的周长不显著。除周长和雄虫圆形度外,其它各类参数在种间的差异达到显著或极显著水平。通过典型判别分析,认为所选用的3种广翅蜡蝉,通过其前翅轮廓特征可以进行种间判别,其原始判别和交叉验证判别的正确率均超过90%。【结论】 通过提取前翅轮廓特征可以实现3种广翅蜡蝉种类的识别,为此类昆虫的自动鉴定和种群监测提供了重要的参考依据。

关键词: 特征提取 ; 雌雄鉴别 ; 种类鉴定 ; 自动监测 ; 蜡蝉

Abstract

[Objectives] To investigate if contour features of the forewings of three Ricaniidae plant hopper species, one of the most important families of pests in orchards, tea plantations, and horticultural plants, can be used to reliably identify an individual’s species and sex. [Methods] Morphological features of the forewings of the three plant hopper species were extracted by image processing and analysis technology and the data analyzed in SPSS v22.0. [Results] There was no significant difference in the contour morphological parameters of the left and right wings of one species. However, there was a difference in these parameters between female and male adults of the other two species. Except for the perimeter measurement in Euricania clara, differences in five actual measurements were either significant, or extremely significant, between females and males. Except for the circularity measurement of males and the perimeter measurement, differences in other parameters were also significant, or extremely significant, between species. Canonical discriminant analysis indicates that these three pest species can be distinguished by their forewing contour features with an accuracy from original and cross validation discrimination of more than 90%. [Conclusion] Three kinds of plant hoppers can be reliably identified by the contour features of their forewings, which provides a method for the automatic identification and population monitoring for these pests.

Keywords: feature extraction ; sex discrimination ; species identification ; automatic monitoring ; plant hopper

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潘鹏亮, 洪枫, 陈俊华, 刘红敏, 尹新明, 熊建伟. 三种广翅蜡蝉前翅形态数值特征提取与分析 *. 应用昆虫学报[J], 2020, 57(4): 980-987 doi:10.7679/j.issn.2095-1353.2020.101

PAN Peng-Liang, HONG Feng, CHEN Jun-Hua, LIU Hong-Min, YIN Xin-Ming, XIONG Jian-Wei. Extraction and analysis of numerical characteristics from forewings of three plant hopper species (Homoptera: Ricaniidae). Chinese Journal of Applied Entomology[J], 2020, 57(4): 980-987 doi:10.7679/j.issn.2095-1353.2020.101

广翅蜡蝉科Ricaniidae的多数种类为害木本植物,其中柿广翅蜡蝉Ricania sublimbata Jacobi主要为害柿、山楂和咖啡;八点广翅蜡蝉Ricania speculum (Walker)寄主较多,可为害苹果、桃、李等果树,也可为害茶树和棉、麻、大豆等农作物;而透明广翅蜡蝉Euricania clara Kato主要为害洋槐(周尧和路进生,1977)。谭济才(1995)发现柿广翅蜡蝉不但为害茶树,且在湖南一年发生两代,而其它蜡蝉一年一代。根据几近来的调查,八点广翅蜡蝉和柿广翅蜡蝉已成为果园(俞素琴等,2018)和茶园(赵丰华等,2010,2011)的重要害虫之一。据报道,常见的广翅蜡蝉科昆虫有26种,对其种类鉴定多依据翅面特征(周尧和路进生,1977)。采用计算机图像处理技术能否通过获取翅面特征实现广翅蜡蝉科种类或种内性别的判断,以节省田间调查时间,这一研究具有重要的应用价值。

在昆虫分类学和现代植保技术领域(翁桂荣,2008;徐鹏等, 2010),通过昆虫数学形态学理论和方法与计算机视觉技术相结合,不但可以解决某些昆虫种类和性别的鉴定和判别问题(杨红珍等, 2011;潘鹏亮等,2017),也可以实现害虫的虫情监测或进行生物多样性研究(沈佐锐和于新文,1998;Kalafi et al.,2018)。比如,在昆虫不育技术领域,利用计算机视觉可以实现地中海实蝇Ceratitis capitata (Wiedemann)性别的自动检测,提高了生产效率(Jose et al.,2008)。在田间,利用机器视觉来探测甜菜夜蛾Spodoptera exigua (Hübner)以提高农业智能系统的能力,实现大田害虫的防控也是基于对图像的处理(Vakilian and Massah,2013)。

利用局部特征对某些昆虫种类或种下分类阶元进行判断也有许多报道。比如对红棕象甲Rhynchophorus ferrugineus Olivier前胸背板特征进行获取,以实现“捕获-标记-再捕获”类试验中个体的精确识别(Joan et al.,2018)。对于西方蜜蜂Apis mellifera L.,该种昆虫有超过20个亚种,可以通过翅脉模式的差异进行区分,通过分析前翅翅脉模式,可以实现蜜蜂亚种、生态型、种群和杂交种的区分(Nawrocka et al.,2018)。然而,对于蜡蝉类害虫还未见此类报道。

本文拟利用图像处理与分析技术,对果树和茶树上重要的3种蜡蝉进行前翅轮廓特征提取与分析,探讨此类昆虫能否通过前翅特征进行区分,为其种群的自动监测提供科学依据。

1 材料与方法

1.1 供试昆虫

柿广翅蜡蝉R. sublimbata,雌虫33头,雄虫36头;八点广翅蜡蝉R. speculum,雌虫5头,雄虫5头;透明广翅蜡蝉E. clara,雌虫34头,雄虫35头。所有标本均采自信阳农林学院附近果园和周边灌木。标本采集后立即放入盛有90%乙醇溶液的50 mL离心管中以保证标本的完整性,带回室内后利用解剖剪截去前翅(图1),左右翅分开编号保存。

图1

图1   3种蜡蝉前翅

A. 柿广翅蜡蝉;B. 八点广翅蜡蝉;C. 透明广翅蜡蝉。

Fig. 1   Forewings from three plant hopper species

A. Ricania sublimbata; B. Ricania speculum; C. Euricania clara.


1.2 图像获取与特征提取

利用爱普生平板式扫描仪(Epson Perfection V370 Photo,印度尼西亚制造)进行批量扫描,扫描模式为胶片(正片),图像类型为24位全彩,分辩率为2 400 dpi,保存的图像类型为JPG格式,且每张图片中只包含一只翅膀。特征提取利用中国农业大学IPMist实验室开发的BugShape 1.0软件,该软件可以一次性自动获得目标区域(翅)面积、周长、长轴长度、短轴长度、等效圆半径、偏心率、紧凑度、球状性、叶状性和似圆度10个参数(于新文等,2003)。

1.3 数据处理与统计分析

对获得到的10个蜡蝉前翅参数进行初步分组,其中,面积、周长、长轴长度、短轴长度和等效圆半径为实际测量值,而偏心率、紧凑度、球状性、叶状性和似圆度是通过相应公式进行计算的二次参数(于新文等,2003)。对同种蜡蝉同种性别的左右翅、同种蜡蝉不同性别等数据采用独立样本t-检验,以明确蜡蝉左右翅,以及不同性别间的差异。对3种蜡蝉间翅形态特征的差异采用单因素方差分析(One-way ANOVA),多重比较采用LSD方法。通过典型判别分析(采用Fisher线性判别函数),对不同类型参数下各样本进行分组判别,以验证所获得特征参数在3种蜡蝉种类鉴定中的效果,所有统计分析过程均在SPSS v22.0中进行。

2 结果与分析

2.1 蜡蝉左右翅的差异

独立样本t-检验结果表明,研究中的3种蜡蝉同一性别左右前翅间所有参数均通过了方差齐性检验(显著性P值均远大于0.05),且各处理间差异均未达到显著水平。其中,柿广翅蜡蝉雌虫各参数(df=64)的t值范围为–0.346-0.709,P值范围为0.481-0.966;柿广翅蜡蝉雄虫各参数(df=70)的t值范围为–0.424-0.447,P值范围为0.673-0.965。八点广翅蜡蝉雌虫各参数(df=8)的t值范围为–1.364-0.704,P值范围为0.210- 0.849;八点广翅蜡蝉雄虫各参数(df=8)的t值范围为–0.555-0.321,P值范围为0.594-0.939。透明广翅蜡蝉雌虫各参数(df=66)的t值范围为–1.042-0.485,P值范围为0.301-0.848;透明广翅蜡蝉雄虫各参数(df=68)的t值范围为–0.289-0.452,P值范围为0.652-0.940。

2.2 不同性别间的差异

独立样本t-检验结果表明,3种蜡蝉在不同参数中的差异显著性有明显不同。其中,柿广翅蜡蝉雌雄间在各实际测量参数中的差异均达到极显著水平(df=136,t=6.543-7.258,P<0.001),而在二次参数中,偏心率(df=136,t=2.616,P=0.010)、紧凑度(df=136,t=–2.071,P=0.040)和圆形度(df=136, t=–2.487, P=0.014)在雌雄间差异达到显著水平,而球状性和叶状性差异不显著。八点广翅蜡蝉雌雄间在周长(df=18,P=0.015)上的差异达到显著水平,其它实际测量参数均达到极显著水平(df=18,P<0.001),而在二次参数中差异均不显著。透明广翅蜡蝉雌雄间除了周长未达到显著水平外,其它实际测量参数均达到极显著水平(df=136,t=4.814-18.719,P<0.001),而在二次参数中只有偏心率(df=136,t=3.641,P<0.001)达到极显著水平,其它二次参数均未达到显著水平(图2)。

图2

图2   3种蜡蝉雌雄翅形态参数比较

A,B. 柿广翅蜡蝉;C,D. 八点广翅蜡蝉;E,F. 透明广翅蜡蝉。图中数据为平均值±标准差,**表示在α=0.01水平上差异显著;*表示α=0.05水平上差异显著;ns表示不显著。Ar:面积;Pe:周长;LA:长轴长度;SA:短轴长度;Eq:等效圆半径;Ec:偏心率;Co:紧凑度;Sp:球状性;Lo:叶状性;Ci:圆形度。

Fig. 2   Comparison results of morphological parameters about forewings of male and female from three plant hopper species

A, B. R. sublimbata; C, D. R. spechlum; E, F. E. clara. The data in the figure are mean±SD; ** indicates significantly different at 0.01 level, * indicates significantly different at 0.05 level, and the ns indicates no significant difference between different sexual samples in one species. Ar: Area; Pe: Perimeter; LA: Long axis; SA: Short axis; Eq: Equal radius; Ec: Eccentricity; Co: Compactness; Sp: Sphericity; Lo: Lobation; Ci: Circlularity.


2.3 种间差异

通过对3种蜡蝉雌虫、雄虫和雌雄数据集分别进行单因素方差分析,结果表明,在3个数据集中除周长、雄虫的圆形度差异不显著外,其它参数在种间均表现出差异显著或极显著。其中,面积、长轴长度、短轴长度、等效圆半径、偏心率、紧凑度、球状性、叶状性在各数据集中P值均远小于0.01,差异达到极显著水平。在雌虫数据集中,圆形度(F=4.255;df=2, 141; P=0.016)在种间的差异达到显著水平,而在雌雄数据集中,其在种间的差异(F=5.545;df=2, 293; P=0.004)达到了极显著水平(图3)。

图3

图3   3种蜡蝉各数据集在前翅形态参数间的差异

图中数据为平均值±标准差。同一组柱上标有不同字母表示种间差异显著,相同字母表示差异不显著(α=0.05)。Ar:面积;Pe:周长;LA:长轴长度;SA:短轴长度;Eq:等效圆半径;Ec:偏心率;Co:紧凑度;Sp:球状性;Lo:叶状性;Ci:圆形度。

Fig. 3   Difference of morphological parameters of forewings from three plant hopper species with different data sets

The data in the figure are mean±SD. Histograms with different letters within one group indicate significant difference between species, while same letters indicate no significant difference at 0.05 level. Ar: Area; Pe: Perimeter; LA: Long axis; SA: Short axis; Eq: Equal radius; Ec: Eccentricity; Co: Compactness; Sp: Sphericity; Lo: Lobation; Ci: Circlularity.


2.4 聚类判别

通过对两类数据集进行典型判别分析,结果表明无论是使用实际测量数据集、二次参数数据集,还是使用综合数据集,其原始判别和交叉验证判别的正确率均在90%以上。其中,使用实际测量数据集交叉验证判别的正确率为97.6%,二次参数依据集交叉验证判别的正确率为90.2%,综合数据集的交叉验证判别正确率最高为98.3%。使用实际测量数据与综合数据对柿广翅蜡蝉和透明广翅蜡蝉的判别结果完全一致,而对八点广翅蜡蝉使用综合数据时误判率低于实际测量数据。仅使用二次数据时对八点广翅蜡蝉误判率最高,大多数的样本被判定为柿广翅蜡蝉(表1)。

表1   3种蜡蝉不同数据集判别分析结果

Table 1  Results of canonical discriminant analysis about different data sets from three plant hopper species

数据类型
Data types
种类
Species
预测组成员(%)Predicted group membership (%)
柿广翅蜡蝉
R. sublimbata
八点广翅蜡蝉
R. spechlum
透明广翅蜡蝉
E. clara
综合数据a1,c1
Aggregate data
原始
Original
柿广翅蜡蝉 R. sublimbata98.60.01.4
八点广翅蜡蝉 R. spechlum0.095.05.0
透明广翅蜡蝉 E. clara0.00.799.3
交叉验证b
Cross-validated
柿广翅蜡蝉 R. sublimbata98.60.01.4
八点广翅蜡蝉 R. spechlum5.090.05.0
透明广翅蜡蝉 E. clara0.00.799.3
实际测量数据a2,c2
Direct measurement data
原始
Original
柿广翅蜡蝉 R. sublimbata98.60.01.4
八点广翅蜡蝉 R. spechlum10.080.010.0
透明广翅蜡蝉 E. clara0.00.799.3
交叉验证b
Cross-validated
柿广翅蜡蝉 R. sublimbata98.60.01.4
八点广翅蜡蝉 R. spechlum10.080.010.0
透明广翅蜡蝉 E. clara0.00.799.3
二次数据a3,c3
Secondary data
原始
Original
柿广翅蜡蝉 R. sublimbata97.80.02.2
八点广翅蜡蝉 R. spechlum55.040.05.0
透明广翅蜡蝉 E. clara8.00.791.3
交叉验证b
Cross-validated
柿广翅蜡蝉 R. sublimbata97.80.02.2
八点广翅蜡蝉 R. spechlum65.030.05.0
透明广翅蜡蝉 E. clara8.00.791.3

a1: 98.6% of original grouped cases correctly classified; a2: 97.6% of original grouped cases correctly classified; a3: 90.9% of original grouped cases correctly classified; b: Cross validation is done only for those cases in the analysis. In cross validation, each case is classified by the functions derived from cases other than that case. c1: 98.3% of cross-validated grouped cases correctly classified; c2: 97.6% of cross-validated grouped cases correctly classified; c3: 90.2% of cross- validated grouped cases correctly classified.

a1:98.6%正确分类的原始分组个案;a2:97.6% 正确分类的原始分组个案;a3:90.9%正确分类的原始分组个案;b:仅为分析中的个案进行交叉验证。在交叉验证中,每个个案根据源自所有个案(除了此个案)的函数进行分类;c1:98.3%正确分类的交叉验证分组个案;c2:97.6% 正确分类的交叉验证分组个案;c3:90.2%正确分类的交叉验证分组个案。

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3 结论与讨论

通过提取柿广翅蜡蝉、八点广翅蜡蝉和透明广翅蜡蝉前翅轮廓形态特征和统计分析,初步认为同一种蜡蝉左右翅无显著差异,其研究意义在于当样本量有限的情况下,可以使用一侧翅代替被破坏的翅进行特征提取。本研究只选用3种蜡蝉的前翅轮廓形态特征作为研究对象,主要从三个方面考虑,一是这3种蜡蝉在信阳地区属于果园、茶园和绿化树上的重要潜在性害虫,样本获取相对容易。通过对其进行图像处理与分析,为将来在复杂环境背景下此类特征的提取与应用奠定基础(Deng et al.,2018);二是3种蜡蝉前翅均有不同程度的不透明区域,且翅脉较多,结构复杂,通过手动(Rohlf, 2015)或自动(Tofilski,2004)获取翅脉交叉点等特征时有一定的困难,工作量较大或目前软件无法实现。而利用几何形态学方法提取轮廓特征更具有实用价值和应用前景;三是在自然状态下,此类昆虫静栖时前翅完全覆盖后翅,且前翅翅面基本构成了它们的种类特征(周尧和路进生,1977),为便于利用局部特征对其进行种类自动鉴定进行方法探索。

3种蜡蝉雌雄间各参数差异显著性不同。其中,5个实际测量参数在柿广翅蜡蝉性别间的差异均达到极显著水平;八点广翅蜡蝉和透明广翅蜡蝉除周长的差异仅为显著水平和不显著外,其余4个参数也达到极显著水平。然而,二次参数在不同蜡蝉雌雄间的差异较大。其中,球状性和叶状性在3种蜡蝉雌雄间的差异均未达到显著水平;柿广翅蜡蝉雌雄间在偏心率、紧凑度和圆形度方面的差异仅达到显著水平,而八点广翅蜡蝉雌雄间在5个二次参数中的差异均未达到显著水平。透明广翅蜡蝉除偏心率在雌雄间的差异达到极显著水平外,其余二次参数的差异也未达到显著水平。

通过不同数据集比较3种蜡蝉各参数的差异,本研究认为除周长在三个数据集中种间差异不显著、雄虫的圆形度差异不显著外,其它参数在种间均表现出差异显著或极显著,这些参数在进行此类昆虫种类自动鉴定中具有重要意义。典型判别分析认为,在对3种蜡蝉样本进行分组判别时,无论使用实际测量数据、二次数据或是综合数据,其原始判别和交叉验证判别的正确率均超过90%。其中,使用综合数据的交叉验证判别正确率最高为98.3%,实际测量数据为97.6%,二次参数为90.2%。

本文通过分析雌雄间和种间前翅轮廓形态参数,发现通过BugShape软件提取到的两类参数在种间差异显著,而在雌雄个体间,二次参数在不同种蜡蝉中差异各不相同,其中在透明广翅蜡蝉雌雄个体间均无显著差异。聚类判别结果也表明,利用二次参数对蜡蝉种类的正确判别率最低,其交叉验证判别率为90.2%,低于实际测量数据的结果。据报道,综合利用翅形特征和其它部位特征会提高对昆虫种类判别的正确率。比如利用翅形特征对蝶角蛉科Ascalaphidae昆虫进行自动判别的正确率为90%-98%,如果事先将复眼特征作为分组依据,正确率会提升到99%(Yang et al.,2015)。然而,对于一些膜翅目Hymenoptera昆虫来说,翅形的变化常常会影响对某些昆虫,比如胡蜂亚科Vespinae昆虫种类的判定,但同属、同种或同一种群内翅形的变化还会受到昆虫体形和气候等因素的影响(Perrard et al.,2014)。本文所研究的蜡蝉属于半翅目Hemiptera昆虫,其翅形是否会受到这些因素的影响有待进行深入研究。

近几年由于人们对环境压力的关注,出现很多通过研究昆虫左右结构的不对称性来揭示温度和农药等因素对昆虫的影响(Chang et al.,2007)。其中,以昆虫左右翅结构的不对称性的报道较多(Nattero et al.,2017;Leonard et al.,2018)。本文对3种蜡蝉前翅轮廓形态结构进行了分析,初步认为标本采集地的3种蜡蝉中,每种蜡蝉左右翅轮廓结构无显著差异。

致谢

中国农业大学高灵旺副教授和沈佐锐教授在软件使用和论文写作过程中提供宝贵建议和指导,在此一并表示感谢!

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