基于统计复杂性测度、多重分形谱等方法的柑橘品质分级  

Citrus quality grading based on statistical complexity measurement and multifractal spectrum method

作　　者：曹乐平[1] 温芝元[2] 

机构地区：[1]湖南生物机电职业技术学院科研处,长沙410127 [2]湖南农业大学理学院,长沙410128

出　　处：《浙江大学学报（农业与生命科学版）》2015年第3期309-319,共11页Journal of Zhejiang University:Agriculture and Life Sciences

基　　金：湖南省科技计划项目(2011NK3005;2012NK4127)

摘　　要：为精确地度量柑橘品质分级,研究了病虫害为害状冰糖橙缺陷果实复杂性测度机器识别、脐橙果实周长-面积分形维数与分段色调单位坐标化多重分形谱高度/宽度的形状和颜色分级及糖酸度无损检测。对冰糖橙生理性缺硼、锈壁虱、油胞凹陷病3种常见病虫害果实为害状缺陷在0°—50°主色调区域实施长度为1°的分段,统计各分段色调区间像素分布概率,并计算统计复杂性测度C(Y)与Shannon信息熵H(Y),以C(Y)与H(Y)为检索词计算机查询果实病虫害检索表来进行病虫害缺陷果机器识别,平均正确识别率为93.33%。对脐橙果实果梗面与侧面在相垂直的2个投影面上的图像进行去背景与边界轮廓提取操作,计算边界轮廓周长-面积分形维数,以此为指标检索果实信息字典进行脐橙形状分级,正确率100%。以脐橙果实相对的2个侧面图像为研究对象,去其背景,将30°—120°主色调区域进行30°—50°、50°—70°、70°—90°和90°—120°的区间分割,生成4幅色调图像,计算此图像多重分形谱质心坐标、高度与宽度,对该高度与宽度进行单位质心坐标化处理,一方面以单位质心坐标化多重分形谱高度与宽度为指标检索果实信息字典进行脐橙颜色分级,正确率98%;另一方面以单位质心坐标化多重分形谱高度与宽度为参数通过糖酸度偏最小二乘模型映射果实糖酸度,糖度与酸度标准差分别在0.77及0.36以内,与实际值的相关系数分别在0.8及0.7以上。试验结果表明:统计复杂性测度、周长-面积分形维数、单位质心坐标化多重分形谱高度与宽度较精确地反映了柑橘分级中需识别的冰糖橙果实病虫害缺陷的特征、脐橙果实形状与颜色特性及内部糖酸度无损检测映射参数特点。Summary Citrus quality grading can raise the observability degree and grade degree of citrus,improving the product level and increasing market competitiveness.It can also make huge economic and social benefits and increase farmers income and agricultural productivity so as to promote the sustained and healthy development of the citrusindustry.For the purpose of precise measurement of citrus quality grading,the complexity measurement of Bingtang orange defective fruit damaged by diseases and insect pest patterns were studied by machine recognition,along with the navel orange fruit perimeter-area fractal dimension and the shape,color grading and sugar acid nondestructive detection of section tone unit coordinates multifractal spectrum height and width.Physiological boron deficiency,Eriophyes oleivorus and rind oil spotting disease were very common in Bingtang orange fruits.The 0°—50°main tone region of these diseases and insect pests damage pattern were augmented into the length of 1°,and pixel distribution probability of each segment tone,complexity measurement C（Y）and Shannon entropy H（Y）were calculated.C（Y）and H（Y）were set as the features to identify fruit diseases and insect pests by machine recognition.The background and extracting boundary contour from the two projection images formed by navel orange fruits’stalk surface and side perpendicular were removed,and then perimeter-area fractal dimension was calculated.The result was used as index to retrieve fruit information and navel orange shape grading.The two side images where navel oranges were relative were set as the research object,and then the background was extracted,and the main tone region 30°—120°of two images were segmented into 30°—50°,50°—70°,70°—90°and90°—120°,and four tone images were created.Its multifractal spectrum barycentric coordinate,height and width were calculated.The height and width were transformed into the unit barycentric coordinate.On one hand,multifractal spectrum height and width of the

关 键 词：冰糖橙与脐橙 复杂性测度 分形维数 多重分形谱 病虫害缺陷果机器识别 形状与颜色机器分级 糖酸度无损检测 

分 类 号：S126[农业科学—农业基础科学] S666.4