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1、 库尔勒香梨论文：库尔勒香梨果形分析及外观质量自动分级方法的研究【中文摘要】本文分析了库尔勒香梨果形分类和质量分级技术现状及目前存在的问题。目前,由于香梨分级手段落后,效率低,造成香梨果形参差不齐,果实大小不一,严重影响其在国际水果市场的竞争力。因此,对香梨进行严格的果形分析和外观质量分级,对于采取不同的贮藏、销售和加工措施是非常有必要的。本文通过采用数字图像处理技术对香梨的果形特征参数、形状系数和几何特性参数进行了定量分析,利用基于聚类分析的综合人工神经网络算法和统计分析的方法,实现了香梨的畸形果判别分类、标准果形分类,并确定了预测香梨质量的数学模型,为香梨质量自动分级的设计和发展提供研究基
2、础。具体研究内容和结论如下：1.基于聚类分析的综合人工神经网络算法的香梨畸形果判别方法研究。采用均分直线法得到20个香梨的果形特征参数,选用的BP网络有一个隐含层,分别为网络隐含层和输出层选取S型对数激活函数和线性激活函数,输入为香梨的果形特征参数,输出节点为3个。利用设计的BP网络对香梨进行训练和测试。实验结果表明,平均正确率80%,分级效果较好。2.香梨标准果形的分类方法研究。根据纵截面的形状香梨的标准果形分为近圆形、卵圆形和纺锤形三类,以纵横比Ar作为量化指标,近圆形香梨纵横比在0.98-1.12的范围内变化；而卵圆形和纺锤形香梨的纵横比的变化范围分别为1.12-1.29和1.28-1.
3、39。根据横截面的形状香梨的标准果形分为圆状和椭圆状,以圆度比Rr作为量化指标,圆状香梨的圆度比变化范围为1.00-1.10；椭圆状香梨的圆度比变化范围为1.10-1.15；同时当香梨横截面的圆度比Rr1.10且纵截面的纵横比Ar1.29时,香梨果形为标准果形,而此区域之外被认为非标准果形。3.香梨质量预测方法研究。香梨的轴径参数、投影面积参数和体积均与其质量有较好的相关性。在基于轴径的质量预测模型中,以香梨长轴和中轴b或短轴c为自变量的线性质量预测模型更为适宜；在基于投影面积的质量预测模型中,以投影面积Pab或Pac为自变量的乘幂形式质量预测模型更为适宜；在基于体积的质量预测模型中,则推荐使
4、用以香梨的体积值Vosp为自变量的乘幂形式模型,可进行香梨的质量准确预测。从高相关性和测量的简便性两方面考虑,最终确定以投影面积Pab为自变量的乘幂形式,即M=0.0079Pab1.197,R2=0.9899作为最佳的质量预测模型。4.库尔勒香梨果形分析与外观质量自动分级软件开发。编写了基于Matlab的库尔勒香梨果形分析及外观质量自动分级软件系统,采用面向对象的程序设计方法,各模块分别实现香梨的畸形果判别、标准果形的分类和香梨的质量预测,实现了库尔勒香梨果形分析和外观质量的自动分级。【英文摘要】Since current methods for classification and anal
5、ysis of fruit shape and mass for Korla fragrant pear are behindhand, this study aimed to develop feasible methods to determine the classification of Korla fragrant pear shape and models for the mass predicting. The shape characters and geometrical parameters are determined using image processing met
6、hods. The discriminate on normal and misshapen fruit shape, the classification of normal fruit shape, models for mass prediction of Korla fragrant pears based on these geometrical parameters are identified. All above can supply basis for the research of mass classification using machine vision metho
7、d. Main contents and conclusions of this study were listed as follows:The study of discriminate of normal and misshapen fruit shape based on the integrated neural network ensemble (InNNE) based on clustering technology. The 20 shape characters are determined by the straight-line divided method from
8、the edge of Korla fragrant pear, which are the input layer of the BP neural network. The activation function of output layer are the logarithm and linear on, which include 3 nodes. It can be concluded that the average accuracy is about 80%.The study on classifying for normal fruit shape of Korla fra
9、grant pear:three normal fruit shapes in Korla fragrant pear based on the A.R are sub orbicular, ovoid and spindle. The A.R of sub orbicular pear range from 0.98 to 1.14, and for ovoid and spindle pear is 1.15 to 1.27 and 1.28 to 1.39.The study on predicting models of the mass for Korla pear:Differen
10、t linear and nonlinear regression models for mass prediction of Korla fragrant pears based on these geometrical parameters were identified. The results indicated that among the mass prediction models of Korla fragrant pear based on diameters, the linear model with the independent variables as major
11、and intermediate diameter (or minor diameter) had higher correlation. Also, among the mass prediction models based on the projected area, the polynomial model with the independent variable as the projected area which includes the major diameter can be optimum. The results showed that it is feasible
12、to predict the mass of Korla fragrant pear use the model M=0.0079Pab1.197, R2= 0.9899, which is more feasible.The development of automatic classification software for Korla fragrant pear:the Object oriented programming method is used to design the automatic classification software for Korla fragrant
13、 pear based on MATLAB, All the modules realized the discriminate of normal and misshapen fruit shape, classifying of the normal fruit shape and mass predicting.【关键词】库尔勒香梨 图像处理 果形 质量预测 分级【英文关键词】Korla pear Image processing methods Fruit shape Mass predicting Classification【目录】库尔勒香梨果形分析及外观质量自动分级方法的研究摘要
14、5-6Abstract6第一章 绪论9-141.1 研究目的和意义91.2 国内外研究现状分析9-111.2.1 果实果形分类研究现状及进展9-101.2.2 果实质量分级研究现状及进展10-111.3 研究内容11-121.4 研究目标12-131.5 技术路线131.6 本章小结13-14第二章 香梨畸形果的判别方法研究14-282.1 引言142.2 材料与方法14-272.2.1 香梨取样14-152.2.2 香梨数字图像的采集152.2.3 香梨数字图像的处理15-202.2.4 香梨果形特征参数的提取20-212.2.5 基于聚类分析的神经网络果形分类方法21-272.3 结果与分
15、析272.3.1 香梨畸形果与标准果的区分272.4 本章小结27-28第三章 香梨标准果形分类方法研究28-373.1 引言283.2 材料与方法28-343.2.1 香梨的取样28-293.2.2 香梨三轴径参数的实测293.2.3 香梨三轴径参数的数字图像法测量29-333.2.4 香梨形状系数的确定33-343.3 结果与分析34-353.3.1 香梨的纵截面形状分析343.3.2 香梨的横截面形状分析34-353.3.3 香梨标准果形的划分353.4 本章小结35-37第四章 香梨质量的预测方法研究37-474.1 引言374.2 材料与方法37-414.2.1 香梨取样374.2.2 香梨三投影面积的实测37-384.2.3 香梨三投影面积的数字图像法测量38-404.2.4 香梨体积的确定404.2.5 香梨质量预测模型的构建方法40-414.3 结果与分析41-46
