基于支持向量机的多光谱成像稻谷品种鉴别

为解决稻谷品种的快速无损鉴别问题,应用多光谱图像采集设备（VideometerLab）获取了5个品种稻谷共250个试验样本在405-970 nm波长范围内的多光谱图像,提取各品种稻谷在不同波长下的光谱反射率和图像特征（面积,宽长比,色差等）作为稻谷品种鉴别的特征变量,基于最小二乘支持向量机（least-square-support vector machine,LS-SVM）建立鉴别模型,通过粒子群寻优（particle swarm optimization,PSO）算法搜索支持向量机的最优参数。将250个稻谷分为建模集（200个样本）和测试集（50个样本）分别进行试验,结果表明,采用该文的建...

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Bibliographic Details
Published in农业工程学报 Vol. 30; no. 10; pp. 145 - 151
Main Author 刘伟 刘长虹 郑磊
Format Journal Article
LanguageChinese
Published 合肥工业大学医学工程学院，合肥 230009 2014
合肥学院机器视觉与智能控制实验室,合肥,230601%合肥工业大学生物与食品工程学院,合肥,230009%合肥工业大学生物与食品工程学院，合肥 230009
Subjects
农作物
多光谱图像
支持向量机
无损检测
稻谷品种
粒子群寻优
multispectral imaging
支持向量机
稻谷品种
nondestructive examination
support vector machines
农作物
多光谱图像
rice seed
无损检测
粒子群寻优
crops
particle swarm optimization
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Summary:为解决稻谷品种的快速无损鉴别问题,应用多光谱图像采集设备（VideometerLab）获取了5个品种稻谷共250个试验样本在405-970 nm波长范围内的多光谱图像,提取各品种稻谷在不同波长下的光谱反射率和图像特征（面积,宽长比,色差等）作为稻谷品种鉴别的特征变量,基于最小二乘支持向量机（least-square-support vector machine,LS-SVM）建立鉴别模型,通过粒子群寻优（particle swarm optimization,PSO）算法搜索支持向量机的最优参数。将250个稻谷分为建模集（200个样本）和测试集（50个样本）分别进行试验,结果表明,采用该文的建模方法结合稻谷光谱特征和图像特征对预测集稻谷品种鉴别的正确率均在90%以上,高于对比的其他方法,该研究成果为稻谷品种的快速无损鉴别提供了一种方法。
Bibliography:11-2047/S
Rice variety identification is important in seed industry to assure rice seed purity and quality. The objective of this study was to assess the feasibility of a rapid and nondestructive determination of varieties of rice seeds using multispectral imaging system. A total of 250 seeds（five varieties with 50 seeds each） were provided by Institute of Rice Research, Anhui Academy of Agricultural Sciences, Hefei, China. The seeds were divided into two groups such as calibration set（40 seeds of each variety） and validation set（10 seeds of each variety）. The multispectral imaging analysis was performed using the VideometerLab equipment（Videometer A/S, H？rsholm,Denmark） which acquired the multispectral images at 19 different wavelengths from the visual to the lower wavelengths of the NIR region（in the range of 405-970 nm）. Image segmentation was performed using the VideometerLab software version 2.12.23. Background of the image was removed by a Canonical Discriminant Analysis（CDA） and rice seed images were se
ISSN:1002-6819
DOI:10.3969/j.issn.1002-6819.2014.10.018