CN107144533A - 一种基于高光谱成像技术的一点红萝卜空心鉴别方法 - Google Patents
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Abstract
本发明公开一种基于高光谱成像技术的一点红萝卜空心鉴别方法,包括步骤:1)获取一点红萝卜的高光谱反射光谱;2)将建模集中一点红萝卜切开,观察判断其是否空心,并将空心样品与非空心样品标注、分类;3)使用步骤1)中获得的反射光谱信息建立支持向量机鉴别模型,分类标准为1‑非空心,2‑空心;4)针对未知空心的待测一点红萝卜样品,重复步骤1)中的操作;5)将获得的待测一点红萝卜样品的光谱信息输入支持向量机鉴别模型,判断待测样品是否空心。本发明主要用于通过获取已知是否空心的一点红萝卜样品的高光谱信息,并以此为基础建立支持向量机鉴别模型,无损鉴别一点红萝卜的空心与否,鉴别过程快捷、简便,鉴别结果准确可靠。
Description
技术领域
本发明属于光谱分析领域,具体涉及一种基于高光谱成像技术的一点红萝卜空心鉴别方法。
背景技术
萝卜(Raphanus sativus L.),二年或一年生草本植物,是一种重要的农作物;其肉质直根呈长圆形、球形或圆锥形,可作蔬菜食用。萝卜种植历史悠久,繁育品种良多。在浙江省杭州市,每年冬季,会上市一种本地萝卜,其肉质根呈纺锤形,地上部分为粉红色或紫红色,地下部分为白色,俗称“一点红”。该品种萝卜肉质致密,皮薄光滑,脆糯清鲜,品质上佳,为杭州民众所喜爱。但一点红萝卜在自然情况下存在空心问题,其空心率约为1/3。萝卜空心,即萝卜根部在生长成熟的中后期和采后的贮藏期内,其薄壁细胞缺乏可利用的营养物质,逐渐出现气泡,继而产生细胞空隙,最后形成空心的状况。将一点红萝卜切开,依据生活经验用肉眼判断其是否空心是目前鉴别一点红萝卜空心的主要方法。该方法需要判断者具有一定的生活经验且较为费时费力,具破坏性。
结合成像技术与光谱检测技术,在光谱维度上进行细致分割的高光谱成像技术能够同时获得待测目标的光谱信息和图像信息,并提供可视化的信息表达。在覆盖可见光波段的400-1000nm波段间,待测目标经高光谱照射而得到的反射光既包含待测目标的大小、外观、缺陷等外部指标的图像信息,同时也包含反映待测目标物理结构、化学成分等内部参数的光谱信息。利用反映待测目标内部参数的光谱信息,加以化学计量学方法辅助,高光谱成像技术可以实现农产品的快速无损检测。
发明内容
基于以上分析,本发明提供一种基于高光谱成像技术的一点红萝卜空心鉴别方法。通过高光谱成像技术分别获取空心一点红萝卜与非空心一点红萝卜在400-1000nm间的图像信息与光谱信息,利用原始光谱建立支持向量机模型,采集待测一点红萝卜的高光谱图像,使用已建立的支持向量机模型快速鉴别待测一点红萝卜是否空心。
本发明所采用的具体技术方案如下:
一种基于高光谱成像技术的一点红萝卜空心鉴别方法,包括以下步骤:
1)取一定数量的一点红萝卜样品,用作建模集;
2)使用高光谱成像技术照取一点红萝卜样品,获取其反射光谱,反射光谱范围是覆盖可见光波段的400-1000nm;
3)将建模集中一点红萝卜切开,依据生活经验并用肉眼观察判断其是否空心,并将空心样品与非空心样品标注、分类;
4)使用2)中获得的原始反射光谱信息建立支持向量机鉴别模型,分类标准为1-非空心,2-空心;
5)针对未知空心的待测一点红萝卜样品,重复步骤2)中的操作;
6)将步骤5)中获得的待测一点红萝卜样品的原始光谱信息输入步骤4)得到的支持向量机鉴别模型,得出未知样品是否空心。
本发明中,建模集中一点红萝卜样品数量应≥40。选取用于建立模型的是一点红萝卜样品的原始光谱信息。
进一步改进的,选取用于建立模型的化学计量学方法是支持向量机。
作为进一步改进,支持向量机模型的相关参数如下:分类类型:C-SVC,核类型:多项式(polynomial),程度:3,C值0.5,g值0.00195。
本发明的有益效果是:
(1)实现了一点红萝卜空心的无损鉴别,鉴别过程快捷、简便;
(2)应用高光谱成像技术结合化学计量学方法对一点红萝卜空心进行无损检测,避免检测过程对一点红萝卜造成的损伤,减少传统鉴别方法对一点红萝卜造成的经济损失;
(3)可根据实际情况,调整支持向量机模型的相关参数,以用于其余品种萝卜的空心鉴别。
附图说明
图1是基于高光谱成像技术的一点红萝卜空心鉴别方法的高光谱成像装置示意图;
图中:1为暗箱,2为高光谱相机,3为光谱仪,4为镜头,5为光源,6为样品台,7为马达,8为计算机;
图2是基于高光谱成像技术的一点红萝卜空心鉴别方法的流程图。
具体实施方式
如图1所示,高光谱成像装置包括:暗箱1,高光谱相机2,光谱仪3,镜头4,光源5,样品台6,马达7和计算机8。
如图2所示,采用上述装置鉴别一点红萝卜空心的方法具体如下:
1)取一定数量的一点红萝卜样品作为建模集,并逐一将样品置于暗箱中的样品台上,记录各样品编号;
2)用高光谱相机照射样品,获取样品在400-1000nm波段间的高光谱反射光谱;
3)逐一切开1)中的一点红萝卜样品,依据生活经验判断其是否空心,非空心萝卜归入类别1,空心萝卜归入类别2,并记录其编号;
4)使用2)中获得的原始光谱信息,以3)中的分类标准及结果在The UnscramblerX中建立支持向量机鉴别模型;
5)将未知空心的待测一点红萝卜样品以步骤1)和步骤2)中的方法同样操作;
6)利用4)中建立的支持向量机鉴别模型,鉴别待测一点红萝卜样品是否空心,并得出结论。
本发明所选定的支持向量机模型参数只针对一点红萝卜空心的快速鉴别,对其余品种萝卜空心的鉴别不适用;如需鉴别其余品种的萝卜是否空心,需重新选定参数,建立支持向量机鉴别模型。
上述具体实施方式用以解释说明本发明,而不是对本发明进行限制,在本发明的精神和权利要求的保护范围内,对本发明做出的任何修改和改变,都落入本发明的保护范围。
Claims (5)
1.一种基于高光谱成像技术的一点红萝卜空心鉴别方法,其特征在于,包括以下步骤:
1)获取一点红萝卜的高光谱反射光谱,光谱范围是400-1000nm;
2)将建模集中的一点红萝卜切开,观察判断其是否空心,并将空心样品与非空心样品标注、分类;
3)使用步骤1)中获得的高光谱反射光谱信息建立支持向量机鉴别模型,分类标准为1-非空心,2-空心;
4)针对未知空心的待测一点红萝卜样品,重复步骤1)中的操作;
5)将步骤4)中获得的待测一点红萝卜样品的高光谱反射光谱输入步骤3)得到的支持向量机鉴别模型,得出未知样品是否空心。
2.如权利要求1所述的基于高光谱成像技术的一点红萝卜空心鉴别方法,其特征在于,用于建模的一点红萝卜样品数量应≥40。
3.如权利要求1所述的基于高光谱成像技术的一点红萝卜空心鉴别方法,其特征在于,选取用于建立模型的是一点红萝卜样品的原始光谱信息。
4.如权利要求1所述的基于高光谱成像技术的一点红萝卜空心鉴别方法,其特征在于,选取用于建立模型的化学计量学方法是支持向量机。
5.如权利要求1所述的基于高光谱成像技术的一点红萝卜空心鉴别方法,其特征在于,选取的支持向量机模型的相关参数如下:分类类型:C-SVC,核类型:多项式(polynomial),程度:3,C值0.5,g值0.00195。
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Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101832926A (zh) * | 2010-03-19 | 2010-09-15 | 江南大学 | 一种利用高光谱图像技术进行苹果粉质化无损检测方法 |
CN103822879A (zh) * | 2014-02-24 | 2014-05-28 | 西北农林科技大学 | 一种基于高光谱成像技术的猕猴桃膨大果无损检测方法 |
CN104280349A (zh) * | 2014-10-28 | 2015-01-14 | 南京农业大学 | 一种基于高光谱图像对白萝卜糠心鉴定的方法 |
CN105158186A (zh) * | 2015-09-17 | 2015-12-16 | 南京农业大学 | 一种基于高光谱图像对白萝卜黑心检测的方法 |
CN105954202A (zh) * | 2016-04-22 | 2016-09-21 | 浙江大学 | 一种柑橘溃疡病高光谱模型传递的方法 |
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Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101832926A (zh) * | 2010-03-19 | 2010-09-15 | 江南大学 | 一种利用高光谱图像技术进行苹果粉质化无损检测方法 |
CN103822879A (zh) * | 2014-02-24 | 2014-05-28 | 西北农林科技大学 | 一种基于高光谱成像技术的猕猴桃膨大果无损检测方法 |
CN104280349A (zh) * | 2014-10-28 | 2015-01-14 | 南京农业大学 | 一种基于高光谱图像对白萝卜糠心鉴定的方法 |
CN105158186A (zh) * | 2015-09-17 | 2015-12-16 | 南京农业大学 | 一种基于高光谱图像对白萝卜黑心检测的方法 |
CN105954202A (zh) * | 2016-04-22 | 2016-09-21 | 浙江大学 | 一种柑橘溃疡病高光谱模型传递的方法 |
Non-Patent Citations (1)
Title |
---|
胡鹏程等: "高光谱图像对白萝卜糠心的无损检测", 《食品科学》 * |
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