CN111351766A - 一种快速识别南瓜种子身份的方法 - Google Patents
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Abstract
本发明公开了一种快速识别南瓜种子身份的方法,属于作物育种中表型获取技术领域,包括:将所有品种的南瓜种子保存,控制室内温度湿度恒定;采用太赫兹脉冲采集南瓜样本的太赫兹图像信号,导出原始太赫兹光谱时域变换谱线,记为X,样本中南瓜所属品种记为Y;根据原始太赫兹光谱时域变换谱线X,找出南瓜对应的特征频域值;在X中提取出与南瓜特征频域值对应的太赫兹光谱折射率数据,记为X1,将所得数据(X1,Y)划分为建模集和预测集;分别以建模集中的X1作为输入,Y作为输出,构建南瓜品种与对应太赫兹时域变换谱线的折射率的卷积神经网络的多分类模型;将预测集中南瓜的光谱折射率X1带入卷积神经网络多分类模型中,得到待测南瓜的对应品种。
Description
技术领域
本发明涉及作物育种中表型获取技术领域,具体地说,涉及一种快速识别南瓜种子身份的方法。
背景技术
南瓜是一种具有较高营养价值的保健蔬菜,有很高的开发利用价值和潜力。南瓜的营养保健价值很高,而南瓜种子也含有丰富的脂肪酸、氨基酸、甾醇、蛋白质、维生素以及其它微量元素等,具有润肺、化痰、消痛以及利尿等功效,可以抗氧化,防治前列腺疾病,降低胆固醇,缓解高血压,预防和缓解心血管疾病,且其提取物具有驱虫作用,也被用在临床治疗中腹痛胀满及寄生虫病等,是一种具有保健功能的食品。
随着杂交育种的发展,需要对基因信息进行更深的挖掘,使得基因领域有了巨大的突破,但杂交育种中作物表型的传统获取方法无法满足高通量测序技术的发展。因此,需要一种高通量的表型获取方法可以实现南瓜杂交育种中表型信息的快速测定。
其中太赫兹时域光谱技术(THz-TDS)是通过宽频带太赫兹脉冲携带介质信息,对材料内部信息进行提取的一种光谱检测方法,常被用于材料的无损检测领域。是检测研究中新兴的光谱检测技术之一,在检测作物表型方面得到了广泛关注,因其以非接触无损的方式获取检测样本的生理和形态信息,能实时获取作物的表型信息,为检测筛选南瓜杂交育种提供了有力的技术支持。
公布号为CN106841054A的中国专利文献公开了一种种子品种识别方法,包括以下步骤:获取测试集中每个样本种子在P个波段下的P个高光谱图像;针对每个样本种子,根据P个高光谱图像获取特征参数;将特征参数输入分类模型,得到每个样本种子的预测品种;根据预测品种从测试集中选取出预定样本种子,并根据预定样本种子更新分类模型;利用更新后的分类模型识别出测试集中样本种子的品种。
以上方法进行种子品种识别时简历的分类模型不稳定,无法达到快速识别的目的。
发明内容
本发明的目的为提供一种快速识别南瓜种子身份的方法,可实现杂交育种中南瓜品种表型信息的获取和快速识别。
为了实现上述目的,本发明提供的快速识别南瓜种子身份的方法,包括以下步骤:
1)将所有品种的南瓜种子保存在室内,控制室内温度湿度恒定;
2)采用太赫兹脉冲采集南瓜样本的太赫兹图像信号,导出原始太赫兹光谱时域变换谱线,记为X,样本中南瓜所属品种记为Y;
3)根据原始太赫兹光谱时域变换谱线X,找出南瓜对应的特征频域值;
4)在X中提取出与步骤3)中南瓜特征频域值对应的太赫兹光谱折射率数据,记为X1,将所得数据(X1,Y)按比例划分为建模集和预测集;
5)分别以建模集中的X1作为输入,Y作为输出,构建南瓜品种与对应太赫兹时域变换谱线的折射率的卷积神经网络的多分类模型;
6)将预测集中南瓜的光谱折射率X1带入卷积神经网络多分类模型中,得到待测南瓜的对应品种。
上述技术方案中,实现了南瓜种子品种身份的快速识别,具有操作简单,检测成本低,高通量获取,准确度高等特点,有效克服了育种中表型信息传统获取和检测方法复杂,成本较高,对样品破坏大等缺点。
作为优选,步骤1)中,将南瓜种子均保存在牛皮纸袋里,将实验对象的其他外部的条件保持一致,仅仅控制一个品种变量。
作为优选,步骤2)中,原始太赫兹光谱时域变换谱线通过计算太赫兹时域光谱仪获得的时域光谱信息与样品厚度数据通过傅里叶变换得到。
作为优选,步骤2)中,对信号采集系统的参数进行优化,得到的优化参数为:采用1560nm飞秒激光器,带宽为100fs的780nm的飞秒脉冲。
作为优选,步骤5)中,卷积神经网络的多分类模型包含输入层,卷积层,池化层,全连接层,输出层五个模块,其中卷积模块有五层,由五层卷积和五层最大池化层构成,卷积核为3×3,过滤器数目由256依次下降,最大池化采用2×2,三层全连接层,神经节点由512依次下降,期间所使用的神经元激活函数均采用elu,最终到达输出层,使用多分类激活函数softmax,得到南瓜品种类别的输出。
作为优选,卷积神经网络的深度学习模式通过卷积层和全连接层一层层深入探究南瓜太赫兹光谱的更具代表性的特征信息,引入优化算法产生更准确的权重和偏置值,所述优化算法采用SGD学习速率lr0.01,动量参数momentum0.9,每次更新的学习率衰减值为0。
为防止过拟合现象,作为优选,在卷积模块中添加Batch Normalization,位于卷积层后最大池化前,以提高模型泛化能力。
与现有技术相比,本发明的有益效果为:
(1)本发明快速识别南瓜种子身份的方法实现了南瓜种子品种的快速鉴定,有利于杂交育种中南瓜种子开发便携式的传感仪器。
(2)本发明利用基于太赫兹时域光谱成像技术的品种鉴别检测,有效克服了传统检测方法程序复杂,成本较高,对样品破坏大等缺点,具有操作简单,成本低、快速高效准确等特点。
附图说明
图1为本发明实施例中深度学习的卷积神经网络的结构示意图。
具体实施方式
为使本发明的目的、技术方案和优点更加清楚,以下结合实施例及其附图对本发明作进一步说明。
实施例
本实施例中,快速识别南瓜种子身份的方法包括以下步骤:
步骤S1,样本采集和保存。本实施例采用的南瓜种子样品由浙江省农业科学院提供,实验对象共包含76个基因型的南瓜品种种子样本,分别是癞蛤南瓜、麦饼金瓜、香榧金瓜、葫芦金瓜、地雷南瓜、东阳老品种金瓜、老金瓜、城西南瓜、硬南瓜、温岭本地南瓜、高灯瓜、粟子瓜、大瓣瓜、长瓜、圆瓜、阔板籽夏南瓜、长柄秋南瓜、圆南瓜、炭瓮瓜、麻风瓜、拉丝皮南瓜、楼塔十姐妹、粉质南瓜、本地圆南瓜、本地南瓜、临海南瓜、菩毯南瓜、茶坑土南瓜、老南瓜、长癞子南瓜、癞子南瓜、梨形南瓜、蟠南瓜、疙瘩南瓜、金丝搅瓜、圆南瓜、长同南瓜、麻皮南瓜、四种麻子南瓜、两种长南瓜、四种土南瓜、十四种金瓜、二十一种其他类南瓜。均保存在牛皮纸袋中,置于温度和湿度不变的温室中。
步骤S2,采集样本的太赫兹成像光谱信号。将太赫兹仪器调整至投射成像模块的状态下,将其待测室充满30min氮气使信号达到稳定,尽量去除水分的影响。首先将透明胶带粘贴在样品架上并放置在待测室中采集其太赫兹信号作为背景信号,然后将南瓜样本固定在透明胶的样品架上放于待测室中,开始采集太赫兹成像信号,并导出原始太赫兹光谱时域变换谱线,记为X,样本中南瓜所属品种记为Y;
步骤S3,根据原始太赫兹光谱时域变换谱线X,找出南瓜主要特征的对应的特征谱线的频域值;
步骤S4,在X中提取出与步骤3)中南瓜特征频域值对应的太赫兹光谱折射率数据,记为X1,将所得数据(X1,Y)按2:1的比例对样本进行划分,得到建模集样本5000个,预测集样本2500个;
步骤S5,分别以建模集中的X1作为输入,Y作为输出,构建南瓜品种与对应太赫兹时域变换谱线的折射率的卷积神经网络的多分类模型;
步骤S6,将预测集中南瓜的光谱折射率X1带入卷积神经网络多分类模型中,得到待测南瓜的对应品种。
参见图1,本实施例中,卷积神经网络的多分类模型包含输入层,卷积层,池化层,全连接层,输出层五个模块,其中卷积模块有五层,由五层卷积和五层最大池化层构成,卷积核为3×3,过滤器数目由256依次下降,最大池化采用2×2,为防止过拟合现象,在卷积层后最大池化前添加Batch Normalization,以提高模型泛化能力。包含了三层全连接层,神经节点由512依次下降,期间所使用的神经元激活函数均采用elu,最终到达输出层,使用多分类激活函数softmax,得到南瓜品种类别的输出。
本实施例中,卷积神经网络的深度学习模式通过卷积层和全连接层一层层深入探究南瓜太赫兹光谱的更具代表性的特征信息,需要引入优化算法产生更准确的权重和偏置值,所述优化算法采用SGD参数为:学习速率lr0.01,momentum=0.9,nesterov=False。此模型建构采用python 3中的第三方库keras实现。结果表明,南瓜的真实品种和卷积网络模型预测的太赫兹时域变换光谱的对应的品种之间的吻合度较好,预测集的准确率达到了94%。
上述结果表明,本发明的方法能实现南瓜种子身份的快速识别,具有良好的应用前景。
Claims (7)
1.一种快速识别南瓜种子身份的方法,其特征在于,包括以下步骤:
1)将所有品种的南瓜种子保存在室内,控制室内温度湿度恒定;
2)采用太赫兹脉冲采集南瓜样本的太赫兹图像信号,导出原始太赫兹光谱时域变换谱线,记为X,样本中南瓜所属品种记为Y;
3)根据原始太赫兹光谱时域变换谱线X,找出南瓜对应的特征频域值;
4)在X中提取出与步骤3)中南瓜特征频域值对应的太赫兹光谱折射率数据,记为X1,将所得数据(X1,Y)按比例划分为建模集和预测集;
5)分别以建模集中的X1作为输入,Y作为输出,构建南瓜品种与对应太赫兹时域变换谱线的折射率的卷积神经网络的多分类模型;
6)将预测集中南瓜的光谱折射率X1带入卷积神经网络多分类模型中,得到待测南瓜的对应品种。
2.根据权利要求1所述的快速识别南瓜种子身份的方法,其特征在于,步骤1)中,将南瓜种子均保存在牛皮纸袋里,将实验对象的其他外部的条件保持一致,仅仅控制一个品种变量。
3.根据权利要求1所述的快速识别南瓜种子身份的方法,其特征在于,步骤2)中,原始太赫兹光谱时域变换谱线通过计算太赫兹时域光谱仪获得的时域光谱信息与样品厚度数据通过傅里叶变换得到。
4.根据权利要求1所述的快速识别南瓜种子身份的方法,其特征在于,步骤2)中,对信号采集系统的参数进行优化,得到的优化参数为:采用1560nm飞秒激光器,带宽为100fs的780nm的飞秒脉冲。
5.根据权利要求1所述的快速识别南瓜种子身份的方法,其特征在于,步骤5)中,所述的卷积神经网络的多分类模型包含输入层,卷积层,池化层,全连接层,输出层五个模块,其中卷积模块有五层,由五层卷积和五层最大池化层构成,卷积核为3×3,过滤器数目由256依次下降,最大池化采用2×2,三层全连接层,神经节点由512依次下降,期间所使用的神经元激活函数均采用elu,最终到达输出层,使用多分类激活函数softmax,得到南瓜品种类别的输出。
6.根据权利要求5所述的快速识别南瓜种子身份的方法,其特征在于,所述的卷积神经网络的深度学习模式通过卷积层和全连接层一层层深入探究南瓜太赫兹光谱的更具代表性的特征信息,引入优化算法产生更准确的权重和偏置值,所述优化算法采用SGD学习速率lr0.01,动量参数momentum0.9,每次更新的学习率衰减值为0。
7.根据权利要求5所述的快速识别南瓜种子身份的方法,其特征在于,在所述卷积模块中添加Batch Normalization,位于卷积层后最大池化前。
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