基于光谱和高光谱图像技术的蚕茧品质无损检测研究
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摘要
栽桑养蚕是我国的传统经济农业,在出口创汇和国民经济发展等方面发挥了重要的历史作用,一直受到国家的重视和扶持。作为蚕桑产业最主要的产物,蚕茧一方面作为商品进行市场流通,另一方面作为缫丝工业的原料生产丝制产品。所以对于蚕茧品质的精确评价,不仅可以作为蚕茧定价的依据,还可以根据不同等级的蚕茧原料生产不同品质的生丝。但是随着市场经济的发展和科学技术的进步,我国现行的蚕茧质量评价方法呈现了操作复杂、执行率低、科技化和自动化水平差等问题,已无法满足国际市场交易和缫丝工业对原料的需求。作为世界上最大的蚕茧生产和出口国,我国在国际市场上不仅需要数量上的绝对优势,在原料和产品质量方面也需要形成有效的竞争力。为了维护我国蚕桑生产的国际地位,有必要加强对蚕茧品质检测的研究和推广,提高蚕茧生产和管理的信息化水平,提升蚕茧品质与丝制产品增值效应,促进蚕桑产业的进一步发展。本论文基于我国现行的蚕茧质量评价标准,结合在蚕茧产销过程中存在的一些实际情况,进行了蚕茧品质无损检测方面的研究,主要研究内容和成果如下：
(1)采用400～1000nm波段的可见／近红外光谱技术分别对鲜茧和干茧的上蔟时间进行了检测。选用基线校正对光谱信号进行了预处理,通过无信息变量消除法(UVE)结合连续投影算法(SPA)分别提取了对应的特征波长,并基于特征波长建立了Bayes判别方法,对鲜茧上蔟时间的预测准确率为91.11%,对干茧上蔟时间的预测准确率为75.56%。说明基于可见／近红外光谱的蚕茧上蔟时间检测具有一定的可行性,且检测效果较好。
(2)应用回归系数法(RC)和CARS变量选择法结合SPA对1250～2500nm波段的光谱数据进行了特征波长选取,分别建立了基于近红外光谱技术的鲜茧和干茧上蔟时间的Bayes判别方法,该方法对鲜茧和干茧的预测准确率分别为70.00%和47.77%,检测效果略差于可见／近红外光谱。
(3)建立了基于可见／近红外光谱技术的鲜茧茧层含水率和干壳量的快速检测模型。比较了RC、UVE和CARS的特征波长选取效果,并提出了基于SPA的二次变量选择方法,实现了特征波长的有效提取。最终建立的基于特征波长的多元线性检测模型对茧层含水率和干壳量的预测相关系数分别为0.8473和0.8143,实现了基于可见／近红外光谱的桑蚕鲜茧茧层含水率和干壳量的精准高效检测。
(4)根据鲜茧茧层所含水分在1250～2500nm波段的近红外光谱特性,建立了鲜茧茧层含水率和干壳量的线性检测模型。研究了5种预处理方法的光谱处理效果,通过相关系数和均方根误差等参数对几种特征波长选取方法的结果进行了比较,最终选择了RC-SPA方法提取的9个特征波长用于茧层含水率检测,以及CARS-SPA方法提取的6个特征波长用于干壳量检测,最终得到的多元线性检测模型对茧层含水率和干壳量的预测相关系数分别为0.7873和0.6992,有助于实现鲜茧收购过程中的快速评级定价。
(5)研究了随机抽样法、浓度排序法、Kennard-Stone(?)和SPXY(?)等校正集样本挑选方法,基于挑选结果建立了鲜茧茧层含水率和干壳量的偏最小二乘(PLS)模型,比较结果显示Kennard-Stone法挑选的校正集样本具有更好的代表性,效果优于其余三种方法。
(6)比较了400～1000nm的可见/近红外光谱技术和1250～2500nm的近红外光谱技术对干茧茧层丝胶溶失率的检测效果,基于CARS-SPA方法从400～1000nm波段提取的13个特征波长建立的最小二乘-支持向量机(LS-SVM)模型的预测相关系数为0.8709,而基于UVE-SPA方法从1250～2500nm波段提取的9个特征波长建立的LS-SVM模型的预测相关系数为0.6893,结果显示可见/近红外光谱技术对干茧丝胶溶失率的检测效果较好,实现了基于光谱技术的桑蚕干茧丝胶溶失率的快速检测。
(7)应用高光谱成像系统,研究了基于450～900nm波段高光谱数据的蚕茧品质指标无损检测方法,最终得到的检测模型对鲜茧茧层含水率和干壳量的预测相关系数分别为0.4969和0.7838,对干茧茧层丝胶溶失率的预测相关系数为0.5585,对鲜茧和干茧上蔟时间的预测准确率分别为73.33%和64.44%,实现了基于高光谱图像技术的蚕茧品质综合检测。
(8)对蚕茧的高光谱图像进行了处理,通过图像二值化、直方图处理和图像反相等数字图像处理技术,得到了黄斑茧、柴印茧和畸形茧等下车茧的区域图像,基于图像特征参数对下车茧和正常茧的预测准确率为72.50%,实现了蚕茧表面缺陷的计算机识别,可将黄斑茧、柴印茧、瘪茧和畸形茧等次下茧与正常茧精确区分,有助于蚕茧的分级和定价。
As the traditional economic agriculture in China, sericulture has been playing an important historical role in export and social economy development with much attention and support. Cocoon was the most important product in sericulture, which could be traded in the market as the raw materials for silk production. So the the precise evaluation of the cocoon quality was useful for pricing and grading. However, with the development of market economy and scientific and technological progress, the current cocoon quality evaluation method in China has turned to be inopportune with complicated operation, low implementation and poor technical level. As the the world's largest cocoon producer and exporter, what we need is not only just the absolute advantage in quantity, but also the effective competitiveness of cocoon quality in the international market. In order to maintain the international status of our sericulture production, it is necessary to research the cocoon quality detection methods, to improve the informationalized level of cocoon production and management, to promote the further development of sericulture industry in China. This study is mainy focused on the nondestructive detection research of cocoon quality, according to the current evaluation criterions and the problems exsisted in real. The main research work and achievement were as follows:
(1) The mounting time of fresh and dried cocoon were detected with the visible and near infrared spectroscopy between400～1000nm. The spectral data was pre-processed with baseline correction, and the effective wavelengths were selected by elimination of uninformative variables (UVE) and successive projections algorithm (SPA). The Bayes discrimation models based on the effective wavelengths were built with the prediction accuracy of91.11% and75.56% for fresh and dried cocoon mounting time detection, separately.
(2) The regression coefficient (RC) and competitive adaptive reweighted sampling (CARS) were used for effective wavelengths selection between1250～2500nm. And the Bayes discrimation models based on the effective wavelengths were built with the prediction accuracy of70.00% and47.77% for fresh and dried cocoon mounting time detection, separately. The results showed that the effect for cocoon mounting time detection of near infrared spectroscopy was worse than visible and near infrared spectroscopy.
(3) The rapid detection models for moisture content (MC) of fresh cocoon layer and dry weight (DW) of the cocoon layer were built. The wavelengths selection results of RC, UVE and CARS were compared, and secondly selection with SPA was promoted for better effective wavelengths selection results. The correlation coefficients of multiple linear regression (MLR) models for MC and DW prediction (Rp) were0.8473and0.8143, separately.
(4) According to the spectral characters of water contained in cocoon layer between1250～2500nm, the MLR models for MC and DW detection of fresh cocoon were built with near infrared spectroscopy. The effects of5methods for spectral data pre-proccsing were researched. And the wavelength selection results of RC-SPA, UVE-SPA and CARS-SPA were systematically compared with the correlation coefficients and root mean square errors of PLS models built based on the selected wavelengths. Finally,9wavelengths were selected for MC detection by RC-SPA, and6wavelengths were selected for DW detection by CARS-SPA. The Rp of MLR models were0.7873和0.6992.
(5) The sampling methods of calibration set, as random, concentration sort, Kennard-Stone, sample set partitioning based on joint x-y distances (SPXY) were all used for MC and DW detection. And the sampling results showed that the samples selected by Kennard-Stone were better than others in representativeness of all samples.
(6) The results for sericin dissolubility (SD) of dried cocoon layer detection based on400-1000nm and1250～2500nm spectral data were compared.13effective wavelengths were selected from4000～1000nm by CARS-SPA, and9effective wavelengths were selected from1250～2500nm by UVE-SPA. The Rp of least square support vector machine (LS-SVM) models built based on the selected wavelengths were0.8709and0.6893. The results showed that the effect for cocoon SD detection of near infrared spectroscopy was worse than visible and near infrared spectroscopy.
(7) The hyperspectral imaging system was applied for the detection of cocoon quality. The Rp of models based on the hyperspectral data between450～900nm were0.4969for MC detection,0.7838for DW detection, and0.5585for SD detection. And the prediction accuracy of models for mounting time of fresh and dried cocoon were73.33% and64.44%, separately.
(8) The hyperspectral images of healthy and unhealthy cocoons were collected and processed by digital picture processing technique. The area-of-interest images of yellow spotted cocoons, cocoons pressed by cocooning frame and malformed cocoons were achived. And the prediction accuracy of Bayes discrimination model based on characteristic parameters of area-of-interest images was72.50%.
(1)采用400～1000nm波段的可见／近红外光谱技术分别对鲜茧和干茧的上蔟时间进行了检测。选用基线校正对光谱信号进行了预处理,通过无信息变量消除法(UVE)结合连续投影算法(SPA)分别提取了对应的特征波长,并基于特征波长建立了Bayes判别方法,对鲜茧上蔟时间的预测准确率为91.11%,对干茧上蔟时间的预测准确率为75.56%。说明基于可见／近红外光谱的蚕茧上蔟时间检测具有一定的可行性,且检测效果较好。
(2)应用回归系数法(RC)和CARS变量选择法结合SPA对1250～2500nm波段的光谱数据进行了特征波长选取,分别建立了基于近红外光谱技术的鲜茧和干茧上蔟时间的Bayes判别方法,该方法对鲜茧和干茧的预测准确率分别为70.00%和47.77%,检测效果略差于可见／近红外光谱。
(3)建立了基于可见／近红外光谱技术的鲜茧茧层含水率和干壳量的快速检测模型。比较了RC、UVE和CARS的特征波长选取效果,并提出了基于SPA的二次变量选择方法,实现了特征波长的有效提取。最终建立的基于特征波长的多元线性检测模型对茧层含水率和干壳量的预测相关系数分别为0.8473和0.8143,实现了基于可见／近红外光谱的桑蚕鲜茧茧层含水率和干壳量的精准高效检测。
(4)根据鲜茧茧层所含水分在1250～2500nm波段的近红外光谱特性,建立了鲜茧茧层含水率和干壳量的线性检测模型。研究了5种预处理方法的光谱处理效果,通过相关系数和均方根误差等参数对几种特征波长选取方法的结果进行了比较,最终选择了RC-SPA方法提取的9个特征波长用于茧层含水率检测,以及CARS-SPA方法提取的6个特征波长用于干壳量检测,最终得到的多元线性检测模型对茧层含水率和干壳量的预测相关系数分别为0.7873和0.6992,有助于实现鲜茧收购过程中的快速评级定价。
(5)研究了随机抽样法、浓度排序法、Kennard-Stone(?)和SPXY(?)等校正集样本挑选方法,基于挑选结果建立了鲜茧茧层含水率和干壳量的偏最小二乘(PLS)模型,比较结果显示Kennard-Stone法挑选的校正集样本具有更好的代表性,效果优于其余三种方法。
(6)比较了400～1000nm的可见/近红外光谱技术和1250～2500nm的近红外光谱技术对干茧茧层丝胶溶失率的检测效果,基于CARS-SPA方法从400～1000nm波段提取的13个特征波长建立的最小二乘-支持向量机(LS-SVM)模型的预测相关系数为0.8709,而基于UVE-SPA方法从1250～2500nm波段提取的9个特征波长建立的LS-SVM模型的预测相关系数为0.6893,结果显示可见/近红外光谱技术对干茧丝胶溶失率的检测效果较好,实现了基于光谱技术的桑蚕干茧丝胶溶失率的快速检测。
(7)应用高光谱成像系统,研究了基于450～900nm波段高光谱数据的蚕茧品质指标无损检测方法,最终得到的检测模型对鲜茧茧层含水率和干壳量的预测相关系数分别为0.4969和0.7838,对干茧茧层丝胶溶失率的预测相关系数为0.5585,对鲜茧和干茧上蔟时间的预测准确率分别为73.33%和64.44%,实现了基于高光谱图像技术的蚕茧品质综合检测。
(8)对蚕茧的高光谱图像进行了处理,通过图像二值化、直方图处理和图像反相等数字图像处理技术,得到了黄斑茧、柴印茧和畸形茧等下车茧的区域图像,基于图像特征参数对下车茧和正常茧的预测准确率为72.50%,实现了蚕茧表面缺陷的计算机识别,可将黄斑茧、柴印茧、瘪茧和畸形茧等次下茧与正常茧精确区分,有助于蚕茧的分级和定价。
As the traditional economic agriculture in China, sericulture has been playing an important historical role in export and social economy development with much attention and support. Cocoon was the most important product in sericulture, which could be traded in the market as the raw materials for silk production. So the the precise evaluation of the cocoon quality was useful for pricing and grading. However, with the development of market economy and scientific and technological progress, the current cocoon quality evaluation method in China has turned to be inopportune with complicated operation, low implementation and poor technical level. As the the world's largest cocoon producer and exporter, what we need is not only just the absolute advantage in quantity, but also the effective competitiveness of cocoon quality in the international market. In order to maintain the international status of our sericulture production, it is necessary to research the cocoon quality detection methods, to improve the informationalized level of cocoon production and management, to promote the further development of sericulture industry in China. This study is mainy focused on the nondestructive detection research of cocoon quality, according to the current evaluation criterions and the problems exsisted in real. The main research work and achievement were as follows:
(1) The mounting time of fresh and dried cocoon were detected with the visible and near infrared spectroscopy between400～1000nm. The spectral data was pre-processed with baseline correction, and the effective wavelengths were selected by elimination of uninformative variables (UVE) and successive projections algorithm (SPA). The Bayes discrimation models based on the effective wavelengths were built with the prediction accuracy of91.11% and75.56% for fresh and dried cocoon mounting time detection, separately.
(2) The regression coefficient (RC) and competitive adaptive reweighted sampling (CARS) were used for effective wavelengths selection between1250～2500nm. And the Bayes discrimation models based on the effective wavelengths were built with the prediction accuracy of70.00% and47.77% for fresh and dried cocoon mounting time detection, separately. The results showed that the effect for cocoon mounting time detection of near infrared spectroscopy was worse than visible and near infrared spectroscopy.
(3) The rapid detection models for moisture content (MC) of fresh cocoon layer and dry weight (DW) of the cocoon layer were built. The wavelengths selection results of RC, UVE and CARS were compared, and secondly selection with SPA was promoted for better effective wavelengths selection results. The correlation coefficients of multiple linear regression (MLR) models for MC and DW prediction (Rp) were0.8473and0.8143, separately.
(4) According to the spectral characters of water contained in cocoon layer between1250～2500nm, the MLR models for MC and DW detection of fresh cocoon were built with near infrared spectroscopy. The effects of5methods for spectral data pre-proccsing were researched. And the wavelength selection results of RC-SPA, UVE-SPA and CARS-SPA were systematically compared with the correlation coefficients and root mean square errors of PLS models built based on the selected wavelengths. Finally,9wavelengths were selected for MC detection by RC-SPA, and6wavelengths were selected for DW detection by CARS-SPA. The Rp of MLR models were0.7873和0.6992.
(5) The sampling methods of calibration set, as random, concentration sort, Kennard-Stone, sample set partitioning based on joint x-y distances (SPXY) were all used for MC and DW detection. And the sampling results showed that the samples selected by Kennard-Stone were better than others in representativeness of all samples.
(6) The results for sericin dissolubility (SD) of dried cocoon layer detection based on400-1000nm and1250～2500nm spectral data were compared.13effective wavelengths were selected from4000～1000nm by CARS-SPA, and9effective wavelengths were selected from1250～2500nm by UVE-SPA. The Rp of least square support vector machine (LS-SVM) models built based on the selected wavelengths were0.8709and0.6893. The results showed that the effect for cocoon SD detection of near infrared spectroscopy was worse than visible and near infrared spectroscopy.
(7) The hyperspectral imaging system was applied for the detection of cocoon quality. The Rp of models based on the hyperspectral data between450～900nm were0.4969for MC detection,0.7838for DW detection, and0.5585for SD detection. And the prediction accuracy of models for mounting time of fresh and dried cocoon were73.33% and64.44%, separately.
(8) The hyperspectral images of healthy and unhealthy cocoons were collected and processed by digital picture processing technique. The area-of-interest images of yellow spotted cocoons, cocoons pressed by cocooning frame and malformed cocoons were achived. And the prediction accuracy of Bayes discrimination model based on characteristic parameters of area-of-interest images was72.50%.
引文
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