摘要
我国棉花生产发展目标是,棉花等级相符率不断提高,异性纤维问题、掺杂使假事件、棉包一致性差现象不断减少等。严把“采摘、收购、加工”关,减少异性纤维含量是解决这些问题的主要措施。本研究把现代机器视觉和模式识别等技术引入传统的采摘作业进行田间籽棉成熟度与品级的判别,为采摘机器人具备精确的作业能力奠定基础,以适应棉花品种的多样性、避免农业化学品引起的环境污染、降低劳动强度、减少农业投入成本等。取得的主要结论归纳如下:
1.将籽棉与背景视为二个类别,基于竞争学习网络分割了籽棉图像。在HSI、Lab、 Ohta、RGB颜色空间下,用带类别标签的训练样本计算k-均值聚类的误分类率,选取了误分类率最小的RGB颜色空间,其B值较优。在RGB颜色空间下,用带类别标签的训练样本的B值一次性地训练了竞争学习网络模型。结合形态学滤波,用所训练的竞争学习网络模型分割907张新图像的准确率达92.94%。该方法结合了有监督的学习算法,使图像分割在先验知识的指导下进行,同时,避免了K-均值聚类的反复迭代和过拟合现象,降低了计算开销,提高了图像分割的效率和精度。
2.基于频域提取了描述籽棉边界轮廓的频谱特征集。用8-邻域跟踪法获取了籽棉区域边界轮廓的坐标序列,基于傅里叶变换提取64个频谱特征,其相关系数都不超过0.9;单因子方差分析表明,大多数频谱特征在成熟和未成熟籽棉上的均值具有显著的差异。
基于空域提取了个描述籽棉形状的几何结构特征集。用一组同心园外接或切割籽棉区域,基于比值法提取了15个几何结构特征,它们是,全局特征、径向切割区域特征1-3、圆周向切割区域特征1~5、径向切割线特征1-2、圆周向切割线特征1-3以及计盒维数,其相关系数超过了0.9;单因子方差分析表明,大多数几何结构特征在成熟和未成熟籽棉上的均值具有显著的差异。
将籽棉是否成熟视为二类问题,基于过滤器模式从上述特征集中启发式搜索了特征子集,评估函数为类可分性准则。最优标量特征组合特征子集时,在训练集上计算每个特征的类可分性测量值并降序排列,将前l个(l=1、2、…、64或15)特征组合形成l维特征子集建立贝叶斯判别模型,随着特征子集容量l的增加,l维特征子集在训练集上的误分类率不断减小,验证集上的误分类率不再减小处为最佳特征子集容量,实验结果表明,频谱特征集的最佳特征子集容量为14,该特征子集在预测集上的识别率为77.78%;几何结构特征集的最佳特征子集容量为8,该特征子集在预测集上的识别率为85.56%。浮动搜索特征子集时,在训练集上浮动搜索具有最大类可分性测量值的l维特征子集建立贝叶斯判别模型,随着特征子集容量l的增加,l维特征子集在训练集上的误分类率不断减小,验证集上的误分类率不再减小处为最佳特征子集容量,实验结果表明,频谱特征集的最佳特征子集容量为22,该特征子集在预测集上的识别率为82.22%;几何结构特征集的最佳特征子集容量为8,该特征子集在预测集上的识别率为85.56%。
将籽棉是否成熟视为二类问题,基于封装器模式从上述特征集中穷举搜索了特征子集,评估函数为分类器的误分类率。在训练集上组合所有可能的l维(l=1、2、64或15)特征子集建立贝叶斯判别模型,并选择在验证集上具有最小误分类率的l维特征子集,随着特征子集容量l的增加,l维特征子集在训练集上的误分类率不断减小,验证集上的最小误分类率不再减小处为最佳特征子集容量,实验结果表明,频谱特征集的最佳特征子集容量为15,该特征子集在预测集上的识别率为83.33%;几何结构特征集的最佳特征子集容量为6,该特征子集在预测集上的识别率为88.89%。
3.基于相关分析选取了测量籽棉色泽的颜色空间。根据中国籽棉品级分级文字标准,在Hunter、Lab和HSI颜色空间下度量籽棉的色泽,相关分析表明,籽棉的色泽特征呈虚假相关;经灰度修正后,HSI颜色空间消除了虚假相关,可度量籽棉的色泽。
基于空域提取了描述籽棉品级的纹理、形状特征集。在HSI颜色空间下,基于亮度Ⅰ和饱和度S的直方图的均值、标准差、平滑度、三阶矩、一致性和熵,提取了12个纹理特征以描述籽棉的色泽、雨锈/轻霜/僵瓣/污染/烂桃等杂质;基于比值法提取了16个形状特征以描述籽棉的大小、几何结构分布,它们是,棉瓣个数、全局特征、径向切割区域特征1~3、圆周向切割区域特征1~5、径向切割线特征1~2、圆周向切割线特征1~3以及计盒维数;剔除相关系数超过0.9的纹理、形状特征后,基于特征集在1~7级样本上均值与品级值之间的回归分析,在0.05的显著性水平下选取了线性相关的有效纹理、形状特征集,它们是,亮度直方图的三阶矩、饱和度直方图的均值、标准差和熵、棉瓣个数、全局特征、圆周向切割区域特征(1,4)、径向切割线特征1、圆周向切割线特征3、计盒维数。
将成熟籽棉品级视为七类问题,基于10折交叉验证和过滤器模式从上述有效特征集中启发式搜索了特征子集。最优标量特征组合特征子集时,在每一个训练集上计算每个特征的类可分性测量值并降序排列,将前l个(l=1、2、…、11)特征组合形成l维特征子集建立贝叶斯判别模型,随着特征子集容量l的增加,l维特征子集在10个训练集上的平均误分类率不断减小,10个对应的验证集上的平均误分类率不再减小处为最佳特征子集容量,实验结果表明,有效特征集的最佳特征子集容量为7,10个训练集在此处建立了10个模型,第5个训练集在此处建立的模型较好,它在预测集上的识别率为74.71%。浮动搜索特征子集时,在每一个训练集上搜索具有最大类可分性测量值的l维特征子集建立贝叶斯判别模型,随着特征子集容量l的增加,l维特征子集在10个训练集上的平均误分类率不断减小,10个对应的验证集上的平均误分类率不再减小处为最佳特征子集容量,实验结果表明,有效特征集的最佳特征子集容量为6,10个训练集在此处建立了10个模型,第9个训练集建立的模型较好,它在预测集上的识别率为70.11%。
将成熟籽棉品级视为七类问题,基于10折交叉验证和封装器模式从上述有效特征集中穷举搜索了特征子集。在每一个训练集上组合所有可能的l维特征子集建立贝叶斯判别模型,并选择在对应验证集上具有最小误分类率的l维特征子集(l=1、2、11),随着特征子集容量l的增加,l维特征子集在10个训练集上的平均误分类率逐渐减小,10个对应的验证集上的最小误分类率的平均值不再减小处为最佳特征子集容量,实验结果表明,有效特征集的最佳特征子集容量为9,10个训练集在此处建立了10个模型,第6个训练集建立的模型较好,它在对应验证集上的最小误分类率最小,在预测集上的识别率为79.31%。
4.基于封装器模式穷举搜索的特征子集最优,它能够有效地剔除冗余特征、辨识关键特征,但计算开销大、速度慢。基于过滤器模式启发式搜索的特征子集次优,可能选出冗余特征,但计算量小,速度快,并可能搜索到过于乐观的特征子集,其验证集误差小于训练集误差,或者预测集误差小于验证集误差。基于交叉验证选择的模型稳定、可靠。
为了判别田间籽棉的成熟度,最终选择在空域中基于封装器模式穷举搜索的几何结构特征子集,该特征子集是径向切割区域特征(1,2)、圆周向切割区域特征(1,3,4)、径向切割线特征2,揭示了“成熟籽棉内部绽开外围饱满,未成熟籽棉内部紧实外围瘦小。”这一关键特性,它在预测集上的识别率为88.89%。
为了判别田间籽棉的品级,最终选择基于交叉验证和封装器模式穷举搜索的纹理、形状特征子集,该特征子集是白度三阶矩、黄度均值、黄度标准差、黄度熵、棉瓣个数、全局特征、圆周向切割区域特征1、径向切割线特征1和计盒维数,揭示了高品级棉花白度高、黄度低、杂质少、棉瓣紧凑/肥大/蓬松,低品级棉花白度低、黄度高、杂质多、棉瓣松散/瘦小/僵硬、淡灰棉/污染棉/烂桃棉多等特性,它在预测集上的识别率为79.31%。籽棉通常被误分入相邻品级,高品级籽棉和低品级籽棉比较容易被识别。
The development goals of cotton production were including, such as the corresponding rate of cotton quality grade improved greatly, the foreign fiber problem, the adulteration event, and the inconsistent phenomenon of cotton baling decreased continuously, etc. The main measures of solving these problems were supervising strictly picking, purchase, and process cotton and decreasing foreign fiber content. Modern technology, for example, machine vision and pattern recognition, was applied in traditional picking task to discriminate ripeness and quality grade of raw cotton, and thus laying a foundation for picking robot to exert exactly. Using robot to pick cotton could adapt various cotton varieties, avoid pollution caused by agriculture chemicals, and reduce labor waste and agriculture cost. The main and creative achievements as follows:
1. Raw cotton and its background were regarded as two classes and segmented based on competitive learning network. The training data with class label was classified into two classes based on K-means clustering in HSI, Lab, Ohta, RGB color space, and the error rate of the training data was lowest in RGB color space-particularly B value. The competitive learning network with B of the training data was trained onetime in RGB color space.907cotton images were segmented with an accuracy of92.94%based on the competitive learning network and morphological filtering. With the help of prior knowledge and supervisory learning, the image segmentation arithmetic obtained high precision and efficiency without iterative and over-fitting of K-means clustering as well as high computing cost.
2. The spectrum feature set used to describe boundary of raw cotton was extracted in frequency domain. A sequence of coordinates of boundary contour of cotton were obtained by using8-neighbour tracking, from which64spectrum features without0.9correlation coefficient were extracted based on Fourier transform. Single factor anova analysis showed that the differences of the average of most spectrum features of ripe and unripe cottons were significant at the0.05level.
The geometric structure feature set used to express cotton shape was extracted in spatial domain. Cotton region was circumscribed or cut by a set of concentric circles, from which15geometric structure features with more0.9correlation coefficient were extracted based on ratio method, including global feature, three radial cut region features, five circumferential cut region features, two radial cut line features, three circumferential cut line features, and box-counting dimension, Single factor anova analysis showed that the differences of the average of most geometric structure features of ripe and unripe cottons were significant at the0.05level.
Ripeness and under-ripeness of raw cotton were regarded as the issue of two-class, and the feature subset was selected from the above feature set based on heuristic searching and filter pattern with an assessing function of class separability criterion. To select the feature subset based on optimal scalar feature, class separability measure of every scalar feature was calculated and sorted by descending on training set, and the frontal l scale features were assembled l feature subset to establish a Bayes-criterion based discrimination model (l=1,2,...,64or15) on training set. With increasing feature subset capacity of l, the error rate on training set kept decreasing while the error rate on validation set changed from decreasing to increasing, and the inflexion of which was used for the best subset capacity in practice. Experiment results showed that spectrum feature subset was selected at the best feature subset capacity of14, based on which the classification rate on prediction set was77.78%; geometric structure feature subset was selected at the best feature subset capacity of8, based on which the classification rate on prediction set was85.56%. To select the feature set based on floating searching,l feature subset (l=1,2,...,64or15) with maximal class separability measure was searched backward and forward to establish a Bayes-criterion based discrimination model on training set. With increasing feature subset capacity of l, the error rate on training set kept decreasing while the error rate on validation set changed from decreasing to increasing, and the inflexion of which was used for the best subset capacity in practice. Experiment results showed that the spectrum feature subset was selected at the best subset capacity of22, based on which the classification rate on prediction set was82.22%; the geometric structure feature subset was selected at the best subset capacity of8, based on which the classification rate on prediction set was85.56%.
Ripeness and under-ripeness of raw cotton were considered as the issue of two-class, and the feature subset was selected from the above feature set based on exhaustive searching and wrapper pattern with an assessing function of error rate. All of l feature subset were used to establish Bayes-criterion based discrimination models on training set and l feature subset (l=1,2,...,64or15) with the minimum error rate on validation set was selected. With increasing feature subset capacity of l, the error rate on training set kept decreasing while the minimum error rate on validation set changed from decreasing to increasing, and the inflexion of which was used for the best subset capacity in practice. Experiment results showed that the spectrum feature subset was selected at the best subset capacity of15, based on which the classification rate on prediction set was83.33%; the geometric structure feature subset was selected at the best subset capacity of6, based on which the classification rate on prediction set was88.89%.
3. The color space measuring cotton color was selected based on correlation analysis. Cotton color was measured in Hunter, Lab, HSI color space according to Chinese government grading standard of raw cotton; and correlation analysis showed that some correlation coefficients of cotton color are false in three color space. With adjusting the component of color space in gray level, all of correlation coefficient are true in HSI color space, which can be used to measure cotton color.
The texture and shape feature set used to discriminate quality grade of raw cotton was extracted in spatial domain.12texture features, including mean, standard deviation, smoothness, the3rd-order moments, consistency, and entropy, were extracted based on the histogram of S and I in HSI color space to describe cotton color and impurity content, including rain rust, hoarfrost, stiff petal, stain, and rot boll.16shape features, including the number of cotton petals, global feature, three radial cut region features, five circumferential cut region features, two radial cut line features, three circumferential cut line features, and box-counting dimension, were extracted based on ratio method to describe cotton size and geometric structure distributing. A valid feature subset without more0.9correlation coefficient was selected based on the correlation of the average texture/shape feature and quality grade from1to7at the0.05level, including the3rd-order moment of I, mean of S, standard deviation of S, entropy of S, the number of cotton petals, global feature, the1st and4th circumferential cut region feature, the1st radial cut line feature, the3rd circumferential cut line feature, and box-counting dimension.
Quality grade of raw cotton was regarded as the issue of seven-class, and the feature subset was selected from the above valid feature set based on10-fold cross-validation, heuristic searching and filter pattern with an assessing function of class separability criterion. To select the feature subset based on optimal scalar feature, class separability measure of every scalar feature was calculated and sorted by descending on every training set, and the frontal l scale features were assembled l feature subset to establish a Bayes-criterion based discrimination model (1=1,2,...,64or15) on every training set. With increasing feature subset capacity of l, the average error rate on10training sets kept decreasing while the average error rate on the10corresponding validation sets changed from decreasing to increasing, and the inflexion of which was used for the best subset capacity in practice. Experiment results showed that the10valid feature subsets were selected at the best subset capacity of7on10training sets respectively, and the fifth feature subset was selected with the minimum error rate on the corresponding validation set, based on which the classification rate on prediction set was74.71%. To select the feature set based on floating searching,l feature subset (l=1,2,...,11) with maximal class separability measure was searched backward and forward to establish a Bayes-criterion based discrimination model on every training set. With increasing feature subset capacity of l, the average error rate on10training sets kept decreasing while the average error rate on the10corresponding validation sets changed from decreasing to increasing, and the inflexion of which was used for the best subset capacity in practice. Experiment results showed that the10valid feature subsets were selected at the best subset capacity of6on10training sets respectively, and the ninth feature subset was selected with the minimum error rate on the corresponding validation set, based on which the classification rate on prediction set was70.11%.
Quality grade of raw cotton was considered as the issue of seven-class, and the feature subset was selected from the above valid feature set based on10-fold cross-validation, exhaustive searching and wrapper pattern with an assessing function of error rate. All of l feature subset were used to establish Bayes-criterion based discrimination models on every training set and l feature subset (l=1,2,...,11) with the minimum error rate on the corresponding validation set was selected. With increasing feature subset capacity of l, the average error rate on10training sets kept decreasing while the average minimum error rate on the10corresponding validation sets changed from decreasing to increasing, and the inflexion of which was used for the best subset capacity in practice. Experiment results showed that the10valid feature subsets were selected at the best subset capacity of9on10training sets respectively, and the sixth feature subset was selected with the minimum error rate on the corresponding validation set, based on which the classification rate on prediction set was79.31%.
4. The feature subset based on exhaustive searching and wrapper pattern is optimal, which can availably eliminate redundant features and identify key features with large computing cost and low speed. The feature subset based on heuristic searching and filter pattern is suboptimal, which may select redundant features with little computing cost and high speed. And an optimistic feature subset may be selected, the error rate of which on validation set was less than that on training set, possibly, prediction set less than validation set.10-fold cross-validation based discrimination model is more steady and reliable.
In order to discriminate ripeness of raw cotton, the geometric structure feature subset was selected with a generalization precision of88.89%based on wrapper pattern and exhaustive searching finally, including the1st and2nd radial cut region feature, circumferential cut region feature (1st,3rd and4th), the2nd radial cut line feature, which revealed that the inner of ripe cotton is dissilient while outer is plump, and the inner of unripe cotton is close while outer is lank.
In order to discriminate quality grade of raw cotton, the texture and shape feature subset was selected with a generalization precision of79.31%based on10-fold cross-validation, wrapper pattern and exhaustive searching finally, including the3rd-order moment of I, mean of S, standard deviation of S, and entropy of S, the number of cotton petals, global feature, the1st circumferential cut region feature, the1st radial cut line feature, and box-counting dimension. The feature subset revealed that high grade cotton petal is whiter, lower yellow, less impurity, more compact, thicker, and fluffier, and that the low grade cotton petal is more light-grey, higher yellow, more impurity, sparser, thinner, stiffer, much stain, and much rot boll. Raw cotton was usually misclassified to neighboring grade, and the higher and lower grade raw cotton were identified exactly.
引文
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