基于机器视觉的目标检测在精细农业中的关键技术研究
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摘要
基于机器视觉的目标检测技术将电子技术、传感技术、计算机技术、智能化控制技术等相结合,对于农业机械自动化的实现起着非常重要的作用。在蓝莓,柑橘等农业采摘机器人的实现过程中,果实识别是重点难点问题；在种植柑橘果树的过程中,黄龙病(Huanglongbing or HLB)的危害极大,其检测问题的解决对于果树的健康成长起着非常重要的作用。在总结国内外研究成果的基础上,以蓝莓估产、绿色柑橘估产、以及黄龙病的检测为研究实例,对基于机器视觉的目标检测在精细农业中的关键技术进行研究。主要研究内容和成果包括：
(1)提出了基于RGB图像颜色分析的蓝莓果实检测方法。在自然场景下除去自然场景中复杂背景(天空、土壤、枝叶等)的干扰,对蓝莓果实进行识别,并对同一枝头不同生长阶段的果实进行分类。基于对多种颜色模型颜色分量的分析,选出了对于果实分类最有用的R、B、H三个分量。通过交叉验证,比较分析朴素贝叶斯分类器,K-近邻分类器(KNN)和提出的监督K-means分类器(SK-means)的表现。KNN分类器在训练集数据的分类精度最高。
(2)以绿色柑橘作为研究对象,对绿色果实在自然光下RGB图像中的检测问题进一步研究。以建立柑橘早期估产系统为目标,提出了基于快速归一化互相关系数(fast normalized cross correlation, FNCC)和机器视觉的自然光图像中绿色柑橘果实检测方法研究。该方法首先基于RGB图像的颜色分量研究,尽可能多的去除了背景的影响,之后通过FNCC检测图像中的果实潜在位置。结合果实的颜色,纹理以及形状特征来对绿色柑橘果实进行检测之后,该方法对最终的结果进行融合,并最终确定了果实的数目。在检测的59幅验证集图像中共有154个果实,有130个被正确检测,24个果实被丢失,25个正误识。果实的正确检测率为84.4%。
(3)提出一种基于机载高光谱(HS)图像的柑橘黄龙病新的检测方法,扩展的光谱角度匹配检测方法(Extended spectral angle mapping, ESAM)。该方法结合端元提取方法,红边位置确定等技术对传统光谱角度匹配算法(SAM)进行扩展。该方法首先分析柑橘叶冠的地面测量数据和机载高光谱图像数据健康和HLB感染树冠的特征和区别,然后使用ESAM算法与常用的三种方法SAM,K-均值和马氏距离分类法分别对训练集样本和验证集样本进行HLB检测,并对其结果进行比较。对训练集样本ESAM的正确率为82.6%,验证集的正确率为86.3%,其正确率最高。还探索了应用多光谱图像进行HLB检测的可行性。使用多光谱图像进行HLB检测的结果比使用机载高光谱图像的结果差很多,说明高光谱图像更适合完成这一任务。
(4)为了更加有效的通过HS图像来检测HLB,需要一种有效的降维方法,来改进HLB的检测精确率。尝试了四种不同的降维方法,主成分分析方法(PCA),最大噪声分离变换方法(MNF),前向特征检测方法(FFSA)和基于Kullback-Leibler散度(KLD)的降维方法。通过使用这四种方法选出的有效波段,比较其检测结果。在基于像素的检测结果中,利用PCA和KLD方法的降维结果表现出较高的HLB检测正确率63.3%。在基于植株的分类中,利用MNF和KLD方法的降维结果的HLB检测正确率达93.3%,显示了降维方法的应用潜力。
Target detection based on machine vision has integreted electronic technology, sensor technology, computer technology, intelligent control technology and so on, which plays a very important role for the realiztion of algricultural machinery automation. Fruit detection is a very challenging problem when developing a blueberry or citrus picking robot. Huanglongbing (HLB) is a great threat to citrus trees during their growth. The identification of HLB is very important for the citrus tree grower. Based on previous research results, this thesis conducted study on machine vision based target detection technology applied on yield mapping of blueberry, green citrus, and HLB detection research. In general, the major works and contribution of this thesis are as follows.
(1)An algorithm termed 'color component analysis based detection (CCAD)' method, was developed. This newly developed 'CCAD' method for blueberry was proved to be efficient for separating fruit from background and identifying blueberry fruit of different growth stages using natural outdoor color images. Color components in several important color model were analyzed, and three components including R, B, and H were selected to do fruit identification. Cross validation was conducted using not only the traditional classifiers such as K-nearest neighbor (KNN) and naive Bayesian classification (NBC), but another newly introduced 'supervised K-means clustering classifier (SK-means)'. KNN classifier yielded the highest classification accuracy using the prebuilt pixel dataset.
(2)This thesis further studied the green fruit detection using RGB images. A fast normalised cross correlation (FNCC) based machine vision algorithm was proposed in this study to develop a method for detecting and counting immature green citrus fruit using outdoor colour images toward the development of an early yield mapping system.Firstly, the background was removed as much as possible based on color component based analysis. Then the potential fruit positions were identified using the proposed fast normalised cross correlation (FNCC) based method. Finally, the number of fruit was determined after combining colour, texture, and shape feature analysis. For a validation dataset of59images,130green fruits were identifies,24fruits were misses,25fruits were false positives. The identification accuracy was84.4%.
(3) In this study, a novel method termed 'extended spectral angle mapping (ESAM)' was proposed to detect HLB.This method extended the traditional spectral angle mapping method by combining endmember extraction method, red edge position technique, and several other techniques. Firstly, the spectral differences between healthy and HLB infected canopies from ground measurement and hyperspectral image was analyzed. Then the performance of the proposed 'ESAM' method and two other commonly used methods:K-means, and Mahalanobis distance (MahaDist) was evaluated and compared, and it was shown ESAM performed best. A fairly high detection accuracy of82.6%was achieved in the calibration set, and86.3%in the validation set was achieved using the proposed ESAM method. The study also explored the feasibility of using MS image for HLB identification. However, it didn't achieve as good result as HS image, which indicated that HS image was a better choice for HLB detection.
(4) To solve citrus greening disease (Huanglongbing, or HLB) detection problem using airborne HS image, an efficient dimension reduction method needs to be applied. In this study four dimension reduction methods including principal component analysis (PCA), maximum noise fraction (MNF) transformation, forward feature selection algorithm (FFSA) and Kullback-Leibler divergence (KLD) based method, were applied on the obtained HS image. The selected bands or components were used for the following pixel based or tree based classification. KLD showed the highest pixel beased HLB detection accuracy of63.3%using validation pixel dataset, which indicated there was much room for study and improvement of how to use dimension reduction method efficiently. MNF and KLD gave the same tree based HLB detection accuracy, which was93.3%, which also indicated the potential of the dimension reduction.
(1)提出了基于RGB图像颜色分析的蓝莓果实检测方法。在自然场景下除去自然场景中复杂背景(天空、土壤、枝叶等)的干扰,对蓝莓果实进行识别,并对同一枝头不同生长阶段的果实进行分类。基于对多种颜色模型颜色分量的分析,选出了对于果实分类最有用的R、B、H三个分量。通过交叉验证,比较分析朴素贝叶斯分类器,K-近邻分类器(KNN)和提出的监督K-means分类器(SK-means)的表现。KNN分类器在训练集数据的分类精度最高。
(2)以绿色柑橘作为研究对象,对绿色果实在自然光下RGB图像中的检测问题进一步研究。以建立柑橘早期估产系统为目标,提出了基于快速归一化互相关系数(fast normalized cross correlation, FNCC)和机器视觉的自然光图像中绿色柑橘果实检测方法研究。该方法首先基于RGB图像的颜色分量研究,尽可能多的去除了背景的影响,之后通过FNCC检测图像中的果实潜在位置。结合果实的颜色,纹理以及形状特征来对绿色柑橘果实进行检测之后,该方法对最终的结果进行融合,并最终确定了果实的数目。在检测的59幅验证集图像中共有154个果实,有130个被正确检测,24个果实被丢失,25个正误识。果实的正确检测率为84.4%。
(3)提出一种基于机载高光谱(HS)图像的柑橘黄龙病新的检测方法,扩展的光谱角度匹配检测方法(Extended spectral angle mapping, ESAM)。该方法结合端元提取方法,红边位置确定等技术对传统光谱角度匹配算法(SAM)进行扩展。该方法首先分析柑橘叶冠的地面测量数据和机载高光谱图像数据健康和HLB感染树冠的特征和区别,然后使用ESAM算法与常用的三种方法SAM,K-均值和马氏距离分类法分别对训练集样本和验证集样本进行HLB检测,并对其结果进行比较。对训练集样本ESAM的正确率为82.6%,验证集的正确率为86.3%,其正确率最高。还探索了应用多光谱图像进行HLB检测的可行性。使用多光谱图像进行HLB检测的结果比使用机载高光谱图像的结果差很多,说明高光谱图像更适合完成这一任务。
(4)为了更加有效的通过HS图像来检测HLB,需要一种有效的降维方法,来改进HLB的检测精确率。尝试了四种不同的降维方法,主成分分析方法(PCA),最大噪声分离变换方法(MNF),前向特征检测方法(FFSA)和基于Kullback-Leibler散度(KLD)的降维方法。通过使用这四种方法选出的有效波段,比较其检测结果。在基于像素的检测结果中,利用PCA和KLD方法的降维结果表现出较高的HLB检测正确率63.3%。在基于植株的分类中,利用MNF和KLD方法的降维结果的HLB检测正确率达93.3%,显示了降维方法的应用潜力。
Target detection based on machine vision has integreted electronic technology, sensor technology, computer technology, intelligent control technology and so on, which plays a very important role for the realiztion of algricultural machinery automation. Fruit detection is a very challenging problem when developing a blueberry or citrus picking robot. Huanglongbing (HLB) is a great threat to citrus trees during their growth. The identification of HLB is very important for the citrus tree grower. Based on previous research results, this thesis conducted study on machine vision based target detection technology applied on yield mapping of blueberry, green citrus, and HLB detection research. In general, the major works and contribution of this thesis are as follows.
(1)An algorithm termed 'color component analysis based detection (CCAD)' method, was developed. This newly developed 'CCAD' method for blueberry was proved to be efficient for separating fruit from background and identifying blueberry fruit of different growth stages using natural outdoor color images. Color components in several important color model were analyzed, and three components including R, B, and H were selected to do fruit identification. Cross validation was conducted using not only the traditional classifiers such as K-nearest neighbor (KNN) and naive Bayesian classification (NBC), but another newly introduced 'supervised K-means clustering classifier (SK-means)'. KNN classifier yielded the highest classification accuracy using the prebuilt pixel dataset.
(2)This thesis further studied the green fruit detection using RGB images. A fast normalised cross correlation (FNCC) based machine vision algorithm was proposed in this study to develop a method for detecting and counting immature green citrus fruit using outdoor colour images toward the development of an early yield mapping system.Firstly, the background was removed as much as possible based on color component based analysis. Then the potential fruit positions were identified using the proposed fast normalised cross correlation (FNCC) based method. Finally, the number of fruit was determined after combining colour, texture, and shape feature analysis. For a validation dataset of59images,130green fruits were identifies,24fruits were misses,25fruits were false positives. The identification accuracy was84.4%.
(3) In this study, a novel method termed 'extended spectral angle mapping (ESAM)' was proposed to detect HLB.This method extended the traditional spectral angle mapping method by combining endmember extraction method, red edge position technique, and several other techniques. Firstly, the spectral differences between healthy and HLB infected canopies from ground measurement and hyperspectral image was analyzed. Then the performance of the proposed 'ESAM' method and two other commonly used methods:K-means, and Mahalanobis distance (MahaDist) was evaluated and compared, and it was shown ESAM performed best. A fairly high detection accuracy of82.6%was achieved in the calibration set, and86.3%in the validation set was achieved using the proposed ESAM method. The study also explored the feasibility of using MS image for HLB identification. However, it didn't achieve as good result as HS image, which indicated that HS image was a better choice for HLB detection.
(4) To solve citrus greening disease (Huanglongbing, or HLB) detection problem using airborne HS image, an efficient dimension reduction method needs to be applied. In this study four dimension reduction methods including principal component analysis (PCA), maximum noise fraction (MNF) transformation, forward feature selection algorithm (FFSA) and Kullback-Leibler divergence (KLD) based method, were applied on the obtained HS image. The selected bands or components were used for the following pixel based or tree based classification. KLD showed the highest pixel beased HLB detection accuracy of63.3%using validation pixel dataset, which indicated there was much room for study and improvement of how to use dimension reduction method efficiently. MNF and KLD gave the same tree based HLB detection accuracy, which was93.3%, which also indicated the potential of the dimension reduction.
引文
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