Low and high-level visual feature-based apple detection from multi-modal images

Automated harvesting requires accurate detection and recognition of the fruit within a tree canopy in real-time in uncontrolled environments. However, occlusion, variable illumination, variable appearance and texture make this task a complex challenge. Our research discusses the development of a machine vision system, capable of recognizing occluded green apples within a tree canopy. This involves the detection of “green” apples within scenes of “green leaves”, shadow patterns, branches and other objects found in natural tree canopies. The system uses both thermal infra-red and color image modalities in order to achieve improved performance. Maximization of mutual information is used to find the optimal registration parameters between images from the two modalities. We use two approaches for apple detection based on low and high-level visual features. High-level features are global attributes captured by image processing operations, while low-level features are strong responses to primitive parts-based filters (such as Haar wavelets). These features are then applied separately to color and thermal infra-red images to detect apples from the background. These two approaches are compared and it is shown that the low-level feature-based approach is superior (74% recognition accuracy) over the high-level visual feature approach (53.16% recognition accuracy). Finally, a voting scheme is used to improve the detection results, which drops the false alarms with little effect on the recognition rate. The resulting classifiers acting independently can partially recognize the on-tree apples, however, when combined the recognition accuracy is increased.     

设施蔬菜自动对靶喷药技术研究现状与分析

[目的]回顾与总结国内外设施蔬菜自动对靶喷药技术的研究现状与进展,为该技术在设施蔬菜自动对靶喷药机器人的发展应用上提供理论和科学依据.[方法]采用相关文献资料、实地调研的方法,汇总、整理及分析.[结果]导航技术国外主要采用基于GPS、机器视觉、激光雷达等技术开发的路径识别及智能避障技术,国内主要采用电磁诱导、基于GPS...     

基于Fisher判别分析的玉米叶部病害图像识别

 【目的】利用计算机视觉技术实现玉米叶部病害的自动识别诊断。【方法】在大田开放环境下采集病害图像样本,综合应用基于H阈值分割、迭代二值化、图像形态学运算、轮廓提取等算法处理病害图像,抽取病斑,提取病害图像的纹理、颜色、形状等特征向量,采用遗传算法优化选择出分类特征,并利用费歇尔判别法识别普通锈病、大斑病和褐斑病3种玉米叶部病害。【结果】研究中提取了墒、相关信息测度、分形维数、H值、Cb值、颜色矩、病斑面积、圆度、形状因子等28个特征向量,利用遗传算法优选出H值、颜色矩、病斑面积、形状因子等4个独立、稳定性好、分类能力强的特征向量,应用费歇尔判别分析法识别病害,准确率达到90%以上。【结论】综合运用数字图像处理技术、图像纹理、颜色、形状特征分析方法、遗传算法、费歇尔判别分析方法可以有效识别基于田间条件下采集的病害图像,为田间开放环境下实现大田作物病虫害的快速智能诊断提供借鉴。     

基于机器视觉的苹果品质在线分级检测

目前苹果分级自动化程度较低,为了实现苹果品质自动、快速、准确分级设计了一套苹果智能在线检测分级系统。以寒富苹果为测试对象,采用机器视觉技术对苹果分级进行研究。采用阈值分割的方法分割苹果正面图像,逐像素遍历法提取苹果外部轮廓,通过计算其各点到重心的距离提取苹果大小特征,同时计算苹果横径与纵径比提取果形特征。采用支持向量机方法分割侧面苹果图像,计算苹果红色像素占苹果像素的比例提取颜色特征,利用Fisher统计识别的方法提取苹果缺陷。实现了整个分级系统的硬件搭建以及软件的功能,利用该系统对400个苹果样本进行了分级试验,结果表明该系统分级的苹果总体正确率达到95%。设计的基于机器视觉的苹果智能在线检测分级系统克服了传统分级方法的不足,加快了苹果品质分级自动化速度,对水果品质分级等领域有重要研究意义。     

Classification of foreign fibers in cotton lint using machine vision and multi-class support vector machine

Automatic classification of foreign fibers in cotton lint using machine vision is still a challenge due to various colors and shapes of the foreign fibers. This paper presents a novel classification method based on multi-class support vector machine (MSVM) which aims at accurate and fast classification of the foreign fibers. Firstly, live images were acquired by a machine vision system and then processed using image processing algorithms. Then the color features, shape features and texture features of each foreign fiber object were extracted and feature vectors were composed. Afterwards, three kinds of multi-class support vector machines were constructed, i.e., one-against-all decision-tree based MSVM, one-against-one voting based MSVM and one-against-one directed acyclic graph MSVM separately. At last, with the extracted feature vectors as input, the MSVMs were tested using leave-one-out cross validation. The results indicate that both the one-against-one voting based MSVM and the one-against-one directed acyclic graph MSVM can satisfy the accuracy requirement of the classification of foreign fibers, and the mean accuracy is 93.57% and 92.34% separately. The one-against-all decision-tree based MSVM only obtains mean accuracy of 79.25% which can not meet the accuracy requirement. In classification speed, one-against-one directed acyclic graph MSVM is the fastest and fitter for online classification.     

一种基于多模态图像技术的葡萄水分胁迫识别

提出了一种利用多模态图像技术,以实现被葡萄水分胁迫水平的测定方法,通过检测获取葡萄植株表面图像的反射率和纹理信息与水分胁迫水平之间的关系,从而实现植物缺水报警。试验将盆栽葡萄人为建立不同的水分胁迫水平,利用3CCD照相机(三通道的R,G和IR)、多光谱相机(在900,970 nm的光谱波段覆盖)和一个数字彩色摄像机(RGB)对叶片定期进行监测。试验采用偏最小二乘(PLS)方法预测水含量的纹理特征和光谱特性,在葡萄生长的前期,RGB相机获得的纹理参量对含水量预测结果的rp,RMSEP和偏差值分别为0.77,1.15和-0.14,而利用3CCD相机获取的反射参量对含水量预测结果的rp,RMSEP和偏差值分别为0.77,1.22和-0.26;在葡萄生长后期,RGB相机获取的纹理参量对含水量预测结果的rp,RMSEP和偏差值分别为0.81,1.34和0.26,而利用3CCD相机获取的反射参量对含水量预测结果的rp,RMSEP和偏差值分别为0.74,1.46和0.15。通过监测植株覆盖率与不同水分灌溉植株的生长周期发现,植株覆盖率能对葡萄植株的水分胁迫检测做辅助参考变量。试验结果表明,所设计的多传感器系统可用于支持葡萄水分胁迫检测的决策,有利于葡萄的田间管理。     

Identification of nine Iranian wheat seed varieties by textural analysis with image processing

Applying machine vision techniques to classify wheat seeds based on their varieties is an objective method which can increase the accuracy of this process in real applications. In this study, several textural feature groups of seeds images were examined to evaluate their efficacy in identification of nine common Iranian wheat seed varieties. On the whole, 1080 gray scale images of bulk wheat seeds (120 images of each variety) were acquired at a stable illumination condition (florescent ring light). Totally, 131 textural features were extracted from gray level, GLCM (gray level cooccurrence matrix), GLRM (gray level run-length matrix), LBP (local binary patterns), LSP (local similarity patterns) and LSN (local similarity numbers) matrices. The so-called stepwise discrimination method was employed to select and rank the most significant textural features of each matrix individually as well as features of all matrices simultaneously. LDA (linear discriminate analysis) classifier was employed for classification using top selected features. The average classification accuracy of 98.15% was obtained when top 50 of all selected features were used in the classifier. The results confirmed that LSP, LSN and LBP features had a significant influence on the improvement of classification accuracy compared to previous studies.     

实现识别行间苗草的机器视觉系统设计

在耕作试验室的试验环境之下,开发并测试了能够识别行间苗草作物的机器视觉系统,硬件系统主要由速度可控制的土槽试验车装备、固定在可升降平台上可实时采集图像的imagesoure工业摄像头和作为控制台的工控计算机组成;机器视觉系统根据植物和背景的颜色特征二值化图像与田间作物的位置特征识别苗草。结果表明,采集并处理一副大小为640×480像素的彩色图像的平均时间为291 ms,在行驶速度为1.8 km·h-1条件下系统正确识别率达到了95%。     

基于背景差分法的稻米动态图像检测识别

根据稻米形态特点设计了稻米动态图像采集系统,选用背景差分法对米粒动态图像进行目标分割,实现了运动状态下稻米图像特征提取。对提取的颜色、形态特征进行多结构神经网络训练,实现了透明整米、垩白整米、碎米和黄米四类稻米的识别,识别准确率分别为95．2％ ,89．6％ ,97．3％和90．5％。识别效果较好,为稻米在线图像检测分选奠定基础。     

Automatic grading of Bi-colored apples by multispectral machine vision

In this paper we present a novel application work for grading of apple fruits by machine vision. Following precise segmentation of defects by minimal confusion with stem/calyx areas on multispectral images, statistical, textural and geometric features are extracted from the segmented area. Using these features, statistical and syntactical classifiers are trained for two- and multi-category grading of the fruits. Results showed that feature selection provided improved performance by retaining only the important features, and statistical classifiers outperformed their syntactical counterparts. Compared to the state-of-the-art, our two-category grading solution achieved better recognition rates (93.5% overall accuracy). In this work we further provided a more realistic multi-category grading solution, where different classification architectures are evaluated. Our observations showed that the single-classifier architecture is computationally less demanding, while the cascaded one is more accurate.     

Kinect传感器的植株冠层三维数据测量

植株三维信息重构能为植株生长状态监测和精确喷雾施药提供有效数据。提出一种基于Kinect传感器技术的植株冠层三维数据测量的方法。由Kinect传感器进行植株彩色和深度图像数据的采集,提取和处理所采集的植株冠层目标有效三维信息,完成对植株深度数值和水平投影面积的计算。以规则形状物体与不规则植株为实验对象,对三维数据测量方法进行准确性实验测试,并将实验结果与人工测量结果进行比对。实验结果显示,该方法的深度和面积测量的准确性较高,深度测量误差小于1.0%,面积测量误差小于3.6%。选取温室吊兰作为场地实验对象,采用由测量机构和控制处理机构组成的冠层三维检测系统对吊兰冠层进行三维数据测量,并实时输出深度以及水平投影面积信息,其深度测量的相对误差为1.77%。研究表明,该方法具有较高的可行性,适用于温室植株冠层三维数据测量。     

光期湿度对植物工厂生菜干烧心及其营养品质的影响

【背景】 近年来,植物工厂因具有可在垂直立体空间进行周年计划性、省力化和无农药、洁净安全生产等传统农业无可比拟的优势,在全球得到蓬勃发展。然而,由于植物工厂内叶菜生长速度较快,而新叶部位的蒸腾较弱等原因,较易发生干烧心,大幅降低叶菜外观和内在品质。【目的】 研究光期不同湿度对水培生菜干烧心、生长及其营养品质的影响,优化预防或减缓生菜干烧心发生的植物工厂湿度环境参数,为防控生菜干烧心发生,提高植物工厂生菜品质和运行效益提供技术和理论支撑。【方法】 选用在植物工厂环境下较易发生干烧心的‘Tiberius’生菜作为研究对象,在光环境、温度、二氧化碳等环境条件保持一致的情况下,使光期（16 h）各处理空气相对湿度分别维持在50%（RH50）、70%（RH70）和90%（RH90）,暗期（8 h）各处理空气相对湿度保持一致为70%,湿度控制正负误差在5%以内。调查各处理组生菜干烧心发生情况、叶片光合参数、产量及营养品质的差异。【结果】 与RH70和RH90相比：（1）RH50处理显著提高了生菜新叶中钙离子含量,降低了采收时干烧心发生率,并且随着相对湿度水平降低,干烧心的初次发生时间得到显著推迟;（2）RH50处理未对试验光强下生菜的净光合速率产生影响,但却显著增加了生菜气孔导度、蒸腾速率和胞间CO₂浓度,促进了茎叶的钙离子运输;（3）RH50处理未显著降低生菜的总叶面积及茎叶鲜重,但却明显改善了生菜营养品质,使淀粉、抗坏血酸和可溶性蛋白的含量得到显著提高。【结论】 植物工厂高湿环境是生菜干烧心频发的原因之一,通过合理降低湿度水平能够在不显著影响生菜光合能力及产量的情况下,有效减缓生菜干烧心的发生,提高生菜的外观和营养品质,增加其商品价值,保障植物工厂优质高效生产。     

Vision Guided Precision Cultivation

A color machine vision based automatic guidance system was developed for precision guidance of an agricultural cultivator. The guidance system was designed to operate in weedy row crop fields at the time of first cultivation. The performance of the system varied from an RMS guidance error of 7 mm under low weed loads to 12 mm under high weed loads and was capable of operating at travel speeds up to 16 km/h.     

基于模拟退火算法的粮虫图像特征提取

特征提取是储粮害虫图像识别中的重要环节,是识别系统的难点所在。针对粮虫的二值化图像提取出17个形态学特征;运用模拟退火算法从粮虫的17维形态学特征中提取出面积、周长等10个特征的最优特征子空间;采用支持向量机分类器对粮虫进行分类,识别率达到95.0000%以上,证实了基于模拟退火算法的粮虫特征提取的可行性。     

基于Bayes的竹片颜色检测分级方法

为了采用机器视觉对竹片自动识别与颜色分选,研究了一种基于竹片图像颜色特征与纹路特征和Bayes分类器的颜色分类方法.首先,对灰度图像采用Canny算子进行边缘检测,再利用Hough变换对竹片进行边缘定位,并对倾斜竹片实施旋转校正,以确定待检测竹片在图像中的具体位置.根据竹片的位置提取竹片区域平均颜色特征及纹路特征,将其作为样本的属性特征,采用Bayes训练的颜色等级作为输出,建立特征参数与颜色等级之间的Bayes分类器,上位机获得分级信号后经串口通过下位机实现竹片的自动分级.试验结果表明,该方法对竹片颜色检测准确率达到91.7%,可为竹制品行业的竹片颜色自动在线检测提供理论依据.     

基于机器视觉的温室作物行间杂草的识别

根据温室行间植物的位置特征,应用空域中值滤波对图像进行预处理,然后运用色度法和最大方差自动取阈值法对图像处理,最后应用种子填充法将杂草和作物进行分割。结果表明,机器视觉技术在对温室杂草的识别方面具有一定的优越性。     

基于机器视觉技术的烤烟鲜烟叶成熟度检测

为准确判定烟叶采收成熟度,以不同成熟度中部烟叶为材料,利用机器视觉技术提取不同成熟度烟叶图像的颜色和纹理特征值,采用主成分分析法对3个颜色特征值(色调、饱和度、亮度)和5个纹理特征值(角二阶矩、相关度、熵、对比度、逆差距)进行优化,利用BP神经网络建立烟叶成熟度检测模型。结果表明,采用前4个主成分可综合反映3个颜色特征值和5个纹理特征值的分级信息,实现了参数的优化;在图像信息主成分因子数为4,中间节点数为16时,该识别模型最佳,模型平均识别率为93.67%,表明基于机器视觉技术对烤烟鲜烟叶成熟度的检测是可行的。     

基于支持向量机的路面图像分类方法

针对路面结构特征,提出一种颜色与纹理特征相融合并结合模糊支持向量机的路面分类识别方法。提取路面图像的HSV颜色空间的颜色矩作为颜色特征,采用灰度共生矩阵法提取纹理特征,融合路面图像的颜色特征与纹理特征,采用模糊支持向量机进行支持向量特征训练,通过训练得到能尽可能多的满足每一种图像的样本数据特征的特征向量。通过实验,对比了采用传统的支持向量机与模糊支持向量机对路面分类识别的正确率。实验表明本研究所提出方法的有效性。     

基于主成分分析的观赏生菜品质综合评价

为筛选决定观赏生菜综合品质的重要指标,用以综合评价观赏生菜品种,以7个观赏生菜品种为材料,测定其形态指标、营养品质、观赏品质以及产量等指标,采用主成分分析法进行综合评价。对16个指标进行主成分分析后,一共提取到5个主成分,其累积方差贡献率达到96.45%,反映了观赏生菜综合品质方面的绝大部分信息。根据主成分载荷结果和主成分贡献率可知,对形态指标影响最大的是叶宽、叶长等2个主要指标;对营养品质影响最大的是硝态氮含量、可溶性蛋白含量、含水量、可溶性糖含量和花青素含量等5个主要指标;在评价观赏品质时,主要评定其叶色、叶形、株型等3个指标;在评价产量时,可通过测定鲜重来判断。利用提取出的5个主成分代替原有16个指标对7个观赏生菜品种品质进行综合评分及排序,筛选出最优品种为Regina Delle Ghiacciole,最差品种为Quattro Stagioni。研究结果为提高观赏生菜品种筛选效率及优良品种推广提供了理论依据。     

基于风洞系统的生菜空气动力学研究

针对目前利用计算流体力学软件(Computational fluid dynamics,CFD)进行植物工厂内部气流模拟仅在空载植物工厂中进行,忽略了生菜对气流存在阻碍的问题,采用风洞试验,对生菜冠层空气动力学参数进行研究。利用风洞系统测定了生菜冠层的阻力系数(C_D),并求得在不同叶面积密度(L)的情况下生菜冠层渗透率(K)与动量损失系数(C_f)之间的关系,将生菜栽培板置于风洞试验段中间位置,分别测量风洞试验段竖直方向和水平方向不同测点位置的稳态压力与风速。通过已求得的参数得到CFD建模中建立生菜多孔介质模型需要的粘滞阻力系数与惯性阻力参数。结果表明:1)本试验测得的生菜冠层阻力系数为0.02;2)成熟生菜(L=32.5 m~2/m~3),其渗透率为0.04 m~2,动量损失系数为0.13;3)动量损失系数C_f取值为0.1～1.0,当叶面积密度L为10、20、30 m~2/m~3时,作物冠层渗透率K的取值范围分别为0.25～25.00、0.06～6.25、0.03～2.78 m~2;4)成熟生菜的粘滞阻力系数为25,惯性阻力系数为1.3。