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1.
An accurate vegetation index is required to identify plant biomass versus soil and residue backgrounds for automated remote sensing and machine vision applications, plant ecological assessments, precision crop management, and weed control. An improved vegetation index, Excess Green minus Excess Red (ExG − ExR) was compared to the commonly used Excess Green (ExG), and the normalized difference (NDI) indices. The latter two indices used an Otsu threshold value to convert the index near-binary to a full-binary image. The indices were tested with digital color image sets of single plants grown and taken in a greenhouse and field images of young soybean plants. Vegetative index accuracies using a separation quality factor algorithm were compared to hand-extracted plant regions of interest. A quality factor of one represented a near perfect binary match of the computer extracted plant target compared to the hand-extracted plant region. The ExG − ExR index had the highest quality factor of 0.88 ± 0.12 for all three weeks and soil-residue backgrounds for the greenhouse set. The ExG + Otsu and NDI − Otsu indices had similar but lower quality factors of 0.53 ± 0.39 and 0.54 ± 0.33 for the same sets, respectively. Field images of young soybeans against bare soil gave quality factors for both ExG − ExR and ExG + Otsu around 0.88 ± 0.07. The quality factor of NDI + Otsu using the same field images was 0.25 ± 0.08. The ExG − ExR index has a fixed, built-in zero threshold, so it does not need Otsu or any user selected threshold value. The ExG − ExR index worked especially well for fresh wheat straw backgrounds, where it was generally 55% more accurate than the ExG + Otsu and NDI + Otsu indices. Once a binary plant region of interest is identified with a vegetation index, other advanced image processing operations may be applied, such as identification of plant species for strategic weed control.  相似文献
2.
机器视觉技术在金莲花灌溉中的应用研究   总被引:7,自引:0,他引:7  
应用机器视觉技术研究了判断作物缺水状态的方法。在日光条件下采集了金莲花叶片图像,然后分别提取了红绿蓝(RGB)三色分量和它们的相对系数rgb及色度H。在RGB和HSI颜色模型下分析了各分量与作物缺水时间之间的相关特性.分析结果表明红色分量R、绿色分量G、蓝色分量B、以及r分量、b分量都与缺水时间之间有相当高的相关性,可以用作利用机器视觉快速判断金莲花缺水状况的指标,而其他分量与缺水时间之间没有明显的相关性。  相似文献
3.
Plant species identification using Elliptic Fourier leaf shape analysis   总被引:6,自引:0,他引:6  
Elliptic Fourier (EF) and discriminant analyses were used to identify young soybean (Glycine max (L.) merrill), sunflower (Helianthus pumilus), redroot pigweed (Amaranthus retroflexus) and velvetleaf (Abutilon theophrasti Medicus) plants, based on leaf shape. Chain encoded, Elliptic Fourier harmonic functions were generated based on leaf boundary. A complexity index of the leaf shape was computed using the variation between consecutive EF functions. Principle component analysis was used to select the Fourier coefficients with the best discriminatory power. Canonical discriminant analysis was used to develop species identification models based on leaf shapes extracted from plant color images during the second and third weeks after germination. The classification results showed that plant species during the third week were successfully identified with an average of correct classification rate of 89.4%. The discriminant model correctly classified on average: 77.9% of redroot pigweed, 93.8% of sunflower, 89.4% of velvetleaf and 96.5% of soybean. Using all of the leaves extracted from the second and the third weeks, the overall classification accuracy was 89.2%. The discriminant model correctly classified 76.4% of redroot pigweed, 93.6% of sunflower, 81.6% of velvetleaf, 91.5% of soybean leaf extracted from trifoliolate and 90.9% of soybean unifoliolate leaves. The Elliptic Fourier shape feature analysis could be an important and accurate tool for weed species identification and mapping.  相似文献
4.
基于机器视觉群体鸡蛋尺寸的检测方法   总被引:6,自引:1,他引:5  
利用机器视觉技术对鸡蛋外形尺寸进行检测,采用区域标记法分割出单个图像区域,然后确定图像区域形心点,求出其长轴、短轴和面积。研究结果表明,软件预测的鸡蛋平面投影面积(像素)长轴和短轴与实测的蛋重、长轴和短轴的相关系数分别为0.92、0.91和0.84。  相似文献
5.
Automatic segmentation of relevant textures in agricultural images   总被引:5,自引:0,他引:5  
One important issue emerging strongly in agriculture is related with the automatization of tasks, where the optical sensors play an important role. They provide images that must be conveniently processed. The most relevant image processing procedures require the identification of green plants, in our experiments they come from barley and corn crops including weeds, so that some types of action can be carried out, including site-specific treatments with chemical products or mechanical manipulations. Also the identification of textures belonging to the soil could be useful to know some variables, such as humidity, smoothness or any others. Finally, from the point of view of the autonomous robot navigation, where the robot is equipped with the imaging system, some times it is convenient to know not only the soil information and the plants growing in the soil but also additional information supplied by global references based on specific areas. This implies that the images to be processed contain textures of three main types to be identified: green plants, soil and sky if any. This paper proposes a new automatic approach for segmenting these main textures and also to refine the identification of sub-textures inside the main ones. Concerning the green identification, we propose a new approach that exploits the performance of existing strategies by combining them. The combination takes into account the relevance of the information provided by each strategy based on the intensity variability. This makes an important contribution. The combination of thresholding approaches, for segmenting the soil and the sky, makes the second contribution; finally the adjusting of the supervised fuzzy clustering approach for identifying sub-textures automatically, makes the third finding. The performance of the method allows to verify its viability for automatic tasks in agriculture based on image processing.  相似文献
6.
【目的】客观评价田间籽棉质量。【方法】依据中国籽棉品级分级标准,基于机器视觉技术选取棉花尺寸、色泽特征建立田间籽棉品级抽样分级模型。【结果】相关分析表明:亮度修正后,图像特征与籽棉品级之间相关显著。贝叶斯判别分析结果表明:基于10折交叉验证建立的籽棉品级判别模型的识别率在75.00%~92.86%之间,模型的平均识别率达83.20%。基于“1个标准误差”规则选取较好的贝叶斯判别模型,它在独立数据集上的泛化精度达89.11%,其中,前3级籽棉的识别率均达到100%。【结论】基于机器视觉技术识别籽棉品级是可行的,有利于提高籽棉品级抽样分级模型精度。  相似文献
7.
基于机器视觉的大豆细菌斑点病粒检测   总被引:3,自引:1,他引:2  
文章基于机器视觉,通过图像获取系统得到大豆的表面颜色特征,应用SAS对大豆表面颜色特征进行LOGISTIC回归后,应用BP神经网络对大豆进行标准粒与细菌斑点病粒的分类。经过网络训练后,选用收敛效果好的网络对数据进行仿真预测,共计160粒,其中标准大豆80粒,细菌斑点病80粒。得到的测试识别率为:标准大豆96.3%、大豆菌斑粒98.8%。本研究为大豆菌斑粒的在线识别提供了一定的依据,有利于实现大豆的在线缺陷粒检测。  相似文献
8.
基于机器视觉的作物对行喷药控制的研究   总被引:2,自引:2,他引:3  
设计了一套基于机器视觉的作物对行喷药控制系统.首先由CCD摄像机拍摄成行作物图像,提取其色调值图像并用最大类间方差法二值化后,经数学形态学腐蚀后用Hough变换方法拟合作物行中心线,最后步进电机移动其喷头标识来对准此作物行.实验验证了该方法的正确性.  相似文献
9.
基于机器视觉的玉米叶片透射图像特征识别研究   总被引:2,自引:0,他引:2       下载免费PDF全文
【目的】建立玉米品种的叶片透射图像特征数据库,研究特征随品种的变化规律,分析各类特征的识别效果,为进一步研究玉米生长期间的机器视觉品种识别提供依据。【方法】以生产中推广的21个常规玉米品种为供试材料,分别采集拔节期、小喇叭口期、大喇叭口期、抽雄开花期4个生育时期的玉米叶片。在灯箱内,采集每一叶片的高画质透射图像,共计420张。基于Matlab R2009a开发了“玉米叶片特征提取与识别软件”,包括图像预处理、特征提取、神经网络识别和阈值选取4个功能模块。依据开发的特征识别平台,对玉米叶片透射图像进行图像预处理和特征提取。提取形态类、颜色类和纹理类共计48个特征,特征数据量共计20 160条。分析48个特征品种间的变异系数,研究玉米叶片透射图像特征随品种的变化规律。建立BP神经网络模型进行综合识别,分析不同时期单特征的识别效果,寻找玉米叶片透射图像中品种区分能力较强的重要特征。进一步分析不同时期3大类特征及其组合的识别效果。【结果】在玉米的4个生育时期,叶片透射图像3类特征品种间的变异系数差异比较明显,颜色类特征变异系数最大,其次是纹理类特征变异系数,形态类特征变异系数最小,并且这种差异随着玉米的生长十分稳定。在玉米的4个生育时期,叶片透射图像48个特征的品种识别率差异比较明显,为9.52%—29.33%。R分量的标准差、短轴长、H分量的标准差、等面圆直径、H分量的平均值、V分量的标准差、B分量的标准差、不变矩6、椭圆度、S分量的平均值、外接凸多边形面积、B分量的平均值、平滑度、S分量的峰度、S分量的标准差的识别率较高,平均识别率在18%以上。单类特征中,颜色类特征识别率最高,平均86.76%;纹理类特征次之,平均为78.05%;形态类特征最低,平均为68.67%。颜色类特征和纹理类特征识别的稳定性较高,纹理类特征识别效果更稳定一些。组合特征中,形态+颜色特征组合识别率最高,平均识别率为92.29%;颜色+纹理类特征组合次之,平均为90.29%;形态+纹理类特征组合识别率最低,但平均识别率也达到了87.43%。在拔节期,3类特征组合的识别率无明显差异,且都在91%以上。在小喇叭口期,颜色+纹理特征组合识别率大幅上升,形态+颜色特征组合识别率小幅下降,形态+纹理特征组合识别率下降较大,但是仍然维持在82%以上。在其他3个时期,形态+纹理特征组合和颜色+纹理特征组合识别率差别不大,并且形态+颜色特征组合识别率相对较高。【结论】研究结果为玉米叶片透射图像特征的研究与应用提供了比较系统全面的数据,为生长期间玉米品种的识别提供了新的方法和量化依据,也必将在其他作物的识别方面发挥很好的借鉴作用。  相似文献
10.
机器视觉识别田间成熟番茄的研究   总被引:1,自引:1,他引:1  
为正确识别自然条件下田间成熟番茄,顺利完成其自动采摘,研究了基于颜色特征的田间番茄识别方法。对采集的100幅自然条件下田间番茄图片进行颜色特征提取和理解的基础上,建立了利用成熟番茄与背景(未成熟番茄、枝叶等)在I2颜色指标上的差异进行番茄识别的颜色模型,并利用Ostu法将成熟番茄从背景中分割出来。通过在顺光条件和逆光条件下进行试验,结果表明该模型可以较好地实现自然条件下田间成熟番茄的识别。  相似文献
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