图像压缩技术在秦巴山区油菜作物监控系统中的应用研究

[目的]在秦巴山区油菜作物监控系统中,为使得图像在保证质量的前提下以较小的空间存储和较小的比特率传输,提出了一种新的图像压缩技术方案。[方法]方案首先将彩色图像分解为3基色亮度图,并分别进行子块划分和DCT变换处理,再对变换域系数进行量化处理和采用Huffman算法进行编码压缩,最后采用逆过程进行解压并匹配出解压后的彩色图像。[结果]仿真试验表明:油菜作物的彩色图像在压缩比为11.972 3∶1时,人眼无法分辨出解压缩图像与源图像之间的差异;当压缩比为高压缩比53.565 6∶1时,PSNR仍能达到30 dB以上,且编码效率能达到0.78以上,冗余度在0.22以下。[结论]该研究表明提出的彩色图像压缩技术方案在保证图像质量的前提下能够实现较高的压缩比,且编码质量和解压缩图像均可达到较理想的效果,完全能满足秦巴山区油菜作物监控系统中的图像存储和传输。     

图像压缩技术在秦巴山区油菜作物监控系统中的应用研究(英文)

[目的]在秦巴山区油菜作物监控系统中,为了使得图像在保证质量的前提下以较小的空间存储和较小的比特率传输,提出了一种图像压缩技术方案,[方法]方案首先将彩色图像分解为三基色亮度图,并分别进行子块划分和DCT变换处理,再对变换域系数进行量化处理和采用Huffman算法进行编码压缩,最后采用逆过程进行解压并匹配出解压后的彩色图像。[结果]通过仿真实验结果表明:油菜作物的彩色图像在压缩比为11.9723∶1时,人眼无法分辨出解压缩图像与源图像之间的差异;当压缩比为高压缩比53.5656∶1时,PSNR仍能达到30dB以上,且编码效率能达到0.78以上,冗余度在0.22以下。[结论]表明提出的彩色图像的压缩技术方案在保证图像质量的前提下能够实现较高的压缩比,且编码质量和解压缩图像均达到较理想的效果,完全可以满足秦巴山区油菜作物监控系统中的图像存储和传输。     

An automated stand-alone in-field remote sensing system (SIRSS) for in-season crop monitoring

A stand-alone in field remote sensing system (SIRSS) with high spatial and temporal resolution was developed in this study. System control and image processing algorithms consisted of image acquisition control, camera parameter control, crop canopy reflectance calibration, image rectification, image background segmentation and vegetation indices map generation were developed and embedded in the SIRSS. The SIRSS is able to automatically capture multispectral images over a testing field at any predefined time points during the growing season and process captured images in real-time. This paper presents the SIRSS system design, image analysis procedures and determination of vegetation indices. In a validation experiment over an 8-plot corn field with three different nutrient treatments spanning the 2006 growing season, a total of 91 images were acquired and four different vegetation indices were derived from the images of each day. The largest differences of indices values among three treatments were indentified during the V6-V8 stages which implied this period could be the best time to detect variability caused by the nitrogen stress in the cornfield. The SIRSS has shown the potential of monitoring changes in vegetation status and condition.     

花生图像采集中背景颜色和光强影响的研究

为评价花生图像质量,首先通过阈值分割算法分割花生图像,接着进行开操作,然后根据连通区域个数得出花生粒数作为评价标准。从3种背景颜色和5种光照强度的试验中得出在黑色背景50～90 lx的光强下效果较好。研究将有助于花生自动检测。     

基于SVM的高粱叶片病斑图像自动分割提取方法研究

为实现高粱叶片病斑的自动化无损监测,利用支持向量机(SVM)技术对高粱叶片病斑图像进行自动分割提取研究。结果表明,通过选取RGB、HIS和Lab 3种颜色空间的颜色特征值可以消除对作物病斑拍照时产生的光照、亮度等影响。在MATLAB软件环境下调用LIBSVM软件对病斑图片中的病斑图像像素点和背景图像像素点建立支持向量机分类模型,可以实现对病斑的高效分割和高质量提取。分割提取效果与人眼识别的病斑图像高度吻合。如果利用大量采集的病斑图像进行模型训练,就可以真正实现完全自动化的病斑分割、提取和判别。因此,该研究对建立完全自动化的作物病斑图像识别系统意义重大。     

An optical automatic measurement method for the moisture content of rough rice using image processing techniques

This study proposed an automatic measurement method for the moisture content of rough rice using image processing techniques. Under a fixed hot air temperature and humidity, this measurement method uses drying time as a variable. After the rough rice with stalks is placed in the rough rice test carrier, an image acquisition system is used to set the multiple thresholds for the color histograms of the images. Based on the distribution of the colors, the stalk images are separated from the rough rice images, and edge enhancement and shape detection are applied to more accurately acquire specific detected areas from the image. Finally, the moisture content of rough rice can be determined according to the specific colors of the stalks. This study also explored the impact of the dynamic equilibrium moisture content on the drying of rice, rendering it more consistent with the actual drying behavior. The experimental results were compared with the results of other measurement methods for correction, in order to achieve real-time measurement and analysis of the batch re-circulating rice drying process.     

An adaptive image segmentation algorithm for X-ray quarantine inspection of selected fruits

Although X-ray scanners are commonly used in airports or customs for security inspection, practical application of X-ray imaging in quarantine inspection to prevent propagation of alien insect pests in imported fruits is still unavailable. The first step to identify insect infestation in fruit by X-ray imaging technique is image acquisition. This is followed by the image segmentation procedure, which can locate sites of infestation. Since the grey level of X-ray images depends on the density and thickness of the test samples, the relative contrast of infestation site to the intact region inside a typical fruit varies with its position. To accurately determine whether a fruit has signs of insect infestation, we have developed an adaptive image segmentation algorithm based on the local pixels intensities and unsupervised thresholding algorithm. This paper presents the detailed image processing procedure including the grid formation, local thresholding, threshold value interpolation, background removal, and morphological filtering for the determination of infestation sites of a fruit in X-ray image. The real-time image processing procedure was tested with X-ray images of several types of fruit such as citrus, peach, guava, etc. Additional tests and analyses were also performed using the developed algorithm on the X-ray images obtained with different image acquisition parameters.     

Evaluation of different approaches for estimating and mapping crop water status in cotton with thermal imaging

Canopy temperature has long been recognized as an indicator of plant water status, therefore, a high-resolution thermal imaging system was used to map crop water status. Potential approaches for estimating crop water status from digital infrared images of the canopy were evaluated. The effect of time of day on leaf temperature measurements was studied: midday was found to be the optimal time for thermal image acquisition. Comparison between theoretical and empirical approaches for estimating leaf water potential showed that empirical temperature baselines were better than those obtained from energy balance equations. Finally, the effects of angle of view and spatial resolution of the thermal images were evaluated: water status was mapped by using angular thermal images. In spite of the different viewing angles and spatial resolution, the map provided a good representation of the measured leaf water potential.     

植物叶片智能分析系统的设计

为避免农作物病害智能诊断过程中人为主观因素的影响,客观准确的表达叶片信息,利用数字图像处理技术和农业植保专家知识相结合,设计了适合于大田作物的植物叶片图像处理与分析系统。该系统主要包括植物叶片几何失真校正模块,几何特征计算模块、颜色识别模块以及病害区域识别模块;以校正后非线性失真现象的叶片图像为基础,实现了叶片几何特征值和颜色值的计算,并提取其病斑区域图像。试验结果表明,该方法满足病害智能诊断要求,具有良好的适应性和实用性。     

基于图像处理的植物虫损叶面面积计算算法研究

提供了采用数字图像处理方法快速计算植物虫损叶片面积的方法,对叶片图像进行图像采集、预处理以及几何校正,提取叶片轮廓并填充后,去除叶柄求得虫损叶片面积及虫损率。此方法简单易行,适合多种形状叶片,同时适用于非虫损叶片面积的测量。     

Evaluating high resolution SPOT 5 satellite imagery for crop identification

High resolution satellite imagery offers new opportunities for crop monitoring and assessment. A SPOT 5 image acquired in May 2006 with four spectral bands (green, red, near-infrared, and short-wave infrared) and 10-m pixel size covering intensively cropped areas in south Texas was evaluated for crop identification. Two images with pixel sizes of 20 m and 30 m were also generated from the original image to simulate coarser resolution satellite imagery. Two subset images covering a variety of crops with different growth stages were extracted from the satellite image and five supervised classification techniques, including minimum distance, Mahalanobis distance, maximum likelihood, spectral angle mapper (SAM), and support vector machine (SVM), were applied to the 10-m subset images and the two coarser resolution images to identify crop types. The effects of the short-wave infrared band and pixel size on classification results were also examined. Kappa analysis showed that maximum likelihood and SVM performed better than the other three classifiers, though there were no statistical differences between the two best classifiers. Accuracy assessment showed that the 10-m, four-band images based on maximum likelihood resulted in the best overall accuracy values of 91% and 87% for the two respective sites. The inclusion of the short-wave infrared band statistically significantly increased the overall accuracy from 82% to 91% for site 1 and from 75% to 87% for site 2. The increase in pixel size from 10 m to 20 m or 30 m did not significantly affect the classification accuracy for crop identification. These results indicate that SPOT 5 multispectral imagery in conjunction with maximum likelihood and SVM classification techniques can be used for identifying crop types and estimating crop areas.     

计算机视觉在作物水分亏缺诊断中的应用进展

随着图像处理与分析技术的蓬勃发展,许多专家学者利用图像获取工具比人眼更精细的分辨能力,应用计算机视觉技术进行信息诊断研究。论文简要介绍了计算机视觉技术,着重分析了作物图像获取方法和作物图像信息的分析方法,指出基于计算机视觉的作物水分亏缺诊断在实际应用中存在的问题,并对该领域的未来发展进行展望,指出:多信息融合是图像信息描述的主流趋势,高效的图像识别与分析算法是关键;水分亏缺诊断与作物缺水临界点、作物灌水量研究有机结合是实现作物田间实时灌溉和精量灌溉的前提。     

基于图像处理技术的四种苜蓿叶部病害的识别

基于图像处理技术,对4种苜蓿叶部病害进行识别研究。利用结合K中值聚类算法和线性判别分析的分割方法对病斑图像作分割,获得了较好的分割效果。结果表明:该分割方法在由4种病害图像数据集整合成的汇总图像数据集上综合得分的平均值和中值分别为0.877 1和0.899 7;召回率的平均值和中值分别为0.829 4和0.851 4;准确率的平均值和中值分别为0.924 9和0.942 4。进一步提取病斑图像的颜色特征、形状特征和纹理特征共计129个,利用朴素贝叶斯方法和线性判别分析方法建立病害识别模型,并结合顺序前向选择方法实现特征筛选,分别获得最优特征子集;同时利用这2个最优特征子集,结合支持向量机(Support vector machine,SVM)建立病害识别模型。比较各模型的识别效果,发现利用所建线性判别分析模型下的最优特征子集,结合SVM建立的病害识别模型识别效果最好,训练集识别正确率为96.18%,测试集识别正确率为93.10%。由此可见,本研究所建基于图像处理技术的病害识别模型可用于识别上述4种苜蓿叶部病害,为苜蓿病害的诊断和鉴别提供了一定依据。     

基于物联网技术的作物远程感知系统的设计与实现

为提高作物生产调控管理水平,基于视频监控、物联网传感器和网络通信等技术,初步设计开发了作物远程感知系统。在作物生长发育过程中,该系统实现了作物生长过程中的关键环境因子、作物长势以及视频图像等参数信息的实时采集,从而提高了获取数据的效率和准确性。它具有功耗低、成本低、扩展灵活等优点,初步试验表明了该系统的合理性与实用性。该系统的构建和运行,为作物长势进行实时跟踪监测与综合分析以及管理提供决策支持。     

洋葱无蜡粉突变体特性初步研究

为了探索洋葱蜡粉缺失突变体的特征特性和形态学应用价值，选择20份无蜡粉材料进行田间表型特征观察及叶面蜡质成分分析，并对突变株进行SSR引物筛选。结果表明，洋葱无蜡粉叶片呈亮绿色，有光泽，遗传分析发现叶片蜡粉受隐性基因控制；突变株表现为苗期长势相对弱，产量与品种特性相关；突变株叶片表现出无或少蓟马危害症状，不使用杀虫剂能够达正常洋葱使用杀虫剂抗葱蓟马的效果，并建立无蜡粉洋葱抗蓟马评价标准；叶表超微结构观察及蜡粉成分分析发现：突变体叶表面蜡粉严重缺失，有少量蜡粉，不足为人眼观察到，但在抽薹开花末期，花薹表面有一层淡淡光亮白色蜡粉。气相色谱分析叶表面蜡质主要成分均为酰胺、酚类、酮类、烃类、酯类，但无蜡粉叶片中16-三十一酮含量差异显著，由相对含量52.66%降至 2.79%，导致叶表面无蜡粉现象；对19061单株自交分离的有蜡粉与无蜡粉植株进行SSR引物筛选，引物196和304可作为单株特异标记能够将19061有蜡粉与无蜡粉区分，但不能区分其他无蜡粉与有蜡粉材料。因此，洋葱无蜡粉突变体初步研究为洋葱形态学标记和杂交制种应用、抗葱蓟马研究奠定重要基础。     

基于GA-BP神经网络和特征向量优化组合的黄瓜叶片病斑识别

针对家庭种植水培黄瓜中用户难以准确识别病害的问题,设计了一种基于图像处理的黄瓜叶片病斑识别系统。应用自适应小波对原始图像进行降噪处理,在HSV空间通过阈值分割结合形态学操作获得理想的黄瓜叶片图像,并通过自适应阈值分离病斑,提取病斑形态学、颜色和纹理原始特征参数。利用GA-BP神经网络定义原始特征参数对分类结果的灵敏度,递归剔除灵敏度较低的若干特征,降低特征参数的维数。根据优化后的特征参数组合,利用支持向量机对黄瓜炭疽病和白粉病进行识别。实验结果表明,本方法对黄瓜炭疽病和白粉病的综合分类正确率在96%以上。设计的方法有效提高了黄瓜病害的识别率,并为其他作物病害的智能识别提供了借鉴。     

机器视觉在植物叶片叶面积测算中的应用

利用机器视觉系统代替人工对叶片叶面积进行测算。运用Halcon图形开发工具,以VB 2008为基础开发平台,采用通过阈值分割与区域特征提取的方法来计算叶面积,并将结果显示到屏幕并保存到文件中。将具体图像采集设备与机器视觉系统连接,完成叶片图像采集、图像处理和结果显示的一体自动化。与手工测算的费时费力和使用专业设备的高昂费用相比,此系统能在保证测算结果精确度的基础上,对多种植株叶片进行快速准确、简单易行、经济实用的测算。     

基于颜色指数与阈值法的稻田图像分割

为系统、全面地分析不同颜色指数对南方稻田图像分割的适应性,以分蘖期、拔节期稻田图像为研究对象,选择36种常用的颜色指数,采用Otsu阈值法开展基于颜色指数和阈值的图像分割研究,通过比较各颜色指数的分割结果,明确分蘖期和拔节期图像分割的主要干扰因素,筛选最适宜稻田图像分割的颜色指数。结果表明:水稻倒影、浮萍是分蘖期稻田图像分割的主要干扰因素,叶片镜面反射、浮萍和土壤阴影是拔节期稻田图像分割的主要干扰因素;组合指数COM2、MxEG、CIVE和GMR在分蘖期图像和拔节期图像均具有较好的分割精度。因此,基于颜色指数COM2、MxEG、CIVE、GMR和Otsu阈值的稻田图像分割方法对稻田图像分割的干扰要素具有较强的区分能力,分割精度较高,更适宜于南方稻田图像处理研究。     

Line-scan hyperspectral imaging system for real-time inspection of poultry carcasses with fecal material and ingesta

In poultry processing plants, fecal material and ingesta are the primary source of carcass contamination with microbial pathogens. The current practice of the poultry inspection in the United States is primarily human visual observations. Since the visual inspection is becoming more challenging in poultry processing plants adopting high-speed lines, a rapid sorting system could significantly improve the detection and monitoring of carcasses with surface fecal material and ingesta. As a result, we developed a prototype line-scan hyperspectral imaging system configured as a real-time multispectral imaging subsystem for online detection of surface fecal material and ingesta. Specifically, we integrated a commercially available off-the-shelf hyperspectral image camera into the system with two line lights and a custom software program for real-time multispectral imaging. The bottleneck of the imaging system was the data acquisition. For that reason, a multithreaded software architecture was designed and implemented not only to meet the application requirements such as speed and detection accuracy, but also to be customizable to different imaging applications such as systemic disease detection in the future. The image acquisition and processing speed tests confirmed the system could operate to scan poultry carcasses in commercial poultry processing plants. The fecal detection algorithm was based on the previous research using different hyperspectral imaging systems. A new carcass detection and image formation algorithm was developed to allow existing image processing and detection algorithms reusable without any modifications. Sixteen chicken carcasses and four different types of fecal and ingesta samples were used in a study to test the imaging system at two different speeds (140 birds per minute and 180 birds per minute) in a pilot-scale poultry processing facility. The study found that the system could grab and process three waveband images of carcasses moving up to 180 birds per minute (a line-scan rate 286 Hz) and detect fecal material and ingesta on their surfaces. The detection accuracy of the system varied between 89% and 98% with minimum false positive errors (less than 1%), depending on tested detection algorithms. Therefore, these findings provide the basis of not only a commercially viable imaging platform for fecal detection but also a single poultry inspection system for multiple tasks such as systemic disease detection and quality sorting.     

应用多光谱数字图像识别苗期作物与杂草

通过对多光谱成像仪获得的数字图片,采用一定的目标分割与形态学处理,对豆苗和杂草进行识别判断.为解决识别速度与正确率的矛盾,以豆苗和杂草图像的识别为例,提出一种基于多光谱图像算法的杂草识别新方法.应用3CCD多光谱成像仪获取豆苗与杂草图像,以多光谱图像的近红外IR通道图像为基础,利用图像分割和形态学方法,将所有豆苗叶子影像提取出来.对于剩下的2种杂草(牛筋草,空心莲子草)图像,先利用图像分析工具统计出图像块的长度、宽度、面积等基本特征参数,并根据它们形状的不同,总结出两条简单的判别规则,进行进一步的识别.本试验对147个目标进行判断,其中误判14个,正确率为90.5%,表明该方法算法简单、计算量小、速度快,能够有效识别这2种杂草,为田间杂草的快速识别提供了一种新方法.