Postharvest noninvasive assessment of fresh chestnut (Castanea spp.) internal decay using computer tomography images

X-ray computed tomography (CT) is an effective noninvasive tool to visualize fresh agricultural commodities’ internal components and quality attributes, including those of chestnuts (Castanea spp). There is no procedure to automatically, effectively and efficiently classify fresh commodities from a continuous inline flow through a CT system. If the information obtained by CT scanning of fresh agricultural commodities is to be used in an industrial application (e.g. inline sorting), automated interpretation of CT images is essential. For this purpose, an image analysis method (algorithm) for the automatic classification of CT images obtained from 2848 fresh chestnuts (cv. ‘Colossal’ and ‘Chinese seedlings’), during the harvesting years from 2009 to 2012, was developed and tested. Classification accuracy was evaluated by comparing the classes obtained from six CT images per chestnut to their internal quality assessment. An experienced human rater performed internal quality assessment by visually and invasively rating fresh chestnut internal decay severity (quality) into 5-, 3- and 2-classes.After CT image preprocessing, cropping and segmentation, 1194 grayscale intensity and textural features were extracted from six resultant CT images per sample. Relevant features were selected using a sequential forward selection algorithm with the Fisher discriminant objective function. 86, 155 and 126 features were effective in designing a quadratic discriminant classifier with a 4-fold cross-validation with a performance accuracy of 85.9%, 91.2% and 96.1% for 5, 3 and 2 classes, respectively. This method is accurate and objective in determining fresh chestnut internal quality, and the methodology is applicable to automatic noninvasive inline CT sorting system development.     

Design of nondestructive test system for plasma clots

One of the most important measures that are used to guarantee blood transfusion safety is to detect clots in the plasma before transfusion. To overcome the disadvantages of manual detection method, this research designs a nondestructive testing (NDT) system for plasma clots inspection based on machine vision technique and artificial neural networks. The key technology for system design are studied and presented. Image acquisition is performed by custom-designed software based on MATLAB platform, and the methods of image cut, reverse color, median filter as well as gray cutting are adopted to preprocess image. The use of fisher discrimination method, combined with iterative threshold segmentation method and the selection of connected domain, can successfully eliminate the interference of air bubble and correctly extract the image of plasma clots. Plasma clots are discriminated by a recognition model based on artificial neural network BP algorithms. The results of clinical contrast experiment shows that the system can effectively detect whether plasma contains plasma clots and the new system shows a much higher degree of repeatability and stability. From the image acquisition and processing to the recognition of plasma clots, the detecting time of a sample is no more than 1 min.     

对公路景观与几何设计的几点意见

本文从公路设计的几个方面 ,阐述了景观与几何设计在公路设计中的重要性 ,对高等级公路尤为重要     

Design of Vision System Based on Service Robot

A vision system based on the service robot is involved. In the system, picture signal is acquired by the picture sensor OV7635. Frame memory AL422B is used as data buffer memory, while CPLD controlled the time order DSP performed. In the software system of image processing, to accomplish color image segmentation and recognition, the threshold vector judgment and improved seed-fill algorithm is introduced, and the image geometric moment is calculated during the segmenting. In order to achieve a vision servo system which composed of image-based feedback and adaptive compensation, the deduced matrix-based Jacobian from the image moment is taken as image feature. Adopted the TFT LCD is adopted to straightly disolav the result of vision recognition and vision tracing.     

基于单目视觉的田间菠萝果实识别

菠萝果实的准确识别是菠萝采摘机器人视觉系统的关键。针对田间复杂环境中的青色菠萝,采用图像处理技术、数学形态学方法,识别菠萝果眼,并获取果眼的中心点信息;引入层次聚类分类方法,对多个中心点进行聚类分析;求取最多点集的中心坐标,作为菠萝区域的近似形心。选用在广东湛江菠萝田间拍摄的35幅图像作为评价样本,经过运算,迎光条件下形心识别正确率达到85%。本研究为菠萝采摘机器人的田间果实识别提供了有效的技术方案。     

机器视觉技术在温室营养液调控系统中的应用

现代设施农业趋向于采用无土栽培.无土栽培是一种采用灌溉营养液来栽培作物的方法.为此,介绍了国内温室无土栽培中营养液供给系统的研究现状,针对目前营养液供给控制存在的问题,将机器视觉技术应用到温室营养液调控中,利用作物的图像信息和环境信息,参考作物专家系统,提出控制策略,调整营养液配置参数,并阐述了系统的软硬件实现方法.     

基于视差图像的重叠果实图像分割算法

为解决自动采摘视觉系统中重叠果实的分割问题,提出了基于视差图像的果实分割算法。采用双目立体视觉系统获取图像对,对图像对进行预处理和校正,通过图像对的立体匹配来获取视差图像,最后对视差图像进行分割。该算法将分割的依据和信息从二维图像的颜色、形状、纹理等扩展到三维空间的深度,对空间距离不同的目标具有较好的分割效果。实验表明,对获取的视差图像进行基于区域的分割时,其区域间灰度对比度为0.98,目标计数一致性达到0.90;进行基于边缘的分割时,其边缘检测误差为5.74%,因此,该方法对重叠果实区域的分割是有效的。     

基于机器视觉的五坐标机床旋转轴误差检测方法

提出了一种利用机器视觉技术对五坐标机床旋转轴转角定位误差进行检测的方法。首先,通过制作特定的标志,用CCD相机获取标志图像;然后,通过数字图像处理技术对所获得的图像进行分析处理;最后,根据标志在不同位置处的相对转角偏差计算机床旋转轴的转角定位误差,实现五坐标机床旋转轴转角定位误差的辨识和测量。同时,将该方法与传统检测方法进行对比,实验结果表明,所提出的检测方法简单、高效,可以实现机床旋转轴误差的快速检测,并为五坐标机床旋转轴误差的补偿提供了计算依据。     

智能苗间锄草平台设计

机械除草一直被视为有机农业生产中重要的组成部分,尤其是近年来,人们对除草剂使环境恶化的担忧和对有机食品需求的增加,机械式除草装置备受关注。深松双翼锄草铲只适用于行间除草,苗间杂草为人工铲锄。由于机械式苗间锄草在行进方向有作物植株阻隔,易损伤秧苗,所以机械式苗间锄草相对行间锄草困难。为此,对智能苗间锄草平台进行了设计,对其原理进行了阐述。智能苗间锄草平台可以解决机械式苗间锄草的难题,自行计算植株位置,控制锄草铲躲避苗株并铲除杂草。