A new vision-based approach to differential spraying in precision agriculture

One of the objectives of precision agriculture is to minimize the volume of herbicides by using site-specific weed management systems. To reach this goal, two major factors need to be considered: (1) the similarity of spectral signatures, shapes, and textures between weeds and crops and (2) irregular distribution of weeds within the crop. This paper outlines an automatic computer vision method for detecting Avena sterilis, a noxious weed growing in cereal crops, and differential spraying to control the weed. The proposed method determines the quantity and distribution of weeds in the crop fields and applies a decision-making strategy for selective spraying, which forms the main focus of the paper. The method consists of two stages: image segmentation and decision-making. The image segmentation process extracts cells from the image as the low-level units. The quantity and distribution of weeds in the cell are mapped as area and structural based attributes, respectively. From these attributes, a multicriteria decision-making approach under a fuzzy context allows us to decide whether any given cell needs to be sprayed. The method was compared with other existing strategies.     

基于自动定向的苹果品质智能分级生产线设计

设计了一种基于自动定向原理的用于苹果品质动态、实时检测的智能化分级生产线,由苹果输送系统、自动定向小车、计算机视觉识别控制系统和分级执行装置组成。其中,苹果输送系统将苹果按分级节拍输送到自动定向小车上,由自动定向小车将苹果果梗花萼轴线定向到垂直于水平面的位置,位于圆周分布的3个摄像头一次性采集苹果表面信息,通过计算机识别控制系统进行智能识别,根据国家标准判断每个苹果的等级,并确定苹果的位置信息,通过计算机识别控制系统发出指令传输给分级控制装置,完成苹果的分级。     

基于机器视觉的君迁子种子尖端特征识别方法研究

针对君迁子种子的外形特点,基于Ramer轮廓拟合算法,结合Halcon算法工具包,提出了一种基于Ramer算法的君迁子种子尖端识别方法。结果表明,该方法可以对种子轮廓进行拟合分割,确定种子的尖端位置。通过对5种不同品种君迁子的250粒种子进行识别,准确率达83.6%。     

机器视觉识别田间成熟番茄的研究

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。     

针叶树种计算机视觉苗木自动分级系统的研究

针对苗木分级领域的实际需要及国际苗木分级技术发展状况,研究,设计了针叶树种计算机视突苗木自动分级系统。该系统应用计算机视觉技术和神经网络技术。通过对苗木图像的处理。在实验室中实现对苗木的自动分级,实验研究表明,计算机视觉自动分级系统在苗木分级领域中具有良好的应用前景。     

基于邻域直方图的玉米田绿色植物图像分割方法

为了准确分割绿色植物与土壤背景,克服田间环境的复杂性,比如植物阴影、残渣的存在和光照强度变化等外界因素带来的影响,采用图像处理和支持向量机方法,以超绿特征(2G-R-B)灰度图像为研究对象,以像素点邻域组成的灰度直方图特征作为输入特征向量,通过试验选出最佳训练参数和最优邻域窗口模型。实验表明,该方法适应不同光照强度,并且可以减小噪声、植物阴影和残渣对图像分割带来的影响,得到完整的分割图像。     

A fast segmentation method for high-resolution color images of foreign fibers in cotton

Foreign fibers in cotton seriously affect the quality of cotton products. The identification of foreign fibers in cotton is a critical step in the automated inspection of foreign fibers in cotton; image segmentation is crucial in this identification process. This paper presents a new approach for segmenting images of foreign fibers in cotton. Firstly, color images were captured, and the edge of color images were detected by an edge detection method based on improved mathematical morphology. The color images were subsequently converted into a gradient map, the law of experience values was analyzed, and the best thresholding value of the gradient map was chosen by selecting the best experience value iteratively. The experiment results indicate that the proposed method successfully segments the high-resolution color images of cotton foreign fibers both directly and precisely. Furthermore, the speed of image processing is much faster than that of conventional methods.     

水稻插秧机自动导航技术研究现状

对插秧机的自动导航技术作了初步介绍,并分析了国内外插秧机自动导航技术的研究现状,介绍了用GPS技术、机器视觉技术和惯性传感器技术对插秧机进行导航的原理、方法及试验获得的精度;对国内插秧机自动导航研究现状也作了分析。最后对插秧机自动导航面临的问题作了总结,并提出了建议。     

红枣自动分级技术的研究现状及展望

调查了国内外在机械式、光电式和基于机器视觉技术的红枣自动分级研究现状,并着重对机械式和基于机器视觉技术自动分级技术报道进行分析。结果表明,机械式在短期未来仍是研究热点之一,光电式未来发展空间不大,而机器视觉作为未来的发展方向,将重点解决在保证生产率和分级精确度的情况下完成红枣全表面在线实时检测和多指标综合自动分级的问题。     

基于改进的几何活动轮廓模型的叶片自动分割

叶片的形态测量在苗木生长自动监测中具有重要意义,在形态测量前首先要将完整的叶片从背景中提取出来.针对彩色苗木叶片图像的特点研究了利用几何活动轮廓模型进行完整叶片的自动分割的方法.首先利用图像的全局信息和C-V模型进行初始分割,当曲线到达目标边界附近时,利用改进的基于图像局部信息的能量模型进行边界的精确定位.该方法将C-...