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%。     

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。     

机器视觉技术在农产品检测中的应用研究

机器视觉技术在农产品检测领域得到了广泛的应用,并随着相关技术的不断成熟和发展,机器视觉在农产品检测领域中的应用必将对传统检测模式产生巨大影响。本文分析了机器视觉技术在农产品检测中的应用现状,提出了应用中出现的问题,并为未来的研究方向进行了初步的探讨。     

我国农业信息化建设的主要目标任务

本文探讨了未来我国农业信息化建设应坚持的基本理念,指出了我国未来农业信息化建设的主要目标任务是：强化农业信息化理论研究;开展农业虚拟网示范研究与农业信息网络的建设;进行农业信息资源系统化建设;加深农业信息资源的开发与利用;加强农业实用软件系统的研究和开发;确立农业信息化指标体系;建立健全农业信息化管理体制。     

Support vector machine approach to real-time inspection of biscuits on moving conveyor belt

An intelligent system for colour inspection of biscuit products is proposed. In this system, the state-of-the-art classification techniques based on Support Vector Machines (SVM) and Wilk's λ analysis were used to classify biscuits into one of four distinct groups: under-baked, moderately baked, over-baked, and substantially over-baked. The accuracy of the system was compared with standard discriminant analysis using both direct and multi-step classifications. It was discovered that the radial basis SVM after Wilk's λ was more precise in classification compared to other classifiers. Real-time implementation was achieved by means of multi-core processor with advanced multiple-buffering and multithreading algorithms. The system resulted in correct classification rate of more than 96% for stationary and moving biscuits at 9 m/min. It was discovered that touching and non-touching biscuits did not significantly interfere with accurate assessment of baking. However, image processing of touching biscuits was considerably slower compared to non-touching biscuits, averaging at 36.3 ms and 9.0 ms, respectively. The decrease in speed was due to the complexity of the watershed-based algorithm used to segment touching biscuits. This image computing platform can potentially support the requirements of the high-volume biscuit production.