Application of Computer Vision Technology in Agriculture

With the development of image processing technology and computer, computer vision technology has been widely used in the production of agriculture,and has made many important achievements. This paper reviews its-research progress on diagnosis of agricultural products, water diagnosis, weed identification,product quality testing and grading, agricultural picking and sorting and other as- pects, and finally put forward its existing problems and prospects for the future.

Audit Optimization Based on Computer Vision Technology

Research Purposes: The project is based on the traditional accounting audit platform, on the basis of technical research, to re-create a three-party relationship, widely used data platform. Research Methodology: We use the SQL Server database data storage principle, the original documents and other first-hand information direct electronic storage. Research Results: It will not only increase file sharing and privilege management algorithms, but also prevent first-hand data modification, and the use of more intelligent embedded management of various modules to protect the three parties to exercise their legitimate rights and interests on the platform.

Application of Computer Vision Technology in Intelligent Transportation

With the social development and the continuous improvement of scientific and technological level,the people's living standards continue to improve,and the demand for intelligent technology is also increasing.In recent years,with the increase of the number of cars and the frequent occurrence of traffic accidents,the problem of traffic safety has attracted the attention of all sectors of society,and computer vision technology has been gradually appliedto intelligent transportation.This paper analyzes the application of computer vision technology in detail,so as to provide reference for the development of intelligent transportation in our country.

Measuring method for micro-diameter based on structured-light vision technology

Based on structured-light vision measurement technology, we study a measuring method for microdiameter. The measurement principle and mathematical model are described. A novel grayscale barycenter extraction algorithm along the radial direction is proposed, which can precisely gather the image coor- dinates of the ellipse-shaped light-stripe centers. The accuracy of the measurement result shows marked improvement by using the algorithm. The method executes circle fitting to the measured three-dimensional （3D） data using linear least square method, which can acquire the diameter, surface profile, and other information of the object effectively. On the scene, a line-structured light vision system using the presented method is applied to measure the curvature radius of metal blades. Experimental results show that the measurement precision of the system is higher than 2 μm.

Quantifying muskmelon fruit attributes with A-TEP-based model and machine vision measurement

In this study, we established a dynamic morphological model using the accumulated thermal effectiveness and photosynthetic active radiation （PAR） （A-TEP）, aiming to explore the relationship between muskmelon （Cucumis melo L.） fruit attributes and environmental factors. Muskmelon surface color was described by parameters of red, green, blue, hue, saturation and brightness （HSI）. Three characteristic parameters, gray level co-occurrence matrix （GLCM）, angular second moment （ASM）, entropy, contrast, and the coverage rate were used to describe the process of muskmelon fruit netting formation. ASM was not significant difference during muskmelon fruit growth. The number and deep of netting stripes gradually increased with fruit growth. Coverage rate increased rapidly for 15-30 d after pollination. The vertical and horizontal diameters of muskmelon fruit were followed a logistic curve. And root mean squared errors （RMSE） between the simulated and measured vertical and horizontal diameters were 3.527 and 4.696 mm, respectively. RMSE of red, green, blue, saturation and brightness were 0.999, 2.690, 2.992, 0.033 and 5.51, respectively, and the RMSE for entropy, contrast and coverage rates were 0.077, 0.063 and 0.015, respectively, indicating a well consistent between measured and simulated values.