Inventi Journals Home
Inventi Impact
Machine Vision

Home Editorial Board Current Issue Past Issues Statistics
Search Article


Journal Scope
Inventi Rapid/Impact: Machine Vision is the peer reviewed journal of Engineering & Technology. The journal contains the research and review paper related to key technologies in the area manufacturing and quality control. It primarily focuses implementation and use of vision algorithm in practical application is provided and engineering aspects of techniques.



A REAL-TIME APPLE GRADING SYSTEM USING MULTICOLOR SPACE
Hayrettin Toylan, Hilmi Kuscu

This study was focused on the multicolor space which provides a better specification of the color and size of the apple in an image. In the study, a real-time machine vision system classifying apples into four categories with respect to color and size was designed. In the analysis, different color spaces were used. As a result, 97% identification success for the red fields of the apple was obtained depending on the values of the parameter “a” of CIE L*a*b*color space. Similarly, 94% identification success for the yellow fields was obtained depending on the values of the parameter Y of CIE XYZ color space.With the designed system, three kinds of apples (Golden, Starking, and Jonagold) were investigated by classifying them into four groups with respect to two parameters, color and size. Finally, 99% success rate was achieved in the analyses conducted for 595 apples....
More
NEW TRENDS IN ROBOTICS FOR AGRICULTURE: INTEGRATION AND ASSESSMENT OF A REAL FLEET OF ROBOTS
Luis Emmi, Mariano Gonzalez-de-Soto, Gonzalo Pajares, Pablo Gonzalez-de-Santos

Computer-based sensors and actuators such as global positioning systems, machine vision, and laser-based sensors have progressively been incorporated into mobile robots with the aim of configuring autonomous systems capable of shifting operator activities in agricultural tasks. However, the incorporation of many electronic systems into a robot impairs its reliability and increases its cost. Hardware minimization, as well as software minimization and ease of integration, is essential to obtain feasible robotic systems. A step forward in the application of automatic equipment in agriculture is the use of fleets of robots, in which a number of specialized robots collaborate to accomplish one or several agricultural tasks. This paper strives to develop a system architecture for both individual robots and robots working in fleets to improve reliability, decrease complexity and costs, and permit the integration of software from different developers. Several solutions are studied, from a fully distributed to a whole integrated architecture in which a central computer runs all processes. This work also studies diverse topologies for controlling fleets of robots and advances other prospective topologies. The architecture presented in this paper is being successfully applied in the RHEA fleet, which comprises three ground mobile units based on a commercial tractor chassis....
More
RAPID DETECTION METHOD OF MOLDY MAIZE KERNELS BASED ON COLOR FEATURE
Xuan Chu, Yong Tao, Wei Wang, Ying Yuan, Mingjie Xi

In order to find the moldy maize kernels quickly, a method based on machine vision was proposed in this paper. Firstly, images of maize kernels were taken by the moldy maize sorting equipment, and three parts of every kernel, that is, moldy plaques, healthy endospermand healthy embryo, were selected fromthese images. Then a threshold was set in R channel by analyzing color features of those three parts in RGB model. In this method, moldy plaques can be identified roughly. After that the location of the moldy plaques on the kernels was studied, a circle, whose centre was approximately the centroid of amaize kernel and diameter was about the width of embryos, was set to exclude the interference caused by shadow. This method, with the accuracy of 92.1%, laid a good foundation for the further study of moldy maize sorting equipment....
More
Patent Watch
Job Watch

E- ISSN: 2277-6249
P- ISSN: Awaited


Inventi Impact
Machine Vision



Frequency: Quarterly
E- ISSN: 2277-6249
P- ISSN: Awaited


Abstracted/ Indexed in: Ulrich’s International Periodical Directory & Google Scholar, SCIRUS, getCITED