The development of the agricultural industry is impossible without automation of the processes of field preparation and harvesting. One of the ways to solve this problem is the implementation of machine vision technologies supplemented with neural networks in the implementation of automated control systems for agricultural equipment. The implementation of machine vision algorithms will allow the recognition of objects in the workspace, the adjustment of the route of movement of technology, the realization of its various operating scenarios. Neural networks will allow you to analyze the surrounding objects and choose the best route to move. In this article, we consider an algorithm for determining objects based on machine vision technologies and the selection of a working area on a frame. The analysis of the intersection of the working area with recognized objects allows you to create controls that regulate the trajectory of traffic. The obtained results are experimentally verified on a laboratory prototype of a universal platform for agricultural machinery. Various approaches to the selection of object boundaries are considered and tested.
Realization of road construction works at a high-quality and modern level is associated with significant requirements for accuracy and speed. In the manual mode, it is no longer possible to carry out the leveling process in accordance with these requirements. Therefore, a topical task is to automate the process of leveling the roadway using modern information technologies. Automated control systems for road construction equipment can improve the quality of performed work on terrain profiling and increase the life of the roadway. The article presents a structural model of the automated leveling system for the grader, reflecting the processes of interaction between subsystems and the relationship between them. The obtained results can be used to develop hardware and software systems for the control of road-building equipment.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.