English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/142144
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:


Machine-Vision Systems Selection for Agricultural Vehicles: A Guide

AuthorsPajares, Gonzalo; García-Santillán, Iván; Campos, Yerania; Montalvo, Martín; Guerrero, José Miguel; Emmi, Luis Alfredo ; Romeo, Juan; Guijarro, María; González-de-Santos, Pablo
Issue Date22-Nov-2016
PublisherMultidisciplinary Digital Publishing Institute
CitationJournal of Imaging 2(4): 34 (2016)
AbstractMachine vision systems are becoming increasingly common onboard agricultural vehicles (autonomous and non-autonomous) for different tasks. This paper provides guidelines for selecting machine-vision systems for optimum performance, considering the adverse conditions on these outdoor environments with high variability on the illumination, irregular terrain conditions or different plant growth states, among others. In this regard, three main topics have been conveniently addressed for the best selection: (a) spectral bands (visible and infrared); (b) imaging sensors and optical systems (including intrinsic parameters) and (c) geometric visual system arrangement (considering extrinsic parameters and stereovision systems). A general overview, with detailed description and technical support, is provided for each topic with illustrative examples focused on specific applications in agriculture, although they could be applied in different contexts other than agricultural. A case study is provided as a result of research in the RHEA (Robot Fleets for Highly Effective Agriculture and Forestry Management) project for effective weed control in maize fields (wide-rows crops), funded by the European Union, where the machine vision system onboard the autonomous vehicles was the most important part of the full perception system, where machine vision was the most relevant. Details and results about crop row detection, weed patches identification, autonomous vehicle guidance and obstacle detection are provided together with a review of methods and approaches on these topics.
Identifiersdoi: 10.3390/jimaging2040034
Appears in Collections:(CAR) Artículos
Files in This Item:
File Description SizeFormat 
jimaging-02-00034.pdf12,07 MBAdobe PDFThumbnail
Show full item record
Review this work

WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.