Next Article in Journal / Special Issue
Automated Soil Physical Parameter Assessment Using Smartphone and Digital Camera Imagery
Previous Article in Journal
Active Infrared Thermography for Seal Contamination Detection in Heat-Sealed Food Packaging
Previous Article in Special Issue
3D Reconstruction of Plant/Tree Canopy Using Monocular and Binocular Vision
Article Menu

Export Article

Open AccessTechnical Note
J. Imaging 2016, 2(4), 34; doi:10.3390/jimaging2040034

Machine-Vision Systems Selection for Agricultural Vehicles: A Guide

1
Department Software Engineering, School of Computer Science, University Complutense of Madrid, José García Santesmases, 16, 28040 Madrid, Spain
2
Center for Automation and Robotics, UPM-CSIC, 28500 Madrid, Spain
3
Department of Computer Architecture and Automatic, School of Computer Science, University Complutense of Madrid, 28040 Madrid, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Francisco Rovira-Más
Received: 12 September 2016 / Revised: 14 November 2016 / Accepted: 15 November 2016 / Published: 22 November 2016
(This article belongs to the Special Issue Image Processing in Agriculture and Forestry)
View Full-Text   |   Download PDF [12072 KB, uploaded 22 November 2016]   |  

Abstract

Machine 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. View Full-Text
Keywords: machine-vision; spectral bands; imaging sensors; optical systems; geometric arrangement; 3D/2D mapping; crop rows detection; weed control; guidance; obstacle detection machine-vision; spectral bands; imaging sensors; optical systems; geometric arrangement; 3D/2D mapping; crop rows detection; weed control; guidance; obstacle detection
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Pajares, G.; García-Santillán, I.; Campos, Y.; Montalvo, M.; Guerrero, J.M.; Emmi, L.; Romeo, J.; Guijarro, M.; Gonzalez-de-Santos, P. Machine-Vision Systems Selection for Agricultural Vehicles: A Guide. J. Imaging 2016, 2, 34.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
J. Imaging EISSN 2313-433X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top