2024-03-28T08:57:57Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/1421442023-01-25T15:15:12Zcom_10261_31565com_10261_4col_10261_31566
DIGITAL.CSIC
author
Pajares, Gonzalo
author
García-Santillán, Iván
author
Campos, Yerania
author
Montalvo, Martín
author
Guerrero, José Miguel
author
Emmi, Luis Alfredo
author
Romeo, Juan
author
Guijarro, María
author
González-de-Santos, Pablo
funder
Consejo Superior de Investigaciones Científicas (España)
funder
European Commission
funder
Consejo Nacional de Ciencia y Tecnología (México)
funder
Universidad Politécnica Estatal del Carchi
2017-01-04T08:54:12Z
2017-01-04T08:54:12Z
2016-11-22
Journal of Imaging 2(4): 34 (2016)
http://hdl.handle.net/10261/142144
10.3390/jimaging2040034
http://dx.doi.org/10.13039/501100003339http://dx.doi.org/10.13039/501100000780http://dx.doi.org/10.13039/501100003141
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.
openAccess
Machine-Vision Systems Selection for Agricultural Vehicles: A Guide
artículo
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URL
https://digital.csic.es/bitstream/10261/142144/1/jimaging-02-00034.pdf
File
MD5
9804b1829db59a5310bc063900b1eaaa
12361268
application/pdf
jimaging-02-00034.pdf