RT Journal Article
T1 Machine-Vision Systems Selection for Agricultural Vehicles: A Guide
A1 Pajares Martinsanz, Gonzalo
A1 García Santillán, Iván Danilo
A1 Campos Silvestre, Yerania
A1 Montalvo Martínez, Martín
A1 Guerrero, José
A1 Emmi, Luis
A1 Romeo, Juan
A1 Guijarro Mata-García, María
A1 González de Santos, Pablo
AB 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.
PB MDPI
SN 2313-433X
YR 2016
FD 2016-11-22
LK https://hdl.handle.net/20.500.14352/19219
UL https://hdl.handle.net/20.500.14352/19219
LA eng
NO Unión Europea. FP7
DS Docta Complutense
RD 30 abr 2024