Machine-Vision Systems Selection for Agricultural Vehicles: A Guide

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Pajares Martinsanz, Gonzalo and García Santillán, Iván Danilo and Campos Silvestre, Yerania and Montalvo Martínez, Martín and Guerrero, José and Emmi, Luis and Romeo, Juan and Guijarro Mata-García, María and González de Santos, Pablo (2016) Machine-Vision Systems Selection for Agricultural Vehicles: A Guide. Journal of Imaging, 2 (4). p. 34. ISSN 2313-433X

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Official URL: https://doi.org/10.3390/jimaging2040034




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.


Item Type:Article
Uncontrolled Keywords:machine-vision; spectral bands; imaging sensors; optical systems; geometric arrangement; 3D/2D mapping; crop rows detection; weed control; guidance; obstacle detection
Subjects:Sciences > Computer science
Sciences > Computer science > Artificial intelligence
ID Code:67621
Deposited On:03 Sep 2021 08:11
Last Modified:03 Sep 2021 08:11

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