{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T10:19:14Z","timestamp":1785752354766,"version":"3.56.0"},"reference-count":82,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2016,11,22]],"date-time":"2016-11-22T00:00:00Z","timestamp":1479772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Seventh Framework Programme [FP7\/2007-2013]","award":["245986"],"award-info":[{"award-number":["245986"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/jimaging2040034","type":"journal-article","created":{"date-parts":[[2016,11,22]],"date-time":"2016-11-22T11:13:07Z","timestamp":1479813187000},"page":"34","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Machine-Vision Systems Selection for Agricultural Vehicles: A Guide"],"prefix":"10.3390","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0915-6282","authenticated-orcid":false,"given":"Gonzalo","family":"Pajares","sequence":"first","affiliation":[{"name":"Department Software Engineering, School of Computer Science, University Complutense of Madrid, Jos\u00e9 Garc\u00eda Santesmases, 16, 28040 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iv\u00e1n","family":"Garc\u00eda-Santill\u00e1n","sequence":"additional","affiliation":[{"name":"Department Software Engineering, School of Computer Science, University Complutense of Madrid, Jos\u00e9 Garc\u00eda Santesmases, 16, 28040 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yerania","family":"Campos","sequence":"additional","affiliation":[{"name":"Department Software Engineering, School of Computer Science, University Complutense of Madrid, Jos\u00e9 Garc\u00eda Santesmases, 16, 28040 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2801-2372","authenticated-orcid":false,"given":"Mart\u00edn","family":"Montalvo","sequence":"additional","affiliation":[{"name":"Department Software Engineering, School of Computer Science, University Complutense of Madrid, Jos\u00e9 Garc\u00eda Santesmases, 16, 28040 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9","family":"Guerrero","sequence":"additional","affiliation":[{"name":"Department Software Engineering, School of Computer Science, University Complutense of Madrid, Jos\u00e9 Garc\u00eda Santesmases, 16, 28040 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis","family":"Emmi","sequence":"additional","affiliation":[{"name":"Center for Automation and Robotics, UPM-CSIC, 28500 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Romeo","sequence":"additional","affiliation":[{"name":"Department Software Engineering, School of Computer Science, University Complutense of Madrid, Jos\u00e9 Garc\u00eda Santesmases, 16, 28040 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mar\u00eda","family":"Guijarro","sequence":"additional","affiliation":[{"name":"Department of Computer Architecture and Automatic, School of Computer Science, University Complutense of Madrid, 28040 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0219-3155","authenticated-orcid":false,"given":"Pablo","family":"Gonzalez-de-Santos","sequence":"additional","affiliation":[{"name":"Center for Automation and Robotics, UPM-CSIC, 28500 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2016,11,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.compag.2007.05.008","article-title":"Autonomous robotic weed control systems: A review","volume":"61","author":"Slaughter","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","unstructured":"Shalal, N., Low, T., McCarthy, C., and Hancock, N. (2013, January 22\u201325). A review of autonomous navigation systems in agricultural environments. Proceedings of the SEAg 2013: Innovative Agricultural Technologies for a Sustainable Future, Barton, Australia. Available online: http:\/\/eprints.usq.edu.au\/24779\/."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.jterra.2013.03.004","article-title":"A technical review on navigation systems of agricultural autonomous off-road vehicles","volume":"50","author":"Mousazadeh","year":"2013","journal-title":"J. Terramech."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1365-3180.2010.00829.x","article-title":"Weed detection for site-specific weed management: Mapping and real-time approaches","volume":"51","year":"2011","journal-title":"Weed Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"484390","DOI":"10.1100\/2012\/484390","article-title":"Crop row detection in maize fields inspired on the human visual perception","volume":"2012","author":"Romeo","year":"2012","journal-title":"Sci. World J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2275","DOI":"10.1016\/j.eswa.2012.10.033","article-title":"A new expert system for greenness identification in agricultural images","volume":"40","author":"Romeo","year":"2013","journal-title":"Exp. Syst. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"11149","DOI":"10.1016\/j.eswa.2012.03.040","article-title":"Support vector machines for crop\/weeds identification in maize fields","volume":"39","author":"Guerrero","year":"2012","journal-title":"Exp. Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.compag.2007.06.003","article-title":"Crop\/weed discrimination in perspective agronomic images","volume":"60","author":"Bossu","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.compag.2008.08.002","article-title":"Mean-shift-based color segmentation of images containing green vegetation","volume":"65","author":"Zheng","year":"2009","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"11889","DOI":"10.1016\/j.eswa.2012.02.117","article-title":"Automatic detection of crop rows in maize fields with high weeds pressure","volume":"39","author":"Montalvo","year":"2012","journal-title":"Exp. Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.compag.2010.09.013","article-title":"Automatic segmentation of relevant textures in agricultural images","volume":"75","author":"Guijarro","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.compag.2008.09.001","article-title":"Improving weed pressure assessment using digital images from an experience-based reasoning approach","volume":"65","author":"Ribeiro","year":"2009","journal-title":"Comput. Electron. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7095","DOI":"10.3390\/s110707095","article-title":"Mapping wide row crops with video sequences acquired from a tractor moving at treatment speed","volume":"11","author":"Ribeiro","year":"2011","journal-title":"Sensors"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.compag.2007.07.008","article-title":"A new vision-based approach to differential spraying in precision agriculture","volume":"60","author":"Tellaeche","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.compag.2009.02.009","article-title":"Assessment of an inter-row weed infestation rate on simulated agronomic images","volume":"67","author":"Jones","year":"2009","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1016\/j.patcog.2007.07.007","article-title":"A vision-based method for weeds identification through the Bayesian decision theory","volume":"41","author":"Tellaeche","year":"2008","journal-title":"Pattern Recognit."},{"key":"ref_17","first-page":"1","article-title":"Review of research on agricultural vehicle autonomous guidance","volume":"2","author":"Li","year":"2009","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1109\/MCS.1987.1105271","article-title":"Vision-based guidance of an agricultural tractor","volume":"7","author":"Reid","year":"1997","journal-title":"IEEE Control. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/BF00735439","article-title":"Vision-guidance of agricultural vehicles","volume":"2","author":"Billingsley","year":"1995","journal-title":"Auton. Robots"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1080\/10255810390445300","article-title":"Machine vision based automated tractor guidance","volume":"5","author":"Zhang","year":"2003","journal-title":"Int. J. Smart Eng. Syst. Des."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.biosystemseng.2008.08.001","article-title":"Development of a stereovision sensing system for 3D crop row structure mapping and tractor guidance","volume":"101","author":"Kise","year":"2008","journal-title":"Biosyst. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.compag.2012.02.009","article-title":"Variable field-of-view machine vision based row guidance of an agricultural robot","volume":"84","author":"Xue","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2389","DOI":"10.13031\/2013.20078","article-title":"Obstacle detection using stereo vision to enhance safety autonomous machines","volume":"48","author":"Wei","year":"2005","journal-title":"Trans. ASABE"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.compag.2015.02.001","article-title":"Obstacle detection in a greenhouse environment using the Kinect sensor","volume":"113","author":"Nissimov","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.asoc.2016.03.016","article-title":"Spatio-temporal analysis for obstacle detection in agricultural videos","volume":"45","author":"Campos","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.compag.2011.07.007","article-title":"Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection","volume":"78","author":"Cheein","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"281","DOI":"10.14358\/PERS.81.4.281","article-title":"Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs)","volume":"81","author":"Pajares","year":"2015","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_28","unstructured":"RHEA Robot Fleets for Highly Effective Agriculture and Forestry Management. Available online: http:\/\/www.rhea-project.eu\/."},{"key":"ref_29","unstructured":"Exelis Visual Information Solutions. Available online: http:\/\/www.exelisvis.com\/docs\/VegetationIndices.html."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.compag.2008.03.009","article-title":"Verification of color vegetation indices for automated crop imaging applications","volume":"63","author":"Meyer","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","unstructured":"Point Grey Innovation and Imaging How to Evaluate Camera Sensitivity. Available online: https:\/\/www.ptgrey.com\/white-paper\/id\/10912."},{"key":"ref_32","unstructured":"Scheneider Kreuznach Tips and Tricks. Available online: http:\/\/www.schneiderkreuznach.com\/en\/photo-imaging\/product-field\/b-w-fotofilter\/products\/filtertypes\/special-filters\/486-uvir-cut\/."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1111\/j.1469-8137.2010.03536.x","article-title":"Sources of variability in canopy reflectance and the convergent properties of plants","volume":"189","author":"Ollinger","year":"2011","journal-title":"New Phytol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Rabatel, G., Gorretta, N., and Labb\u00e9, S. (2011, January 7\u201311). Getting NDVI Spectral Bands from a Single Standard RGB Digital Camera: A Methodological Approach. Proceedings of the 14th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2011, La Laguna, Spain.","DOI":"10.1007\/978-3-642-25274-7_34"},{"key":"ref_36","unstructured":"Xenics Infrared Solutions Bobcat-640-GigE High Resolution Small form Factor InGaAs Camera. Available online: http:\/\/www.applied-infrared.com.au\/images\/pdf\/Bobcat-640-GigE_Industrial_LowRes.pdf."},{"key":"ref_37","first-page":"1","article-title":"Machine Vision and Soil Trace-based Guidance-Assistance System for Farm Tractors in Soil Preparation Operations","volume":"4","author":"Kiani","year":"2012","journal-title":"J. Agric. Sci."},{"key":"ref_38","first-page":"95","article-title":"Automated crop and weed monitoring in widely spaced cereals","volume":"1","author":"Hague","year":"2006","journal-title":"Precis. Agric."},{"key":"ref_39","unstructured":"JAI 2CCD Cameras. Available online: http:\/\/www.jai.com\/en\/products\/ad-080ge."},{"key":"ref_40","unstructured":"3CCD Color cameras Image acquisition. Resource Mapping. Remote Sensing and GIS for Conservation. Available online: http:\/\/www.resourcemappinggis.com\/image_technical.html."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.biosystemseng.2004.12.008","article-title":"A Stereovision-based Crop Row Detection Method for Tractor-automated Guidance","volume":"90","author":"Kise","year":"2005","journal-title":"Biosyst. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.compag.2007.07.007","article-title":"Stereo vision three-dimensional terrain maps for precision agriculture","volume":"60","author":"Zhang","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"322","DOI":"10.3390\/agronomy4030322","article-title":"Development of a Mobile Multispectral Imaging Platform for Precise Field Phenotyping","volume":"4","author":"Svensgaard","year":"2014","journal-title":"Agronomy"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"20078","DOI":"10.3390\/s141120078","article-title":"A Review of Imaging Techniques for Plant Phenotyping","volume":"14","author":"Li","year":"2014","journal-title":"Sensors"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.eja.2015.11.026","article-title":"Are vegetation indices derived from consumer-grade camerasmounted on UAVs sufficiently reliable for assessing experimentalplots?","volume":"74","author":"Rasmussen","year":"2016","journal-title":"Eur. J. Agron."},{"key":"ref_46","unstructured":"Bockaert, V. Sensor sizes. Digital Photography Review. Available online: http:\/\/www.dpreview.com\/glossary\/camera-system\/sensor-sizes."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4014","DOI":"10.3390\/s140304014","article-title":"Integrating Sensory\/Actuation Systems in Agricultural Vehicles","volume":"14","author":"Emmi","year":"2014","journal-title":"Sensors"},{"key":"ref_48","unstructured":"Choosing the Right Camera Bus. Available online: http:\/\/www.ni.com\/white-paper\/5386\/en\/."},{"key":"ref_49","unstructured":"Cambridge in Colour. Available online: http:\/\/www.cambridgeincolour.com\/tutorials\/camera-exposure.htm."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Montalvo, M., Guerrero, J.M., Romeo, J., Guijarro, M., de la Cruz, J.M., and Pajares, G. (2013, January 28\u201331). Acquisition of Agronomic Images with Sufficient Quality by Automatic Exposure Time Control and Histogram MatchingLecture Notes in Computer Science. Proceedings of the Advanced Concepts for Intelligent Vision Systems (ACIVS\u201913), Poznan, Poland.","DOI":"10.1007\/978-3-319-02895-8_4"},{"key":"ref_51","unstructured":"Cinegon 1.9\/10 Ruggedized Lens. Available online: http:\/\/www.schneiderkreuznach.com\/fileadmin\/user_upload\/bu_industrial_solutions\/industrieoptik\/16mm_Lenses\/Compact_Lenses\/Cinegon_1.9\u201310_ruggedized.pdf."},{"key":"ref_52","unstructured":"Optical Filters. Available online: http:\/\/www.edmundoptics.com\/technical-resources-center\/optics\/optical-filters\/?&#guide."},{"key":"ref_53","unstructured":"Point Grey Innovation and Imaging Selecting a lens for Your Camera. Available online: https:\/\/www.ptgrey.com\/KB\/10694."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"6270","DOI":"10.3390\/s110606270","article-title":"Robust Crop and Weed Segmentation under Uncontrolled Outdoor Illumination","volume":"11","author":"Jeon","year":"2011","journal-title":"Sensors"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.compag.2011.11.007","article-title":"Determination of the number of green apples in RGB images recorded in orchard","volume":"81","author":"Linker","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.biosystemseng.2011.01.003","article-title":"Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops","volume":"108","author":"Moshou","year":"2011","journal-title":"Biosyst. Eng."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compag.2014.03.001","article-title":"Automatic detection of powdery mildew on grapevine leaves by image analysis: Optimal view-angle range to increase the sensitivity","volume":"104","author":"Oberti","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.compag.2010.10.012","article-title":"Stereo vision with texture learning for fault-tolerant automatic baling","volume":"75","author":"Blas","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_59","first-page":"85","article-title":"Performance evaluation of multiple ground based sensors mounted on a commercial wild blueberry harvester to sense plant height, fruit yield and topographic features in real-time","volume":"84","author":"Farooque","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"115","DOI":"10.3390\/jimaging1010115","article-title":"Precise navigation of small agricultural robots in sensitive areas with a smart plant camera","volume":"1","author":"Dworak","year":"2015","journal-title":"J. Imaging"},{"key":"ref_61","unstructured":"Fu, K.S., Gonzalez, R.C., and Lee, C.S.G. (1988). Rob\u00f3tica: Control, Detecci\u00f3n, Visi\u00f3n e Inteligencia, McGraw-Hill."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"15304","DOI":"10.3390\/s140815304","article-title":"A Novel Approach for Weed Type Classification Based on Shape Descriptors and a Fuzzy Decision-Making Method","volume":"14","author":"Herrera","year":"2014","journal-title":"Sensors"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.proeng.2011.11.2514","article-title":"Review on fruit harvesting method for potential use of automatic fruit harvesting systems","volume":"23","author":"Li","year":"2011","journal-title":"Procedia Eng."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"18427","DOI":"10.3390\/s150818427","article-title":"Automated mobile system for accurate outdoor tree crop enumeration using an uncalibrated camera","volume":"15","author":"Nguyen","year":"2015","journal-title":"Sensors"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"V\u00e1zquez-Arellano, M., Griepentrog, H.W., Reiser, D., and Paraforos, D.S. (2016). 3-D imaging systems for agricultural applications-a review. Sensors, 16.","DOI":"10.3390\/s16050618"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.compag.2014.05.006","article-title":"Recognition of clustered tomatoes based on binocular stereo vision","volume":"106","author":"Rong","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Steen, K.A., Christiansen, P., Karstoft, H., and J\u00f8rgensen, R.N. (2016). Using deep learning to challenge safety standard for highly autonomous machines in agriculture. J. Imaging, 2.","DOI":"10.3390\/jimaging2010006"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1145\/356893.356896","article-title":"Computational stereo","volume":"14","author":"Barnard","year":"1982","journal-title":"ACM Comput. Surv."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1109\/34.159902","article-title":"3-D Surface Description from binocular stereo","volume":"14","author":"Cochran","year":"1992","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1646","DOI":"10.1109\/TSMCB.2004.827391","article-title":"On combining support vector machines and simulated annealing in stereovision matching","volume":"34","author":"Pajares","year":"2004","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2043","DOI":"10.1016\/j.eswa.2013.09.003","article-title":"Automatic expert system for 3D terrain reconstruction based on stereo vision and histogram matching","volume":"41","author":"Correal","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.biosystemseng.2009.09.013","article-title":"Design parameters for adjusting the visual field of binocular stereo cameras","volume":"105","author":"Wang","year":"2010","journal-title":"Biosyst. Eng."},{"key":"ref_73","unstructured":"Pajares, G., and de la Cruz, J.M. (2007). Visi\u00f3n por Computador: Im\u00e1genes Digitales y Aplicacione, RA-MA. (In Spanish)."},{"key":"ref_74","unstructured":"MicroStrain Sensing Systems. Available online: http:\/\/www.microstrain.com\/inertial\/3dm-gx3\u201335."},{"key":"ref_75","unstructured":"SVS-VISTEK. Available online: https:\/\/www.svs-vistek.com\/en\/svcam-cameras\/svs-svcam-search-result.php."},{"key":"ref_76","unstructured":"National Instruments CompactRIO. Available online: http:\/\/sine.ni.com\/nips\/cds\/view\/p\/lang\/es\/nid\/210001."},{"key":"ref_77","unstructured":"National Instruments LabView. Available online: http:\/\/www.ni.com\/labview\/esa\/."},{"key":"ref_78","unstructured":"Cyberbotics Webots Robot Simulator. Available online: https:\/\/www.cyberbotics.com\/."},{"key":"ref_79","unstructured":"Gazebo. Available online: http:\/\/gazebosim.org\/."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1016\/j.eswa.2012.07.073","article-title":"Automatic expert system based on images for accuracy crop row detection in maize fields","volume":"40","author":"Guerrero","year":"2013","journal-title":"Exp. Syst. Appl."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Gonzalez-de-Santos, P., Ribeiro, A., Fernandez-Quintanilla, C., L\u00f3pez-Granados, F., Brandstoetter, M., Tomic, S., Pedrazzi, S., Peruzzi, A., Pajares, G., and Kaplanis, G. (2016). Fleets of robots for environmentally-safe pest control in agriculture. Precis. Agric., 1\u201341.","DOI":"10.1007\/s11119-016-9476-3"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.eswa.2015.12.047","article-title":"Mix-opt: A new route operator for optimal coverage path planning for a fleet in an agricultural environment","volume":"54","author":"Pajares","year":"2016","journal-title":"Exp. Syst. Appl."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/2\/4\/34\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:27:10Z","timestamp":1760210830000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/2\/4\/34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11,22]]},"references-count":82,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2016,12]]}},"alternative-id":["jimaging2040034"],"URL":"https:\/\/doi.org\/10.3390\/jimaging2040034","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,11,22]]}}}