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      Agricultural Robotics for Field Operations

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          Abstract

          Modern agriculture is related to a revolution that occurred in a large group of technologies (e.g., informatics, sensors, navigation) within the last decades. In crop production systems, there are field operations that are quite labour-intensive either due to their complexity or because of the fact that they are connected to sensitive plants/edible product interaction, or because of the repetitiveness they require throughout a crop production cycle. These are the key factors for the development of agricultural robots. In this paper, a systematic review of the literature has been conducted on research and commercial agricultural robotics used in crop field operations. This study underlined that the most explored robotic systems were related to harvesting and weeding, while the less studied were the disease detection and seeding robots. The optimization and further development of agricultural robotics are vital, and should be evolved by producing faster processing algorithms, better communication between the robotic platforms and the implements, and advanced sensing systems.

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          Most cited references148

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          Autonomous robotic weed control systems: A review

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            Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring

            Current approaches to field phenotyping are laborious or permit the use of only a few sensors at a time. In an effort to overcome this, a fully automated robotic field phenotyping platform with a dedicated sensor array that may be accurately positioned in three dimensions and mounted on fixed rails has been established, to facilitate continual and high-throughput monitoring of crop performance. Employed sensors comprise of high-resolution visible, chlorophyll fluorescence and thermal infrared cameras, two hyperspectral imagers and dual 3D laser scanners. The sensor array facilitates specific growth measurements and identification of key growth stages with dense temporal and spectral resolution. Together, this platform produces a detailed description of canopy development across the crops entire lifecycle, with a high-degree of accuracy and reproducibility.
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              Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                07 May 2020
                May 2020
                : 20
                : 9
                : 2672
                Affiliations
                [1 ]Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece; nmylonas@ 123456aua.gr (N.M.); gmalounas@ 123456aua.gr (I.M.); efthimisr@ 123456yahoo.gr (E.R.)
                [2 ]Fraunhofer IPA, Nobelstr 12, 70569 Stuttgart, Germany; christoph.hellmann.santos@ 123456ipa.fraunhofer.de
                [3 ]Wageningen Plant Research, Wageningen University and Research, P.O. Box 644, 6700 AP Wageningen, The Netherlands; erik.pekkeriet@ 123456wur.nl
                Author notes
                [* ]Correspondence: sfountas@ 123456aua.gr
                Article
                sensors-20-02672
                10.3390/s20092672
                7273211
                32392872
                b7547914-d3ec-4b79-a9a5-f0cf5ec07321
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 30 March 2020
                : 05 May 2020
                Categories
                Review

                Biomedical engineering
                crops,autonomous vehicles,field operations,perception,execution
                Biomedical engineering
                crops, autonomous vehicles, field operations, perception, execution

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