Detection of Field Structures for Agricultural Vehicle Guidance

Fleischmann, Patrick; Föhst, Tobias; Berns, Karsten

doi:10.1007/s13218-013-0264-1

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Agricultural Big Data Analytics and the Ethics of Power Griepentrog et al 2013;Bernardi 2013;Fleischmann et al 2013;Scheuren et al 2013). A recent agricultural technological development is data retrieval and analytics to assess specific patterns and prescribe alternatives with regards to planting, watering, weeding, harvesting, and general care of their farm.…”

Section: Agricultural Technologymentioning

confidence: 99%

Agricultural Big Data Analytics and the Ethics of Power

Ryan

2019

J Agric Environ Ethics

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Agricultural Big Data analytics (ABDA) is being proposed to ensure better farming practices, decision-making, and a sustainable future for humankind. However, the use and adoption of these technologies may bring about potentially undesirable consequences, such as exercises of power. This paper will analyse Brey's five distinctions of power relationships (manipulative, seductive, leadership, coercive, and forceful power) and apply them to the use agricultural Big Data. It will be shown that ABDA can be used as a form of manipulative power to initiate cheap land grabs and acquisitions. Seductive power can be exercised by pressuring farmers into situations they would not have otherwise chosen (such as installing monitors around their farm and limited access to their farm and machinery). It will be shown that agricultural technology providers (ATPs) demonstrate leadership power by getting farmers to agree to use ABDA without informed consent. Coercive power is exercised when ATPs threaten farmers with the loss of ABDA if they do not abide by the policies and requirements of the ATP or are coerced to remain with the ATP because of fear of legal and economic reprisal. ATPs may use ABDA to determine willingness-topay rates from farmers, using this information to force farmers into precarious and vulnerable positions. Altogether, this paper will apply these five types of power to the use and implementation of ABDA to demonstrate that it is being used to exercise power in the agricultural industry.

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Section: Agricultural Technologymentioning

confidence: 99%

Agricultural Big Data Analytics and the Ethics of Power

Ryan

2019

J Agric Environ Ethics

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“…Methods that rely on 3D structure detect the height difference between the ground and crop rows using scanning lasers (Biber et al., ; Chateau et al., ; Fleischmann et al., ; Hiremath et al., ; Weiss & Biber, ), or stereo vision (Hanawa et al., 2012; Hiremath et al., 2014; Rovira et al., 2004; Rovira‐Más et al., 2008). The use of 3D structure‐based methods relies on detecting a height difference between the crop and the ground, thus they may not be applicable to very young or short crops.…”

Section: Literaturementioning

confidence: 99%

Vision‐based Obstacle Detection and Navigation for an Agricultural Robot

et al. 2016

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This paper describes a vision-based obstacle detection and navigation system for use as part of a robotic solution for the sustainable intensification of broad-acre agriculture. To be cost-effective, the robotics solution must be competitive with current human-driven farm machinery. Significant costs are in high-end localization and obstacle detection sensors. Our system demonstrates a combination of an inexpensive global positioning system and inertial navigation system with vision for localization and a single stereo vision system for obstacle detection. The paper describes the design of the robot, including detailed descriptions of three key parts of the system: novelty-based obstacle detection, visually-aided guidance, and a navigation system that generates collision-free kinematically feasible paths. The robot has seen extensive testing over numerous weeks of field trials during the day and night. The results in this paper pertain to one particular 3 h nighttime experiment in which the robot performed a coverage task and avoided obstacles. Additional results during the day demonstrate that the robot is able to continue operating during 5 min GPS outages by visually following crop rows. C 2016Wiley Periodicals, Inc.This section describes the field environment, the robot (including the physical and software robot platform), and the algorithmic details of our navigation system.

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“…Laser scanner-based systems such as those presented by Fleischmann et al (2013) and Choi et al (2014) track the work edge during harvesting operations by detecting the difference in height between the cut and un-cut portions of the crop.…”

Section: Work Edge Trackingmentioning

confidence: 99%

Visually-Aided Localisation for Autonomous Agricultural Vehicles

English¹

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This thesis presents an approach to visually-aided navigation of agricultural robots in cropping fields.Many agricultural robots and vehicles navigate solely using sophisticated RTK-GPS/INS systems, however these systems remain expensive or are prone to signal occlusion. Alternatively, navigation in agricultural fields can be performed relative to crop rows using local sensors such as cameras. However, current visual crop row tracking algorithms are applicable to only a subset of fields since they make strong and restrictive assumptions about the appearance of the crop. Additionally, current navigation filters for agricultural robots do not fuse crop row tracking together with global navigation sensors to improve their position estimate.This thesis proposes a localisation system suitable for small agricultural robots that augments low-cost GPS and odometry sensors with novel visual crop row tracking methods. In working towards this goal this thesis provides two main contributions.

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Detection of Field Structures for Agricultural Vehicle Guidance

Cited by 6 publications

References 8 publications

Agricultural Big Data Analytics and the Ethics of Power

Agricultural Big Data Analytics and the Ethics of Power

Vision‐based Obstacle Detection and Navigation for an Agricultural Robot

Visually-Aided Localisation for Autonomous Agricultural Vehicles

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