An Autonomous Weeding Robot for Organic Farming

Bakker, T.; Asselt, K. van; Bontsema, J.; Müller, Joachim; Straten, G. van

doi:10.1007/11736592_48

Cited by 8 publications

(8 citation statements)

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“…One solution to this problem could be to robotise intelligent weeding machines, thereby cutting labour (operator) costs. Robotic weeding is not yet as close to practical application as the sensing technologies described above, although the University of Halmstad in Sweden has a prototype working in sugar beet (http://www2.hh.se/staff/bjorn/mech-weed) and similar developments are taking place in the Netherlands (Zuydam & Achten, 2002; Bakker et al. , 2005; Van Evert et al.…”

Section: Non‐selective Tools Combined With Crop Sensingmentioning

confidence: 99%

Innovation in mechanical weed control in crop rows

et al. 2008

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SummaryWeed control within crop rows is one of the main problems in organic farming. For centuries, different weed removal tools have been used to reduce weeds in the crop rows. Stimulated by the demand from organic farmers, research in several European countries over the last decade has focused on mechanisation using harrowing, torsion finger weeding and weeding with compressed air (Pneumat). Intelligent weeders are now being developed which offer more advanced ways to control weeds, including larger ones and to leave the crop plants unharmed. One of the first commercially available intelligent weeders, the Sarl Radis from France, has a simple crop detection system based on light interception, which guides a hoe in and out of the crop row, around the crop plants. The inclusion of innovative technologies, including advanced sensing and robotics, in combination with new cropping systems, might lead to a breakthrough in physical weed control in row crops leading to significant reductions, or even elimination, of the need for hand weeding.

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Section: Non‐selective Tools Combined With Crop Sensingmentioning

confidence: 99%

Innovation in mechanical weed control in crop rows

et al. 2008

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“…Although much scientific progress has been made in this area (e.g. Bakker et al ., , ), certainly in the field of algorithms for weed‐detection (Eddy et al ., ), commercial use is still limited (Merfield, ). The aim of these kinds of efforts is illustrated by a prototype weed control robot that is truly autonomous (Van Evert et al ., ).…”

Section: Current Applications Of Big Data For Weed Control and Crop Pmentioning

confidence: 99%

Big Data for weed control and crop protection

et al. 2017

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SummaryFarmers have access to many data-intensive technologies to help them monitor and control weeds and pests. Data collection, data modelling and analysis, and data sharing have become core challenges in weed control and crop protection. We review the challenges and opportunities of Big Data in agriculture: the nature of data collected, Big Data analytics and tools to present the analyses that allow improved crop management decisions for weed control and crop protection. Big Data storage and querying incurs significant challenges, due to the need to distribute data across several machines, as well as due to constantly growing and evolving data from different sources. Semantic technologies are helpful when data from several sources are combined, which involves the challenge of detecting interactions of potential agronomic importance and establishing relationships between data items in terms of meanings and units. Data ownership is analysed using the ethical matrix method to identify the concerns of farmers, agribusiness owners, consumers and the environment. Big Data analytics models are outlined, together with numerical algorithms for training them. Advances and tools to present processed Big Data in the form of actionable information to farmers are reviewed, and a success story from the Netherlands is highlighted. Finally, it is argued that the potential utility of Big Data for weed control is large, especially for invasive, parasitic and herbicide-resistant weeds. This potential can only be realised when agricultural scientists collaborate with data scientists and when organisational, ethical and legal arrangements of data sharing are established.

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“…It has been applied to various agricultural operations, such as weeding [3,4], plant monitoring [5], application of fertilizer and chemicals [6] and harvesting [7][8][9]. However, to date, there have been limited commercial applications of these agricultural robots; mainly due to their high cost.…”

Section: Introductionmentioning

confidence: 99%

Moving Object Localization Using Sound-Based Positioning System with Doppler Shift Compensation

et al. 2013

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Sound-based positioning systems are a potential alternative low-cost navigation system. Recently, we developed such an audible sound-based positioning system, based on a spread spectrum approach. It was shown to accurately localize a stationary object. Here, we extend this localization to a moving object by compensating for the Doppler shift associated with the object movement. Numerical simulations and experiments indicate that by compensating for the Doppler shift, the system can accurately determine the position of an object moving along a non-linear path. When the object moved in a circular path with an angular velocity of 0 to 1.3 rad/s, it could be localized to within 25 mm of the actual position. Experiments also showed the proposed system has a high noise tolerance of up to −25 dB signal-to-noise ratio (SNR) without compromising accuracy.

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An Autonomous Weeding Robot for Organic Farming

Cited by 8 publications

References 0 publications

Innovation in mechanical weed control in crop rows

Innovation in mechanical weed control in crop rows

Big Data for weed control and crop protection

Moving Object Localization Using Sound-Based Positioning System with Doppler Shift Compensation

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