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Robotics for weed control: I-Weed Robot for a specific spraying

Authors: Gée, Christelle; Busvelle, Eric; Salis, G.; Villette, Sylvain; Paoli, Jean-Noël; JONES, Gawain;

Robotics for weed control: I-Weed Robot for a specific spraying

Abstract

International audience; To preserve environment for a sustainable agriculture, we explore the development of a new autonomous robot, called I-Weed Robot (Intelligent Weed Robot), which aims at reducing herbicides in crop fields (maize, sunflower...). Using a high precision positioning signal (RTK) to locate the robot in the field, a Kaman filter and a proportional-integral-derivative controller (PID controller) allow adjusting the orientation of the robot depending on a predefined trajectory. As for the spraying system, a camera in front of the mobile platform detects weed plants thanks to an image processing based on a crop/weed discrimination algorithm (Hough Transform). At the back a spray boom triggers the right nozzle at the right time depending on the location of weeds. This article assesses the performance of the guidance and weed detection algorithms using numerical simulations (virtual trajectory, virtual field image). The robustness of the guidance algorithm is tested for different noisy signals (GPS, DGPS and RTK). The accuracy of the crop/weed discrimination algorithm is evaluated using a large data base of synthetic images generated by the 'SimAField' software. To evaluate the accuracy of the algorithm and understand the sources of misclassification errors, the results are summarized in a confusion matrix which indicates the number of correctly and incorrectly classified pixels (both weed and crop classes). Results indicate accuracy up to 90%. A significant number of weed pixels are always considered as crops so that crop detection is overestimated. The reason is that the bandwidth value of the crop row mask that is automatically generated overlaps weed plant close to the crop row. These simulations demonstrate that both algorithms are reliable. However, further research in field conditions is necessary to confirm the promising results.

Country
France
Keywords

[SDV] Life Sciences [q-bio], weed control, herbicides, Precision agriculture, spraying, [SDV]Life Sciences [q-bio], robots, machine vision, Weeds, algorithms, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing

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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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