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Sensors 2017, 17(10), 2381;

Optical Sensing of Weed Infestations at Harvest

Columbia Basin Agricultural Research Center, Oregon State University, Adams, OR 97810, USA
Soil and Water Conservation Research Unit, Agricultural Research Service (USDA-ARS), Adams, OR 97810, USA
Author to whom correspondence should be addressed.
Received: 24 August 2017 / Revised: 23 September 2017 / Accepted: 10 October 2017 / Published: 19 October 2017
(This article belongs to the Special Issue Sensors in Agriculture and Forestry)
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Kochia (Kochia scoparia L.), Russian thistle (Salsola tragus L.), and prickly lettuce (Lactuca serriola L.) are economically important weeds infesting dryland wheat (Triticum aestivum L.) production systems in the western United States. Those weeds produce most of their seeds post-harvest. The objectives of this study were to determine the ability of an optical sensor, installed for on-the-go measurement of grain protein concentration, to detect the presence of green plant matter in flowing grain and assess the potential usefulness of this information for mapping weeds at harvest. Spectra of the grain stream were recorded continuously at a rate of 0.33 Hz during harvest of two spring wheat fields of 1.9 and 5.4 ha. All readings were georeferenced using a Global Positioning System (GPS) receiver with 1 m positional accuracy. Chlorophyll of green plant matter was detectable in the red (638–710 nm) waveband. Maps of the chlorophyll signal from both fields showed an overall agreement of 78.1% with reference maps, one constructed prior to harvest and the other at harvest time, both based on visual evaluations of the three green weed species conducted by experts. Information on weed distributions at harvest may be useful for controlling post-harvest using variable rate technology for herbicide applications. View Full-Text
Keywords: weed mapping; on-line optical sensing; site-specific weed management weed mapping; on-line optical sensing; site-specific weed management

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Barroso, J.; McCallum, J.; Long, D. Optical Sensing of Weed Infestations at Harvest. Sensors 2017, 17, 2381.

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