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- W3000694414 abstract "This research uses Unmanned Arial Vehicle (UAV) imagery and manual samplings methods simultaneously to recognize parasitic weed at Alfalfa stand. The objective is to identify the best techniques to monitor parasitic weed infestation. Imagery was digitized using GIS software to measure weed spatial distribution across four strips. Advantage and limitations of using the drone to the arid land conditions are discussed. Results indicated UAV, manual sampling, and GIS software methods are complemented rather than substituted to each other. The integration of the UAV and GIS components produces a feasible option against costly complex multi-spectrum camera systems for parasitic weed recognition. The proposed system enables early weed recognition for farmers to enable further parasitic weed management." @default.
- W3000694414 created "2020-01-23" @default.
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- W3000694414 date "2019-11-01" @default.
- W3000694414 modified "2023-10-16" @default.
- W3000694414 title "Unmanned Arial Vehicle (UAV) Imagery and Manual Sampling for Parasitic Weed Recognition and Measurements" @default.
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- W3000694414 doi "https://doi.org/10.1109/icecta48151.2019.8959613" @default.
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