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• W3111036395 abstract "We present an image processing method for accurately segmenting crop plots from Unmanned Aerial System imagery (UAS). The use of UAS for agricultural monitoring has increased significantly, emerging as a potentially cost effective alternative to manned aerial surveys and field work for remotely assessing crop state. The accurate segmentation of small densely-packed crop plots from UAS imagery over extensive areas is an important component of this monitoring activity in order to assess the state of different varieties and treatment regimes in a timely and cost-effective manner. Despite its importance, a reliable crop plot segmentation approach eludes us, with best efforts being relying on significant manual parameterization. The segmentation method developed uses a combination of edge detection and Hough line detection to establish the boundaries of each plot with pixel/point based metrics calculated for each plot segment. We show that with limited parameterization, segmentation of crop plots consistently over 89% accuracy are possible on different crop types and conditions. This is comparable to results obtained from rice paddies where the plant material in plots is sharply contrasted with the water, and represents a considerable improvement over previous methods for typical dry land crops." @default.
• W3111036395 created "2020-12-21" @default.
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• W3111036395 date "2020-12-09" @default.
• W3111036395 modified "2023-09-26" @default.
• W3111036395 title "Semi-Automated Field Plot Segmentation From UAS Imagery for Experimental Agriculture" @default.
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• W3111036395 doi "https://doi.org/10.3389/fpls.2020.591886" @default.
• W3111036395 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7755984" @default.
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