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An Advanced Real-time Plant Species Identification System
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: Paper number 131619696, 2013 Kansas City, Missouri, July 21 - July 24, 2013. (doi: http://dx.doi.org/10.13031/aim.20131619696) @2013
Authors: George E Meyer, Garret F Coffman, Kathryn Conroy, Stephen L Young
Keywords: Machine Vision Plants Species Identification
Abstract. Undesirable plants are negatively impacting many ecosystems and causing significant losses both economically and environmentally. Examples of losses include altered stream flow in riparian areas, increased frequency of fires in rangelands and forests, fewer habitats with high species diversity and increased management for aesthetics and yields in natural areas and production systems, respectively. The services provided by the terrestrial systems in which undesirable plants have invaded or are established have yet to be completely quantified, but estimates are in the billions of dollars. An improved plant species identification program is being developed and is currently being tested as potential "ap" for identifying selected plant species found in Nebraska and fields of the Great Plains. The identification process takes place through an acquired digital leaf image. The identification algorithm performs a classical analysis using a Fast Fourier Transform but also concentrates on detailed leaf venation and surface texture. Leaf species samples were initially supplied from plants grown in an environmental chamber, but will extend to spring/summer/fall field specimens. Plants will eventually include various herbaceous species that are native and non-native (e.g., velvet leaf, pigweed, downy brome, phragmites, common reed, leafy spurge, cheat grass). Specimens were imaged using a high-resolution digital camera and a special portable lighting system. The impact from the development of a real-time plant species identification system (APSIS) to identify desired and undesirable plants would revolutionize the way plant populations are studied by researchers, ecologists, and monitored by crop and land managers.
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