首页|Research from University of Kentucky Provides New Study Findings on Machine Lear ning (Hyperspectral Imaging and Machine Learning as a Nondestructive Method for Proso Millet Seed Detection and Classification)
Research from University of Kentucky Provides New Study Findings on Machine Lear ning (Hyperspectral Imaging and Machine Learning as a Nondestructive Method for Proso Millet Seed Detection and Classification)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting out of Lexington, Kentucky, b y NewsRx editors, research stated, “Millet is a small-seeded cereal crop with bi g potential.” Financial supporters for this research include Usda-nifa Multistate. The news editors obtained a quote from the research from University of Kentucky: “There are many different cultivars of proso millet (Panicum miliaceum L.) with different characteristics, bringing forth the issue of sorting which are import ant for growers, processors, and consumers. Current methods of grain cultivar de tection and classification are subjective, destructive, and time-consuming. Ther efore, there is a need to develop nondestructive methods for sorting the cultiva rs of proso millet. In this study, the feasibility of using near-infrared (NIR) hyperspectral imaging (900-1700 nm) to discriminate between different cultivars of proso millet seeds was evaluated. A total of 5000 proso millet seeds were ran domly obtained and investigated from the ten most popular cultivars in the Unite d States, namely Cerise, Cope, Earlybird, Huntsman, Minco, Plateau, Rise, Snowbi rd, Sunrise, and Sunup. To reduce the large dimensionality of the hyperspectral imaging, principal component analysis (PCA) was applied, and the first two princ ipal components were used as spectral features for building the classification m odels because they had the largest variance.”
University of Kentucky, Lexington, Kentu cky, United States, North and Central America, Cyborgs, Emerging Technologies, M achine Learning
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