首页|Recent Findings from University of Laval Has Provided New Information about Machine Learning (Automatic Estimation of Lipid Content From in Situ Images of Arctic Copepods Using Machine Learning)
Recent Findings from University of Laval Has Provided New Information about Machine Learning (Automatic Estimation of Lipid Content From in Situ Images of Arctic Copepods Using Machine Learning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Oxford Univ Press
Current study results on Machine Learning have been published. According to news originating from Quebec City, Canada, by NewsRx correspondents, research stated, "In Arctic marine ecosystems, large planktonic copepods form a crucial hub of matter and energy. Their energy-rich lipid stores play a central role in marine trophic networks and the biological carbon pump." Financial support for this research came from Natural Sciences and Engineering Research Council of Canada (NSERC). Our news journalists obtained a quote from the research from the University of Laval, "Since the past similar to 15 years, in situ imaging devices provide images whose resolution allows us to estimate an individual copepod's lipid sac volume, and this reveals many ecological information inaccessible otherwise. One such device is the Lightframe On-sight Keyspecies Investigation. However, when done manually, weeks of work are needed by trained personnel to obtain such information for only a handful of sampled images. We removed this hurdle by training a machine learning algorithm (a convolutional neural network) to estimate the lipid content of individual Arctic copepods from the in situ images." According to the news editors, the research concluded: "This algorithm obtains such information at a speed (a few minutes) and a resolution (individuals, over half a meter on the vertical), allowing us to revisit historical datasets of in situ images to better understand the dynamics of lipid production and distribution and to develop efficient monitoring protocols at a moment when marine ecosystems are facing rapid upheavals and increasing threats."
Quebec CityCanadaNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversity of Laval
NETL
NSTL
Oxford Univ Press
