首页|University of Science and Technology Beijing Reports Findings in Machine Learnin g (Integrating automated machine learning and metabolic reprogramming for the id entification of microplastic in soil: A case study on soybean)
University of Science and Technology Beijing Reports Findings in Machine Learnin g (Integrating automated machine learning and metabolic reprogramming for the id entification of microplastic in soil: A case study on soybean)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating in Beijing, Peopl e’s Republic of China, by NewsRx journalists, research stated, “The accumulation of polyethylene microplastic (PE-MPs) in soil can significantly impact plant qu ality and yield, as well as affect human health and food chain cycles. Therefore , developing rapid and effective detection methods is crucial.” The news reporters obtained a quote from the research from the University of Sci ence and Technology Beijing, “In this study, traditional machine learning (ML) a nd H2O automated machine learning (H2O AutoML) were utilized to offer a powerful framework for detecting PE-MPs (0.1 %, 1 %, and 2 % by dry soil weight) and the co-contamination of PE-MPs and fomesafen (a common h erbicide) in soil. The development of the framework was based on the results of the metabolic reprogramming of soybean plants. Our study stated that traditional ML exhibits lower accuracy due to the challenges associated with optimizing com plex parameters. H2O AutoML can accurately distinguish between clean soil and co ntaminated soil. Notably, H2O AutoML can detect PE-MPs as low as 0.1 % (with 100 % accuracy) and co-contamination of PE-MPs and fomesafen (with 90 % accuracy) in soil. The VIP and SHAP analyses of the H2 O AutoML showed that PE-MPs and the co-contamination of PE-MPs and fomesafen sig nificantly interfered with the antioxidant system and energy regulation of soybe an.”
BeijingPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine Learning
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