首页|Studies from China Agricultural University Further Understanding of Machine Lear ning (Classifying Grain and Impurity To Assess Maize Cleaning Loss Using Time-fr equency Images of Vibro-piezoelectric Signals Coupling Machine Learning)
Studies from China Agricultural University Further Understanding of Machine Lear ning (Classifying Grain and Impurity To Assess Maize Cleaning Loss Using Time-fr equency Images of Vibro-piezoelectric Signals Coupling Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Fresh data on Machine Learning are pre sented in a new report. According to newsreporting out of Beijing, People’s Rep ublic of China, by NewsRx editors, research stated, “Accuratelydifferentiating maize mixtures and assessing grain cleaning loss contributes to improving the ef ficiencyand sustainability of agricultural systems. This study proposes a novel detection method integrating timefrequencyimages of particle vibro-piezoelect ric signals and machine learning to classify grain and impurityand assess maize cleaning loss.”
BeijingPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningChina Agricultural Universi ty
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