An artificial intelligence method for rapid p : vimarsana.co

An artificial intelligence method for rapid p

Estimating photosynthetic quantum yield makes plant phenotyping easy. However, plant samples must be dark-adapted, which is time-consuming and complicates measurement of the ratio of variable to maximum fluorescence (Fv/Fm). A research consortium led by scientists from Jiangnan University has developed an artificial intelligence method, known as least-squares support vector machine model (LSSVM), that makes rapid Fv/Fm calculations without dark adaptation. This high-throughput method saves time, processes complex datasets, and is applicable in the field.

Related Keywords

Zhao Tang , Henan , China , Jinglu , Zhejiang , Qian Xia , Jiangsu , United States , Jiangnan , Sichuan , Urbana Champaign , Lijiang Fu , Ya Guo , Govindjee , Department Of Plant Biology , Method Of Research , University Of Illinois At Urbana Champaign , Department Of Biochemistry , University Of Missouri , University Of Columbia , Department Of Biomedical , Jiangnan University , University Of Illinois , Illinois Urbana Champaign , Key Laboratory , Advanced Process Control , Light Industry , Chemical Engineering , Quantitative Biology , Plant Biology , Dark Adaptation ,

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