The invention provides a method for predicting the early
nicotine conversion rate of
tobacco nicotine transformed plants based on a hyperspectral technology and application, and belongs to the technical field of
agricultural information technologies and
plant physiology. The method is used for solving the technical problems of
hysteresis, destructiveness and low efficiency caused by the traditional method for determining the
nicotine conversion rate and screening the
nicotine transformant by a pure chemical method. The identification method comprises the following steps: acquiring hyperspectral data of a to-be-detected tobacco
plant, inputting the hyperspectral data of the to-be-detected tobacco
plant into a
tobacco nicotine transformed plant early-stage identification model, predicting the content of nicotine and
nornicotine, calculating the nicotine transformation rate, and judging the type of the to-be-detected tobacco plant according to the nicotine transformation rate. Based on the combination of hyperspectral
remote sensing and
machine learning, the nicotine and
nornicotine contents of the tobacco plants are predicted, and the nicotine conversion rate is rapidly determined, so that the rapid, accurate and nondestructive early
screening method for the
tobacco nicotine transformed plants is realized.