Study on Rapid Inversion Method for Chlorophyll Content in Ginseng Leaves Based on Reflectance Spectroscopy

Abstract

Rapid and accurate monitoring of the chlorophyll content of ginseng leaves to reflect the growth condition of ginseng is of great significance in guiding the cultivation of ginseng. To address the problem that the existing means of detecting chlorophyll in ginseng leaves are time-consuming and disruptive and cannot meet the demand for rapid detection of chlorophyll in ginseng leaves, this study firstly establishes a variety of prediction models for chlorophyll in ginseng leaves based on the hyperspectral reflectance data and the vegetation index, respectively, and determines the strengths and weaknesses of the models by comparing the RMSE and the MAE of the test sets; Secondly, the analytical model construction process used for ginseng leaf chlorophyll content prediction was obtained through comparison and summary, and a fast and non-destructive ginseng leaf chlorophyll prediction method based on hyperspectral imaging technology and combining vegetation indices with machine learning algorithms was proposed; The experimental results showed that the final VI-SPA-RFR ginseng leaf chlorophyll prediction model had the best prediction performance, which had an RMSE of 1.1568 and an MAE of 0.9936 in the test set. In summary, this study provides a method to achieve rapid and accurate monitoring of the chlorophyll content of ginseng leaves, which is expected to provide a scientific means of monitoring for the cultivation of ginseng and expand the application field of hyperspectral imagers in the field of agriculture.

Keywords

• Hyperspectral
• ginseng leaves
• chlorophyll
• VI
• RFR