Tobacco Moisture Content Monitoring Based on RSM Algorithm
In order to explore the application of RSM algorithm in the monitoring of tobacco moisture content,the samples of tobacco leaf No.8 of Guiyan were collected,and the visible light sampling was carried out from 09:00 to 12:00,and the gradient processing of image brightness was carried out to simulate the change of all-day light.The measured moisture content of tobacco leaf samples and the RGB third-order color moment data of the image were used as data sets.The RSM algorithm was used to establish the moisture content regression model for the samples,and compared with the LM neural network algorithm and the SVM algorithm.The results showed that the RSM algo-rithm based on the RGB color moments of tobacco leaves had good application effect.The determination coefficient of the regression model was 0.9202,the root mean square error(RMSE)was 0.56％,and the relative analysis error(RPD)was 3.5483.Therefore,the regression model of leaf moisture content based on the random subspace RSM algorithm has good stability and can realize the monitoring of tobacco moisture content.
tobacco moisture contentRSM algorithmRGB color momentmonitoring
NETL
NSTL
万方数据
维普
