Construction of a tobacco virus disease classification model based on canopy hyperspectra of tobacco plants
[Objective]Tobacco virus diseases affect the growth of tobacco,and even lead to the reduction of tobacco yield and death of tobacco plants,which seriously affects the quality of tobacco.This study was conducted to achieve rapid and efficient tobacco virus disease monitoring.[Method]Using the Finnish SPECIM IQ hyperspectral camera,through the non-destructive detection of the infection degrees of tobacco virus diseases,a hyperspectral camera was used in the tobacco field to collect images of healthy and different levels of virus disease infection degrees of the tobacco plants,pre-processing to extract the wavelength range of 397-1 004 nm of 204 bands of spectral features,and using four wavelength selection methods with four machine learning algorithm,and so sixteen field tobacco virus disease class classification models were constructed.[Result]Model testing results showed as follows:more than half of the models had an accuracy of more than 0.70.Among them,the model identification accuracy of the whale optimization algorithm combined with the random forest algorithm reached 0.83,which was the best performance.The analysis of band selection and variable contribution showed that the near-infrared spectral region had important reference values in distinguishing between healthy and infected tobacco leaves with viral diseases.[Conclusion]This study has proved that based on hyperspectral imaging technology combined with machine learning and wavelength selection algorithms,tobacco virus diseases can be effectively monitored,which provides the foundation and theoretical support for the monitoring of tobacco virus diseases on a wide range of fields.
万方数据
维普
