Control Effects of Different Agents on Tobacco Mosaic Virus Disease

[Objectives] This study was conducted to screen out suitable agents for controlling tobacco mosaic virus disease and the best control period in Zhangzhou tobacco area, providing a theoretical basis for the control of virus diseases, thereby improving the quality of flue-cured tobacco and the income of tobacco farmers. [Methods] The effects on tobacco mosaic virus disease under the interaction between different agents and different application periods were investigated. The incidence of tobacco mosaic virus disease was investigated, and its control effect was analyzed. [Results] Different agents and different application periods had different control effects on tobacco mosaic virus disease. The incidence of tobacco mosaic virus disease: At 30 and 45 d after transplanting, the incidences of A2B1 treatment were the lowest, at 0.85%, 1.71%, respectively;and at 60 d after transplanting, the incidence of A3B1 treatment was the lowest, only 10.68%. The control effect: At 30 and 45 d after transplanting, A2B1 treatment had better control effects, reaching 79.39% and 73.06%, respectively. [Conclusions] 3% hypersensitive protein sprayed at 1 d before transplanting and 7 and 15 d after transplanting achieved the best effect, followed by 10% ningnanmycin sprayed at 1 d before transplanting and 7 and 15 d after transplanting. In tobacco production, it is recommended to apply 1 000 times dilution of 3% supersensitive protein microgranules for three times(at 1 d before transplanting and 7 and 15 d after transplanting), which can effectively prevent tobacco mosaic virus disease.

Grading method of soybean mosaic disease based on hyperspectral imaging technology

Soybean is a crop with a long cultivation history that occupies an important position in agricultural production.Soybean mosaic virus disease(SMV)has caused a rapid decline in soybean yields,causing huge losses to the soybean industry,wherefrom its early detec-tion is particularly important.This study proposes a new classification method for the early SMV,dividing its severity into grades 0,1 and 2.In the case of a small number of experi-mental samples of soybeans,this study proposes a combined convolutional neural network and support vector machine(CNN-SVM)method for the early detection of SMV.Experimen-tal results showed that the accuracy of the training set of the CNN-SVM model reached 96.67%,and the accuracy rate of the test set reached 94.17%.The experiment proved the feasibility of using the proposed CNN-SVM model to classify early SMV under the new clas-sification method,and provided a new direction for early SMV detection based on hyper-spectral images.