Research on High Efficiency Flue-cured Tobacco Grade Identification Model Based on Deep Learning
The grade identification of flue-cured tobacco is a crucial step in the tobacco industry.In order to reduce the labor intensity of tobacco farmers in grading flue-cured tobacco,minimize subjective factors,and enhance identification accuracy,it is necessary to achieve the automatic grading of flue-cured tobacco.This article investigated multiple methods of multi-layer feature extraction using various convolutional neural networks in deep learning.Based on ShuffleNetV2,an improved model(ShuffleNetV2_FTC)was proposed.The ShuffleNetV2_FTC modified the backbone units of ShuffleNetV2 and incorporated the CBAM(Convolutional Block Attention Module)attention mechanism and SiLU activation function.This model was applied to the identification and classification of 27 categories of flue-cured tobacco images.The testing accuracy of this model reached 93.2％,and the detection frame rate achieved 15.3 frames per sec-ond.Compared to the original model,there was an improvement of 0.24％(0.5×),6.06％(1×)and 4.73％(1.5×)in accuracy,as well as an increase in the detection frame rate from 12.3 frames per second to 15.3 frames per second.The ShuffleNetV2_FTC,combined with machine vision technology,can effec-tively identify the grades of flue-cured tobacco,laying the foundation for optimizing the procurement,drying and processing processes of flue-cured tobacco.
万方数据
维普
