Tobacco leaves maturity recognition device based on CNN-Swin transformer model

The maturity of fresh tobacco leaves has a significant impact on the drying process of tobacco leaves, and it is very important to quickly and accurately identify the maturity of tobacco leaves. Machine vision technology provides a new idea for tobacco leaf maturity recognition. According to the differences in color and texture of tobacco leaf images, it is possible to identify the maturity of tobacco leaf. In order to improve the accuracy and convenience of fresh tobacco maturity recognition, a tobacco maturity recognition device is designed. The device uses the deep learning model based on CNN-Swin Transformer and the stacking model fusion method to obtain new features, and the tobacco maturity is recognized by the new features, with an accuracy of 93.9%. In addition, a matching maturity recognition system is designed and applied to a tobacco planting base.