This invention provides a method and
system for grouping
flue-cured tobacco using feature channel weighting and dynamic loss control, relating to the fields of
deep learning and
flue-cured tobacco grading. The method involves acquiring images of
flue-cured tobacco from N main groups using an
image acquisition device to establish a flue-cured tobacco grouping dataset, where N is a positive integer. This dataset is then preprocessed to obtain a preprocessed dataset. A flue-cured tobacco grouping classification network (TGNet) is designed and trained on the preprocessed dataset to obtain a flue-cured tobacco grouping classification model. Based on this model, the grouping results are obtained. This invention solves the technical problems of existing
deep learning methods for flue-cured tobacco grouping, such as lack of key feature representation in high-scale features, limited inter-
class discrimination ability, and a tendency for the model to learn from
majority class samples during training. It achieves real-time classification of flue-cured tobacco groups, effectively improving the efficiency of flue-cured tobacco group classification and reducing the cost of manual grading.