山东农业大学学报（自然科学版）2025，Vol.56Issue(1)：49-57,9.DOI:10.3969/j.issn.1000-2324.2025.01.006
基于混合注意力机制的香菇菌棒成熟度分级研究
Maturity Grading of Shiitake Mushroom Logs Based on Bottleneck Attention Module
摘要
Abstract
Shiitake mushroom is the most important edible mushroom species in China,and realizing accurate grading of maturity of shiitake mushroom sticks is a prerequisite for improving shiitake yield.In this paper,a maturity grading model of shiitake mushroom sticks based on hybrid attention mechanism is proposed with shiitake mushroom sticks as the research object.Firstly,based on the captured maturity images of shiitake mushroom sticks,the data enhancement of shiitake mushroom stick images is carried out by DCGAN model to learn the feature distribution of the maturity images of shiitake mushroom sticks at each stage,and the maturity dataset of shiitake mushroom sticks is constructed.The hybrid attention module BAM is added to the ResNeXt network using grouped convolution to generate effective feelings and improve the grading accuracy by adaptively adjusting the focus of feature attention.In the section of comparative experimental analysis,the effects of data enhancement methods on the grading model are first evaluated,and the results show that the dataset constructed by the DCGAN model in this paper has stronger robustness in grading,and then the effects of two optimizers,SGD and Adam,as well as the effects of different initial learning rates on the model are comparatively analyzed,and the Adam optimizer and the learning rate of 0.001 are selected as the proposed The parameter settings of the BAM-ResNeXt model,and finally the model was compared with ResNeXt,VGG-16,and ResNet-50 in the mushroom stick maturity dataset in a comparison experiment,and the accuracy,precision,and recall of the BAM-ResNeXt model were 97.88%,94.26%,and 97.45%,which were better than the above models,and the experimental The results show that the BAM-ResNeXt model proposed in this paper has high effectiveness in grading the maturity of shiitake mushroom sticks.关键词
香菇菌棒/成熟度分级/深度学习/注意力机制/生成式对抗网络Key words
Shiitake mushroom sticks/maturity grading/deep learning/attention module/generative adversarial network分类
农业科学引用本文复制引用
王鲁,王明振,吴秋兰..基于混合注意力机制的香菇菌棒成熟度分级研究[J].山东农业大学学报（自然科学版）,2025,56(1):49-57,9.基金项目
山东省重点研发计划(重大科技创新工程)项目(2022CXGC010609) （重大科技创新工程）
