农业机械学报2023，Vol.54Issue(z1)：245-251,7.DOI:10.6041/j.issn.1000-1298.2023.S1.026
基于VGG-ST模型的奶牛粪便形态分类方法研究
Cow Manure Classification Method Based on VGG-ST Model
摘要
Abstract
Accurate and rapid identification of cow manure morphology is of great significance for monitoring and precise management of cow gastrointestinal health.In response to the problems of strong artificial dependence and difficulty in identification in current cow manure recognition methods,a method for identifying cow thin,loose,hard,and normal manure was proposed based on the VGG-ST(VGG-Swin Transformer)model.Firstly,a total of 879 images of the four different forms of manures was collected from lactating Holstein cows and augmented to 5 580 images using operations such as flipping and rotation as the dataset.Then,five typical deep learning image classification models,namely Swin Transformer,AlexNet,ResNet-34,ShuffleNet and MobileNet,were selected for cow manure classification research.Through comparative analysis,Swin Transformer was determined to be the optimal base classification model.Finally,the VGG-ST model combined the VGG model with the Swin Transformer model.The VGG model was utilized to capture local features of cow manure,while the Swin Transformer model extracted global self-attention features.After feature concatenation,the cow manure images were classified.The experimental results showed that the Swin Transformer model achieved a classification accuracy of 85.9％on the testing set,which was 1.8 percentage points,4.0 percentage points,12.8 percentage points,and 23.4 percentage points higher than that of ShuffleNet,ResNet-34,MobileNet,and AlexNet,respectively.The classification accuracy of the VGG-ST model was 89.5％,which was 3.6 percentage points higher than that of the original Swin Transformer model.The research result provided a method reference for the development of automatic inspection robots for cow manure morphology.关键词
奶牛/粪便分类/Swin Transformer/深度学习Key words
dairy cow/manure classification/Swin transformer/deep learning分类
信息技术与安全科学引用本文复制引用
纪宝锋,李斌,卫勇,赵文文,周孟创..基于VGG-ST模型的奶牛粪便形态分类方法研究[J].农业机械学报,2023,54(z1):245-251,7.基金项目
国家重点研发计划项目(2022YFD1301103)、河北省重点研发计划项目(22322909D)、北京市农林科学院改革与发展项目和北京市农林科学院智能装备技术研究中心开放项目(KFZN2020W011) （2022YFD1301103）
