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基于SimAM-ConvNeXt-FL的茶叶病害小样本分类方法研究

田甜 程志友 鞠薇 张帅

农业机械学报2024,Vol.55Issue(3):275-281,7.
农业机械学报2024,Vol.55Issue(3):275-281,7.DOI:10.6041/j.issn.1000-1298.2024.03.027

基于SimAM-ConvNeXt-FL的茶叶病害小样本分类方法研究

Small Sample Classification of Tea Diseases Based on SimAM-ConvNeXt-FL

田甜 1程志友 1鞠薇 1张帅1

作者信息

  • 1. 安徽大学互联网学院,合肥 230039
  • 折叠

摘要

Abstract

In order to realize accurate classification of tea diseases,a disease image classification method based on SimAM-ConvNeXt-FL model of migration learning was proposed to address the small sample problem and uneven distribution of categories in tea disease image classification.Firstly,an SimAM module was added to the ConvNeXt model to enhance the extraction of complex features.Secondly,to address the problem of uneven sample distribution,the Focal Loss function was used as the loss function in the training process,and the effect of uneven sample distribution was reduced by increasing the weights of a smaller number of samples.Finally,the SimAM-ConvNeXt-FL model was used to train the Plant Village dataset,and the parameters obtained from the training were migrated to the measured tea leaf disease images and fine-tuned to reduce the impact of overfitting,and ablation experiments were set up to prove the validity of the model improvement,and comparison experiments were carried out with the different classification models AlexNet,VGG16,and ResNet34 models comparison experiments were conducted respectively.The experimental results showed that the SimAM-ConvNeXt-FL model had the best recognition effect,with an accuracy of 96.48%,and the F1 values of the SimAM-ConvNeXt-FL model compared with the original ConvNeXt model for tea coal disease,tea phoma,tea anthracnose,healthy leaves,and tea white star disease were improved by 4.46 percentage points,3.76 percentage points,0.43 percentage points,0.22 percentage points,and 5.23 percentage points respectively.The results showed that the model proposed had high classification accuracy and strong generalizability,which can promote the development of tea disease classification.

关键词

茶叶病害/图像分类/小样本/迁移学习/ConvNeXt

Key words

tea disease/image classification/small sample/transfer learning/ConvNeXt

分类

计算机与自动化

引用本文复制引用

田甜,程志友,鞠薇,张帅..基于SimAM-ConvNeXt-FL的茶叶病害小样本分类方法研究[J].农业机械学报,2024,55(3):275-281,7.

基金项目

国家自然科学基金项目(61672032) (61672032)

农业机械学报

OA北大核心CSTPCD

1000-1298

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