中国农学通报2025，Vol.41Issue(2)：109-116,8.
基于迁移学习ResNet-18的水稻病虫害识别研究
Research on Identification of Rice Disease and Pest Based on Transfer Learning and ResNet-18
摘要
Abstract
The study aims to improve the automatic recognition of rice pest and disease images andi better guide agricultural pest and disease control.Using a combination of transfer learning and ResNet-18 model,we organized open source plant disease data on the internet,and obtained images of 9 rice pests and diseases,including bacterial blight,blast and Tungro,as well as a healthy leaf as the research objects.11414 cleaned images were selected to establish a dataset for model training,and the 30％dataset was split as the test set.On the basis of six pre trained models such as ResNet-18,GoogLeNet,VGG-16,and MobileNet-v2,a series of parameter adjustments were made to the transfer model.The results show that:(1)under the consistent training parameters,the proposed model ResNet-18 has significantly higher validation accuracy and lowest loss value compared with MobileNet-v2,AxeNet,VGG-16,GoogLeNet,SqueezeNet,and the original ResNet-18 model.The final accuracy of the model is 96.97％.(2)Compared with the original model,the training accuracy of all transferred learning models has been improved significantly,with the improved accuracy ranging from 5.03％to 13.90％.The optimized training model has the characteristics of fast recognition speed and improved accuracy,which can accurately and quickly identify the type of crop disease,providing support for the automatic diagnosis of crop diseases.关键词
水稻/深度学习/病虫害/迁移学习/ResNet-18/图像识别Key words
rice/deep learning/diseases and pests/transfer learning/ResNet-18/image recognition分类
农业科技引用本文复制引用
张志从,崔东,郭金锋,吾木提·艾山江,李亮..基于迁移学习ResNet-18的水稻病虫害识别研究[J].中国农学通报,2025,41(2):109-116,8.基金项目
伊犁师范大学科研项目"伊犁河谷稻田土壤微生物群落地理分布及其驱动因子研究"(2022YSYY003) （2022YSYY003）
伊犁哈萨克自治州科技计划项目(YJC2024A05). （YJC2024A05）
