河南农业科学2023，Vol.52Issue(12)：162-171,10.DOI:10.15933/j.cnki.1004-3268.2023.12.018
基于迁移学习和残差网络的谷子病害识别研究
Research on Millet Disease Identification Based on Transfer Learning and Residual Network
摘要
Abstract
A method of millet disease image recognition based on transfer learning and residual network(Residual CNN)was proposed for millet disease.First,the original sample set was established,which was composed of four kinds of disease images including millet white disease,blast,red leaf disease,rust disease and normal millet leaf image.Then,the original image was segmented by using the maximum inter-class variance method based on super green feature,the millet disease segmentation image dataset was established,and the dataset was extended.Finally,based on the expanded segmentation image data set of millet disease,the recognition model of millet disease was established by using the idea of transfer learning and residual network.The results showed that the recognition rate of this model reached 98.2% ,which was 8.9 percentage points higher than that of the support vector machine(SVM)based millet disease recognition model,and the training time of this model was reduced by 17.69 min compared with that of the convolutional neural network(CNN)based millet disease recognition model.The results indicated that the recognition model of millet disease based on transfer learning and residual network could effectively identify the four kinds of millet leaf diseases.关键词
谷子/病害识别/图像处理/计算机视觉/迁移学习/残差网络Key words
Millet/Disease identification/Image processing/Computer vision/Transfer learning/Residual network分类
农业科技引用本文复制引用
张红涛,罗一铭,谭联,杨加蓬,王宇..基于迁移学习和残差网络的谷子病害识别研究[J].河南农业科学,2023,52(12):162-171,10.基金项目
国家自然科学基金项目(31671580) （31671580）
河南省重点研发与推广专项(232102110265) （232102110265）
