Research on Millet Disease Identification Based on Transfer Learning and Residual Network
ZHANG Hongtao
LUO Yiming
TAN Lian
YANG Jiapeng
WANG Yu
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.
Keywords：MilletDisease identificationImage processingComputer visionTransfer learningResidual network
Publication Date：2023-12-15
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：10( 162-171 )