Identification of Mung Bean Leaf Spot Disease Based on Convolutional Neural Network and Chlorophyll Fluorescence Imaging
Zhang Haomiao
Gao Shangbing
Jiang Dongshan
Li Jie
Yuan Xingxing
Chen Xin
Liu Jinyang
Abstract：In order to solve the problem of confusion among different disease levels of mung bean leaf spot,a Multi-Module Sequential Convolutional Neural Network(MMS-Net)model was proposed based on chlorophyll fluorescence imaging of mung bean leaves infected by the disease.The model was mainly composed of the Sub modules and Wave modules proposed in this article,and the Convolutional Block Attention Module(CBAM)was added into each Sub module and at the end of each Wave module,which could detect similar disease spot features in more detail and reduce the mixing of non-leaf spot features at the same time,thereby improved the accuracy rate of disease recognition.Under the same conditions,compared with several classic convolutional neural network models(VGG16,GoogLeNet,ResNet50)and popular lightweight convolutional neural network models(MobileNetV2,MobileNeXt,MobileNetv3,ShuffleNetV2),the parameter size of the MMS-Net model was only 11.43 M and the test accuracy was 91.25％,which were higher than those in the other models,so it showed the best classification effect.By analyzing evaluation indicators such as precision,recall rate and Fl-score,it was concluded that the MMS-Net model exhibited better robustness and generaliza-tion ability,which could provide new ideas for screening disease-resistant germplasm resources of mung bean and other crops.
Keywords：Mung bean leaf spotDisease degreeConvolutional neural networkChlorophyll fluores-cence imagingAttention mechanism
Publication Date：2024-09-28
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：9( 133-141 )