首页|基于卷积神经网络和叶绿素荧光成像的绿豆叶斑病识别研究

基于卷积神经网络和叶绿素荧光成像的绿豆叶斑病识别研究

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
为了解决绿豆叶斑病不同病害等级之间容易混淆的问题，本研究以感染不同程度叶斑病的绿豆叶片叶绿素荧光图像为研究对象，提出了多模块串联卷积神经网络(Multi-Module Sequential Convolutional Neural Network，MMS-Net)模型。该模型主要由本研究搭建的Sub模块和Wave模块串联堆叠组成，并且在每个Sub模块中和每个Wave模块结尾处加入混合注意力机制CBAM，在减少非叶斑病特征干扰的同时，对相似病斑进行更为细致的特征提取，提高病害识别的准确率。在相同条件下，与经典的卷积神经网络模型(VGG16、GoogLeNet、ResNet50)以及流行的轻量级卷积神经网络模型(MobileNetV2、MobileNeXt、MobileNetV3、Shuffle-NetV2)进行比较，本研究提出的MMS-Net模型参数量仅为11。43 M，测试准确率达到91。25％，均高于其他模型，分类效果最优。通过分析精度、召回率、Fl分数等评价指标可以看出MMS-Net模型具有较好的鲁棒性和泛化能力。本研究结果可为绿豆等作物的抗病种质资源识别和筛选提供新思路。
Identification of Mung Bean Leaf Spot Disease Based on Convolutional Neural Network and Chlorophyll Fluorescence Imaging
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.

Mung bean leaf spotDisease degreeConvolutional neural networkChlorophyll fluores-cence imagingAttention mechanism

张浩淼、高尚兵、蒋东山、李洁、袁星星、陈新、刘金洋

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淮阴工学院计算机与软件工程学院,江苏淮安 223001

江苏省农业科学院经济作物研究所,江苏南京 210014

绿豆叶斑病 病害等级 卷积神经网络 叶绿素荧光成像 注意力机制

国家自然科学基金面上项目科技部重点研发政府间国际合作项目江苏省一带一路国际合作项目江苏省种业揭榜挂帅项目江苏省研究生科研与实践创新计划项目

620761072019YFE0109100BZ2022005JBGS[2021]004SJCX24_2145

2024

10.14083/j.issn.1001-4942.2024.09.018

山东农业科学
山东省农业科学院,山东农学会,山东农业大学

山东农业科学

CSTPCD北大核心
影响因子：0.578
ISSN：1001-4942
年,卷(期)：2024.56(9)