基于VGG16-DenseNet集成模型的烤烟智能分级
Intelligent Grading of Flue-cured Tobacco Based on VGG16-DenseNet Integrated Model
黄本荣 1范兆烽 1王飞 1江逸昕 1马祥根 1肖光林 1詹德良 1吴善建 1黄嘉星 2温永仙2
作者信息
- 1. 福建省烟草公司三明市公司,福建三明 365000
- 2. 福建农林大学计算机与信息学院,福州 350002
- 折叠
摘要
为实现烤烟烟叶等级快速、准确的智能化识别,本研究基于手机拍摄的不同品种烤烟烟叶正、反面图像,构建了VGG16与DenseNet组合的新网络模型VGG16-Dense,并应用手机拍摄的翠碧1号、云烟87烤烟烟叶6个等级正反面图片,总共 24类,验证该模型的有效性,同时与 5个网络模型DenseNet121、ResNet50、AlexNet、VGG16和GoogLeNet进行比较.研究表明:VGG16-Dense网络模型在验证集的各评估指标(准确率、精确率、召回率、F1分数和平均损失值)均达到优秀值,在测试集的各评估指标较其他模型是最优的,准确率为 92.71%,精确率为 93.07%,召回率为 92.71%,F1分数为92.72%,平均损失值为 0.22,有较好的泛化能力,错判较少.VGG16-Dense网络模型能同时智能判别烤烟烟叶等级及其正反面,甄别不同品种,这为初级烤烟收购中的定级实现智能化提供理论指导.
Abstract
Aiming to achieve intelligent recognition of flue-cured tobacco leaves grade quickly and accurately,the images of front and back tobacco leaves were taken by mobile phone,and a new model(VGG16-Dense)integrated with VGG16 and DenseNet was constructed.The validity of the model was verified by using twenty-four types of front and back leaf images of cv.Cuibi-1 and Yunyan87.The model was also compared with five other network models,i.e.DenseNet121,ResNet50,AlexNet,VGG16 and GoogLeNet.The results shows that excellent values appeared in all evaluation indicators(accuracy,precision,recall,F1-score and avg-loss)of validation set for VGG16-Dense,and the evaluation indicators of test set for VGG16-Dense performed optimal compared with that of other network models.VGG16-Dense exhibited superior generalization ability and fewer misjudgments,with the accuracy,precision recall,F1-score,and avg-loss reaching 92.71%,93.07%,92.71%,92.72%,and 0.22,respectively.VGG16-DENSE network model can intelligently distinguish the grade,the front and back,and the species for flue-cured tobacco leaves at the same time.This provides a theoretical guidance for the intelligentized grading of primary flue-cured tobacco acquisition.
关键词
烤烟智能分级/深度学习/组合网络模型/SE模块Key words
intelligent grading of flue-cured tobacco/deep learning/integrated with network model/SE module引用本文复制引用
基金项目
中国烟草总公司福建省公司科技项目(2022350000240064)
出版年
2024
国家科技期刊平台
NSTL
万方数据