基于轻量级神经网络MobileNetV3-large的黄茶闷黄程度判别
Identification of yellowing degree of yellow tea based on lightweight neural network MobileNetV3-large
葛炳钢 1张旭雯 1刘岁 1杨亚 1周铁军 2傅冬和3
作者信息
- 1. 茶学教育部重点实验室,湖南 长沙 410128;国家植物功能成分利用工程技术研究中心,湖南 长沙 410128
- 2. 湖南农业大学信息与智能科学技术学院,湖南 长沙 410128
- 3. 茶学教育部重点实验室,湖南 长沙 410128;国家植物功能成分利用工程技术研究中心,湖南 长沙 410128;植物功能成分利用省部(教育部)共建协同创新中心,湖南 长沙 410128
- 折叠
摘要
以碧香早品种为材料,通过相机采集不同闷黄时长下的闷黄叶图像共 675 张,建立了 3 种闷黄程度的黄茶样本数据集,采用位置变换、随机亮度、增加对比度、添加噪声、随机缩放操作对闷黄叶图像集进行数据增强,运用迁移学习方法,在ImageNet数据集取得MobileNetV3-Large的预训练模型,对迁移网络的所有权重信息进行训练,最终建立了针对黄茶闷黄程度的轻量级卷积神经网络 MobileNetV3-Large 识别模型,并利用Grad-CAM热力图可视化和置信分数监控黄茶品质的变化.结果表明:经训练后的MobileNetV3-Large模型测试的识别准确率达到 98.51%,精确率为 99.10%,召回率为 98.93%,加权分数为 98.20%;MobileNetV3-Large模型的识别准确率高于传统机器学习模型 SVM、XGBoost 和 KNN;通过 Grad-CAM 热力图可视化显示,MobileNetV3-Large 模型在不同的识别场景下能够准确定位并提取闷黄叶特征,准确地识别闷黄程度.可见,MobileNetV3-Large模型有较好的泛化性,可以快速、无损地识别黄茶的闷黄程度.
Abstract
In this study,three dataset of yellow tea sample data were collected from a total of 675 Bixiangzao variety tea images of yellowing leaves at different stage.Position transformation,random brightness,increasing contrast,adding noise,and random scaling operations were used to enhance the data.The pre-training model of MobileNetV3-Large was obtained from the ImageNet data sets using transfer learning method,and ownership weight information of the network was transferred for training,then the lightweight convolutional neural network MobileNetV3-Large recognition model for yellowing degree of yellow tea was established.Grad-CAM(Gradient-weighted class activation mapping)heat map visualization and confidence scores were used to monitor the changes in yellow tea quality.The results of using the model to estimate the teas showed that the trained MobileNetV3-Large model achieves recognition accuracy of 98.51%,precision of 99.10%,recall of 98.93%and F1-score of 98.20%.The recognition accuracy of MobileNetV3-Large model was higher than the traditional machine learning models SVM(Support vector machine),XGBoost(eXtreme gradient boosting)and KNN(K-Nearest neighbors).As visualized by Grad-CAM heat map,the MobileNetV3-Large model was able to accurately locate and extract yellowing leaf features in different recognition scenarios,and accurately identify the yellowing degree.In conclustion,the MobileNetV3-Large model had better generalization and could rapidly and nondestructively identify the yellowing degree of yellow tea.
关键词
黄茶/闷黄程度识别/卷积神经网络/迁移学习Key words
yellow tea/identification of yellowing degree/convolutional neural network/transfer learning引用本文复制引用
基金项目
湖南省创新型省份建设专项(2021NK1020)
国家"十四五"重点研发计划项目(2022YFD2101102)
湖南省科技重点研发项目(2018NK2035)
湖南农业大学研究生科研创新项目(2022XC064)
出版年
2024
国家科技期刊平台
NSTL
万方数据