农学学报2026，Vol.16Issue(1)：57-64,8.
基于Resnet与支持向量机融合识别网络的鲜烟叶部位识别
Position Identification of Fresh Tobacco Leaf Based on Resnet and Support Vector Machine Fusion Recognition Network
摘要
Abstract
This study aims to achieve digital recognition of fresh tobacco leaf harvesting positions and meet the demand for rapid,non-destructive identification in intelligent curing.A hybrid network model(R-SVM)integrating Resnet-50 and support vector machine(SVM)is proposed for fresh tobacco leaf position recognition.Based on the features of different convolutional layers(layers 1,10,22,40,49)of fresh tobacco leaf images extracted by the pre-trained Resnet-50 network model,combined with different pooling methods[average pooling(AVP),global average pooling(GAP)and spatial pyramid pooling(SPP)]and dimensionality reduction algorithms[principal component analysis(PCA)and ReliefF],support vector machines(SVM)were trained respectively and different recognition models of fresh tobacco leaf harvesting positions were screened out,and then different model fusion strategies(hard voting,soft voting,Stacking method)were used to obtain the final recognition model of fresh tobacco leaf position.The results indicated that different pooling methods exhibited distinct impacts on model performance.In low-level convolution layers,SPP pooling significantly improved model accuracy by over 10%,while its effect was minimal on models trained using features from high-level convolution layers.PCA dimensionality reduction effectively enhanced recognition performance across all convolutional layers.The 40th layer output model in different convolution layers had the highest accuracy rate on the test set,which was 92.12%.The model obtained by the Stacking fusion method had the best performance,and the accuracy rate on the test set was 96.83%.The fusion recognition model for fresh tobacco leaf position established in this study can achieve accurate and non-destructive identification of tobacco leaf positions.关键词
残差网络/支持向量机/模型融合/鲜烟叶部位/分类Key words
residual network/support vector machine/model fusion/fresh tobacco leaves position/classification分类
农业科技引用本文复制引用
李昌根,李珂,赵东方,张帅,孟祥宇,林勇,魏硕,王廷贤..基于Resnet与支持向量机融合识别网络的鲜烟叶部位识别[J].农学学报,2026,16(1):57-64,8.基金项目
福建省烟草公司南平市公司资助项目"南平烟区翠碧一号烘烤工艺数字化基础研究"(NYK2023-03-03) （NYK2023-03-03）
中国烟草总公司科技重点研发项目"基于图像精准识别的烟叶智能烘烤关键技术研究与应用"(110202102007). （110202102007）
