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.