# Research on the Classification of Sun-Dried Wild Ginseng Based on an Improved ResNeXt50 Model
## 1 Introduction
introduced MobileNets, a lightweight deep neural network based on a streamlined architecture using depthwise separable convolutions. They introduced two simple global hyperparameters for practical trade-offs between latency and accuracy, maintaining strong performance compared to other ImageNet classification models at the time [9]. [PERSON] et al. improved ResNet with the concept of grouped convolutions, resulting in the iterative convolutional neural network ResNeXt, which uses fewer parameters and achieves lower error rates with the same computational load, yielding better results in image classification [10]. [PERSON] et al. adapted the final fully connected layer and output neurons of EfficientNetB4 to differentiate various chili leaf diseases meticulously, employing data preparation techniques like scaling, pixel normalization, and augmentation to enhance model resilience, enabling the model to recognize image variations in chili leaves due to light, angles, and disease severity, achieving an improved average accuracy of 91.2%, demonstrating deep learning's potential in addressing specific agricultural issues and extending its applications to different crops [11]. In 2023, [PERSON] et al. used AlexNet as a base model to study 10 types of medicinal herbs, employing ridge regression and transfer learning to effectively alleviate overfitting and analyze multiple common data, achieving a high recognition accuracy of 95.4% [12]. [PERSON] et al. improved the DenseNet-201 model, using 50 types of medicinal slices as a dataset [13]. To prevent overfitting due to excessive parameters in the fully connected layer, they used dropout, randomly setting 50% of the elements, enriching feature learning diversity, enhancing each feature's contribution to model prediction, and strengthening the regularization effect during training. [PERSON] et al. proposed a method for identifying medicinal materials based on an improved TCM-Net, introducing an attention mechanism and improved mobile inverted bottleneck convolution modules, ensuring network lightness while significantly enhancing the accuracy of medicinal material recognition [14]. [PERSON] et al. used SE-MobileNetV2 as a base model, employing the Momentum optimization algorithm and ReLU6 activation function, training the network model for the recognition task of traditional Chinese medicine powder microscopic identification images [15]. Experimental results showed an overall recognition accuracy of 97.5% in the eight tested types of medicinal powder microscopic identification images. Based on previous experiments and literature review, we determined that convolutional neural networks can classify and identify medicinal materials, addressing issues such as excessive reliance on manual classification and traditional methods' relatively low accuracy and slow classification speed. Considering computational load, parameter count, and model size, the ResNeXt50 model was chosen as the base model. This experiment designed an improved ResNeXt50 model to achieve fast and accurate classification of sun-dried wild ginseng grades. The critical improvement involves replacing the three-layer convolution in the Bottleneck structure with the Ghost module, reducing computational load and increasing classification efficiency while ensuring accuracy. Additionally, the SE (Squeeze and Excitation) attention mechanism was used to highlight essential features of sun-dried wild ginseng and suppress irrelevant or unimportant features [16]. The ELU (Exponential Linear Unit) activation function was employed to extract finer-grained features, enhancing the overall recognition accuracy of the model and thereby addressing the challenging problem of classifying sun-dried wild ginseng [17].