Electronics, Vol. 13, Pages 4504: Research on the Quality Grading Method of Ginseng with Improved DenseNet121 Model

Electronics, Vol. 13, Pages 4504: Research on the Quality Grading Method of Ginseng with Improved DenseNet121 Model

Electronics doi: 10.3390/electronics13224504

Authors: Jinlong Gu Zhiyi Li Lijuan Zhang Yingying Yin Yan Lv Yue Yu Dongming Li

Ginseng is an important medicinal plant widely used in traditional Chinese medicine. Traditional methods for evaluating the visual quality of ginseng have limitations. This study presents a new method for grading ginseng’s appearance quality using an improved DenseNet121 model. We enhance the network’s capability to recognize various channel features by integrating a CA (Coordinate Attention) mechanism. We also use grouped convolution instead of standard convolution in dense layers to lower the number of model parameters and improve efficiency. Additionally, we substitute the ReLU (Rectified Linear Unit) activation function with the ELU (Exponential Linear Unit) activation function, which reduces the problem of neuron death related to ReLU and increases the number of active neurons. We compared several network models, including DenseNet121, ResNet50, ResNet101, GoogleNet, and InceptionV3, to evaluate their performance against our method. Results showed that the improved DenseNet121 model reached an accuracy of 95.5% on the test set, demonstrating high reliability. This finding provides valuable support for the field of ginseng grading.