DeepPHSI: attention-driven CNN-LSTM fusion for hyperspectral origin traceability across Pogostemon cablin batches

Abstract

Pogostemon cablin (P. cablin) is rich in chemical compounds and is extensively utilized in the medicine, food, and fragrance industries. However, factors such as variety, regional ecology, growth conditions, harvest time, and processing methods result in differences in the traits and quality of P. cablin from different origins. The traditional labor-intensive identification methods require a lot of manpower and material resources, and the accuracy of identification is also affected by individual subjectivity. In this study, a deep learning network based on a pixel-level hyperspectral image was constructed to identify P. cablin from different origins, named DeepPHSI. DeepPHSI can be used to distinguish between the three main origins of P. cablin and their stems and leaves from the background. The DeepPHSI model was designed based on convolutional neural and long short-term memory networks. The hyperspectral image data collected under two experimental conditions were used for training and fine-tuning, respectively. Results showed that DeepPHSI can accurately identify the origin of P. cablin under different experimental conditions with transfer learning. The prediction based on DeepPHSI also enabled the fully automated identification of origins and parts, which makes the model suitable for the rapid analysis of large-scale samples. These advantages make DeepPHSI a promising method in hyperspectral applications.