Jung, Dae-Hyun Kim, Ho-Youn Won, Jae Hee Park, Soo Hyun 2024-01-19T09:31:02Z 2024-01-19T09:31:02Z 2023-06-29 2023-06 1664-462X https://pubs.kist.re.kr/handle/201004/113656 Cynanchum wilfordii is a perennial tuberous root in the Asclepiadaceae family that has long been used medicinally. Although C. wilfordii is distinct in origin and content from Cynancum auriculatum, a genus of the same species, it is difficult for the public to recognize because the ripe fruit and root are remarkably similar. In this study, images were collected to categorize C. wilfordii and C. auriculatum, which were then processed and input into a deep-learning classification model to corroborate the results. By obtaining 200 photographs of each of the two cross sections of each medicinal material, approximately 800 images were employed, and approximately 3200 images were used to construct a deep-learning classification model via image augmentation. For the classification, the structures of Inception-ResNet and VGGnet-19 among convolutional neural network (CNN) models were used, with Inception-ResNet outperforming VGGnet-19 in terms of performance and learning speed. The validation set confirmed a strong classification performance of approximately 0.862. Furthermore, explanatory properties were added to the deep-learning model using local interpretable model-agnostic explanation (LIME), and the suitability of the LIME domain was assessed using cross-validation in both situations. Thus, artificial intelligence may be used as an auxiliary metric in the sensory evaluation of medicinal materials in future, owing to its explanatory ability. English Frontiers Media S.A. Development of a classification model for Cynanchum wilfordii and Cynanchum auriculatum using convolutional neural network and local interpretable model-agnostic explanation technology Article 10.3389/fpls.2023.1169709 1 Frontiers in Plant Science, v.14 Frontiers in Plant Science 14 Y scie scopus 001008508600001 2-s2.0-85162062645 Plant Sciences Plant Sciences Article C-21 STEROIDAL GLYCOSIDES MOLECULAR MARKERS IDENTIFICATION explainable artificial intelligence image classification Incpetion-ResNet sensory evaluation analysis image classification algorithm