A Group Labelled Classification Model for Accurate Medical Plant Detection Used in Drug Preparation

The use of medical plants in the preparation of medicines has been increased in recent years. Medical plants are an essential component in the production of medicinal products. Medicines are made from root powder or plant leaves. When the herbal medicine is reduced to powder, more experience is required to determine the medicinal product through pharmacognoses. Inaccurate medical plants can cause patients serious health problems. For standardization and quality control of medical drugs the correct identification of the powder shape of medical plants is important. Medical plants are currently classified using a chemical leaf-based assessment, physical assessment and biological assessment. In medicine industry it is extremely necessary to identify the right medicinal plants for the preparation of a medicine. Its leaves form, color and texture are the key features needed to recognize a medicinal plant. In hierarchical clustering technology, the coefficient of inconsistency is used to generate natural clusters. Intra class differences can be seen with the amount of clusters obtained for plant organisms. The aggregate of the corresponding vectors of each sample of a cluster is calculated for one cluster representation. In terms of its leaf samples, therefore, the multiple members of the valued interval type are used to represent the plants in an effective way. The proposed model performs classification of leaf features using group labelled clustering model and then perform locking of labelling. This paper considers a Group Labelled Classification (GLC) Model that examines feature on the front and back of a green leaf, along with morphological characteristics, to achieve a specific optimal combination of features that optimize the recognition rate. The proposed model efficiently extracts the relevant features only from the medical leaf for accurate medical leaf detection. The proposed model is compared with the traditional methods and the results show that the proposed model performance is better.