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.

Plants are essential for human life. They help people breathe, provide food, clothing, medicine, and fuel, and also safeguard the environment. Plants can be loaded with medicinal properties and possess active substances that can be used for medical purposes. Several beneficial plant species are disappearing as a result of such factors as global warming, increasing population, professional secrecy, insufficient government support for research efforts, and the lack of public understanding of medicinal plants. It takes time to identify medicinal plants, therefore use professionals to assist you. For better benefit to humankind, a new method to identify and classify therapeutic plants must be developed. Because of the advanced technology in our day and age, medicinal plant identification and classification is an important subject of research in the field of image processing. Feature extraction and classification are the most important components in the process of identifying medicinal plants and classifying them. This research examines methods used in identifying and classifying medicinal plants as well as the medicinal properties of plants that have become increasingly relevant in the recent past. There is a vital importance placed on identifying the suitable medicinal plants in the creation of an ayurvedic medication. In order to identify a medicinal plant, look for these three features: leaf form, colour, and texture. From the both sides of the leaf, there are both deterministic and nondeterministic factors that identify the species. In this study, a combination of traits is designed that is said to identify a single tree the most effectively while minimising errors. The database is made up of scanned photos of both the front and back side of ayurvedic medicinal plant leaves, which is an ayurvedic medicinal plant identification database. In leaf identification, rates as high as 99% have been found when tested on a wide range of classifiers. Extending the prior work by using dried leaves and feature vectors results in identification using which identification rates of 94% are possible. Identification of the correct medicinal plants that goes in to the preparation of a medicine is very important in ayurvedic medicinal industry. The main features required to identify a medicinal plant is its leaf shape, colour and texture. Colour and texture from both sides of the leaf contain deterministic parameters to identify the species. This paper explores feature vectors from both the front and back side of a green leaf along with morphological features to arrive at a unique optimum combination of features that maximizes the identification rate. A database of medicinal plant leaves is created from scanned images of front and back side of leaves of commonly used ayurvedic medicinal plants. The leaves are classified based on the unique feature combination. Identification rates up to 99% have been obtained when tested over a wide spectrum of classifiers. The above work has been extended to include identification by dry leaves and a combination of feature vectors is obtained, using which, identification rates exceeding 94% have been achieved.

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