MikrobatX: The Use of SIFT Feature Extraction in a Deep Learning Approach for Identification and Classification of Microscopic Fragments of Medicinal Leaves

Abstract

Due to a lack of references that contain standard references, it is still difficult to evaluate the accuracy of the raw material for the medicinal plant Simplicia powder based on microscopic testing in the pharmaceutical industry. Furthermore, it takes much time to manually match the findings of microscopic tests with standard reference materials. For these reasons, artificial intelligence must be used so that researchers can rapidly and reliably forecast the kinds of medicinal plants based on microscopic fragments. Deep learning performance in computer vision has demonstrated encouraging outcomes in recent years. Convolutional neural networks (CNN) enhanced by SIFT feature extraction, dubbed "MikrobatX," are used in the proposed work to identify and classify microscopic fragment images of the medicinal plant Simplicia. This technique plays a key role in the microscopic identification and classification of medicinal plant simplicia. Using microscopic photographs of the leaves of medicinal plants, MikrobatX was able to extract essential Simplicia characteristics. Our proposed model may produce the greatest accuracy value of 89.42% for microscopic medicinal leaf Simplicia image problems, according to experimental results utilizing the Mikrobat dataset. Due to the lack of comparable research using the Microbat dataset, these findings cannot be compared to earlier investigations.