Enhanced Medicinal Plant Identification and Classification Using Vision Transformer Model

Identification and classification of medicinal plants constitute the establishments of any progression in pharmacology and the resultant utilization of homegrown medication. Customarily the result prospects are inconstant, time consuming and inclined to mistakes and irregularities owing to the capriciousness of allotted staff, this work presents a modern progressive approach for consequently distinguishing and classifying restorative plants based on a vision transformer demonstration recognized as ViT. This work with the therapeutic plant’s dataset comprising high-resolution pictures of different restorative plants extension strategies of the dataset with turn flipping and color adjustment move forward generalization and strength in the show, the application of self-attention by the instrument of vit, exceptionally increment the model’s chance of finding complex highlights at different scales incredibly upgrading the quality of classification execution in numerous ponders. The vit show was compared with the conventional CNN in any case there are a few works that assess execution by F1-score precision and recall a testing, it comes about showing up promising with a categorization rate of more than 95% of the work will be intrigued by the ponded of home grown pharmaceutical and its applications in healthcare along with an adaptable way of distinguishing restorative plants.