Review of Medicinal Plant classification System

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Previous studies in the fields of botanical sciences and medicine have looked closely at the categorization and identification of therapeutic plants. In the past, scholars have delved into conventional knowledge bases with the goal of deciphering the botanical characteristics and medicinal applications of many plant species, establishing the foundation for contemporary research. Nevertheless, distinguishing and categorizing a wide range of medicinal plant species proved to be a significant obstacle for studies. The challenge was precisely identifying and classifying the subtle and unique characteristics shared by various plant species. These obstacles impeded the creation of thorough and accurate classification schemes designed especially for medicinal plants. Our suggested Medicinal Plant Classification System uses a novel strategy that makes use of convolutional neural networks (CNNs) to overcome these difficulties. The CNN-based method greatly improves our classification system's accuracy and versatility while making it easier to identify subtle botanical features. For our work, CNNs seem to be an essential answer, demonstrating unmatched effectiveness in capturing various complex properties present in photos of medicinal plants. The flexibility, capacity for feature extraction, and hierarchical learning that CNNs possess highlight their critical role in creating a strong and adaptable classification system designed especially for the identification of medicinal plants.