Investigation of Deep Learning Techniques Used in Medicinal Plants Identification and Classification

There is a global dependence on medicinal plants for the treatment of diverse health conditions, particularly in developing countries, where government medical facilities are inadequate. Conventional machine learning techniques and traditional methodologies are inadequate for rapid, accurate, and reliable identification and classification of medicinal plants. This has paved the way for deep learning(DL) models to identify and classify medicinal plants. This review focuses on the following questions: 1) Which deep learning models were used for medicinal plant identification and classification and how did they perform? 2) Did hybrid models perform better than ensemble models? 3) What common datasets are used for deep learning-based medicinal plant identification and classification studies? 4) Which data augmentation techniques were prevalent in medicinal plant identification and classification studies? 5) Which performance metrics are frequently used to evaluate medicinal plant identification models? 6) What data splitting ratios are commonly used? 7) Was transfer learning prevalent in medicinal plant identification and classification studies? This review complements the limited literature on medicinal plant identification and classification using deep learning. This study analyzes gaps in the existing literature and provides recommendations for future research. This will enhance the development of robust deep learning models to identify and classify medicinal plants for indigenous knowledge preservation, biodiversity monitoring, and their proper use in pharmaceutical processes.