<p>Open set recognition (OSR) is the problem that appeared while handling the unknown classes, which do not exist in the training dataset. The existing classifiers assume that only known groups are present in the research field. Conventional open-set classifiers depend on deep learning architectures, which are trained in a supervised manner with known classes in the training set. This creates specialization in the analyzed presentation to known classes and generates complexity to differentiate unknowns from known ones. Moreover, the image classification techniques are commonly provided with small medical leaf image sets, but they do not hold the data about more medicinal plants. Further, in the existing mechanisms, experts in the agricultural fields are used to identify the anomalies in the plants caused by climate conditions, diseases, nutritional deficiencies, and pests. Hence, deep learning-aided medicinal plant classification techniques are used to identify the medicinal plant types accurately, but they provide a poor efficiency rate. Hence, to improve the medical plant variant classification rate, OSR-aided classification techniques are designed. Thus, in this survey, a brief analysis is made of OSR algorithms based on medicinal plant classification for solving the open-set problem, especially in classifying untrained medical plants. This survey conducts an overall review of the recently developed OSR techniques based on machine learning, deep learning, and image processing domains. Also, the datasets, such as private and public datasets, used for existing OSR models, are taken into the review. In addition, the implementation tools are analyzed along with the chronological review of OSR models. Both the merits and demerits of the conventional OSR works are discussed. Consequently, suggestions are provided to figure out the appropriate techniques to obtain better prediction accuracy. Finally, the challenges faced in implementing the OSR models using machine learning and deep learning models are discussed for medicinal plant classification.</p>