Real Time Medicinal Plant Identification & Classification Using Random Forest Algorithm

In recognition of their therapeutic qualities, medicinal plants have been used for millennia, greatly influencing traditional medical systems all over the world. But classifying and identifying these plant species by hand can be laborious and prone to mistakes. In this study, we suggest a unique method for identifying and detecting species of medicinal plants using machine learning techniques specifically, Random Forest. As part of the method we used, we gathered a broad dataset with pictures of eighty distinct kinds of medicinal plants. Preprocessing methods are used to standardize characteristics and improve image quality. Next, the Random Forest method is used for classification, taking advantage of its capacity to manage intricate datasets and yield comprehensible outcomes. The outcomes of the experiments show how well the suggested method works for correctly identifying and identifying medicinal plant species. The model demonstrates its promise for practical applications in domains including pharmacology, botany, and traditional medicine by achieving a high level of classification accuracy of 98%. Additionally, because of the Random Forest algorithm's interpretability, researchers may get insights into the factors that influence categorization judgments, which helps them better understand the traits that set distinct plant species apart.