A Machine Learning Approach To Classify Medicinal Plant Leaf By Using Random Forest And KNN

Abstract

Numerous plant and herb types partake remained cast-off broadly in traditional medicine due to their medicinal properties and cost-effectiveness compared to modern prescription drugs. Nevertheless, accurately identifying medicinal plants poses a significant challenge in the realm of machine learning. This project leverages a comprehensive dataset of leaf images from various medicinal plants to train and evaluate a classification model.The primary impartial of this explore is to develop an advanced ML model capable of accurately identifying different Indian medicinal plants. By focusing on several vulnerable species, the study employs the Random Forest and K-Nearest Neighbors algorithms to classify the plants. The KNN algorithm achieved an accuracy of 84.33%, while the Random Forest algorithm outperformed it with an accuracy of 85.82%, demonstrating its superior effectiveness.Beyond mere identification, the project explores the medicinal properties & aids related with individually recognized plant. Given the visual similarities among many plant species, classification is predominantly stimulating, expressly when commerce with extensive datasets. This research aims to bridge the gap between traditional botanical knowledge and modern technological advancements, thereby contributing to the preservation and dissemination of valuable medicinal information.By utilizing a Random Forest-based predictive model, this structure not only aids in the accurate identification of medicinal plants but also supports the conservation of ancient and traditional medicinal knowledge. This integration of historical wisdom with contemporary machine learning techniques ensures that valuable botanical information is preserved and made accessible for future generations