MEDICAL PLANTS IDENTIFICATION USING MACHINE LEARNING

The identification of medicinal plants plays a crucial role in pharmacology, herbal medicine, and biodiversity conservation. Traditional methods of plant identification rely on manual observation and expert knowledge, which are time-consuming and prone to human error. In this study, we explore the application of machine learning techniques to automate and enhance the accuracy of medicinal plant identification. Leveraging image processing, feature extraction, and supervised learning algorithms, the proposed system analyzes plant characteristics such as leaf shape, texture, color,and venation patterns. We utilize a diverse dataset of labeled medicinal plant images and implement models like Convolutional Neural Networks (CNNs) for feature recognition and classification. Additionally, transfer learning techniques are employed to optimize model performance with limited training data. Experimental results demonstrate high accuracy in identifying medicinal plant species, with the model achieving robustness across various environmental conditions and imaging scenarios.