Identification of Different Medicinal Plants using ML

The use of medicinal plants has been a longstanding practice in traditional medicine worldwide. Accurately identifying medicinal plants is crucial for determining their medicinal properties and potential applications. However, it can be a challenging task due to the complexity of their appearance. Identifying plants through their leaves is a thoroughly pursued endeavor that has widely varying applications ranging from ecology, horticulture, disease identification, rare plant preservation in plants to medicinal applications in Ayurveda and various plant bases medical systems. Our purpose in this project is to identify plant species using the image of a single leaf through neural networks. We will approach our project using filtering methods, Random forest algorithm, Tensorflow, and Convolutional Neural Networks. The proposed approach can significantly impact the field of herbal medicine research, enabling researchers and healthcare professionals to identify and classify medicinal plants more accurately. The identification of different medicinal plants using Machine Learning (ML) techniques is a crucial and interdisciplinary field that holds promise for applications in healthcare, botany, and conservation. This research focuses on the development of ML models and systems designed to classify, catalog, and analyze various medicinal plant species, driven by the pressing need to harness the potential of botanical resources for medicinal purposes and preserve biodiversity.By leveraging ML algorithms, image recognition, and data analysis, this work aims of our project.