Medico Flower Detection Using Deep Neural Network

The identification and classification of medicinal flowers play a crucial role in healthcare, agriculture, and biodiversity conservation, as these plants often possess significant therapeutic properties. Manual recognition, however, is time-consuming and error-prone due to the similarity in floral structures across species. To address this challenge, this project employs a deep learning-based approach for automated flower classification using Convolutional Neural Networks (CNN). The proposed system is trained on a curated dataset of medicinal flower images and is capable of learning intricate features such as color, texture, and petal shape for accurate classification. Experimental results demonstrate that the model achieves high classification accuracy, reducing the risk of misidentification and improving reliability in real-world applications. This work highlights the potential of CNN-based models to assist researchers, farmers, and practitioners in the fields of botany, Ayurveda, and pharmacology, thereby bridging the gap between technology and traditional medicine.