Abstract :

Medical flower classification using deep learning refers to automatically identifying and categorizing medicinal flowers from images using neural networks. This approach leverages convolutional neural networks (CNNs) to extract features and distinguish between species based on visual patterns. Accurate identification has applications in herbal medicine recognition, biodiversity monitoring, and agriculture. Traditional methods relying on manual classification are time-consuming and prone to human error. Deep learning models trained on labeled flower images can achieve high precision and scalability. The research explores dataset preparation, model architecture selection, training strategies, and evaluation metrics. A custom dataset of medicinal flowers was used to train models such as ResNet and MobileNet. The system’s performance is evaluated using accuracy, loss curves, and confusion matrices. Results demonstrate the feasibility of deep learning for reliable medical flower classific