Grayscale Flower Image Classification Using CNN: A Novel Approach to Enhancing Performance and Precision

This project offers a complete solution for automatic flower classification and identification through the use of web-based interfaces and deep learning algorithms. Using a trained convolutional neural network (CNN)., the system analyzes user-uploaded photos to help users recognize different kinds of flowers. The project's main elements include a strong backend system for picture preprocessing and prediction., an intuitive Flask-based web application., and a machine learning model for categorization. TensorFlow/Keras was used to create and train the machine learning model using a dataset of flower photos. To manage changes in input data and enhance model generalization., the dataset was preprocessed and augmented. Convolutional and pooling layers are used in the model architecture to extract features., and fully linked layers are then used for classification. Crossandra., Hibiscus., Jasmine., Periwinkle., and Rose are among the classifications. The efficacy of the trained model in classifying various flower species is demonstrated by its high accuracy on the validation dataset. The interface between users and the trained model is a Flask application. Images uploaded by users using the web interface are preprocessed (resized., grayscaled., and normalized) before being sent to the model for prediction. A dynamic., visually appealing results page presents the findings., which include a descriptive overview., a confidence score., and the anticipated flower class. While prior models show promising results., issues such as overfitting., poor generalization., and noise sensitivity remain unresolved., which motivated the strategy described in this study.