Abstract

The classification of flowers using digital images has numerous applications in agriculture, biodiversity conservation, and education. This project introduces a Deep Convolutional Neural Network (DCNN) approach to recognize flower species from images. The model is trained on a dataset consisting of thousands of flower images across five categories. By employing layers of convolution, max-pooling, and fully connected nodes, the DCNN effectively learns spatial hierarchies in the data. Preprocessing techniques such as normalization, resizing, and data augmentation are used to improve performance. Evaluation metrics including accuracy, precision, recall, and F1-score were used to assess the model’s effectiveness. A user-friendly interface was developed to allow users to classify new flower images.

Key Contributions

• Developed a deep CNN model capable of distinguishing five flower categories with high accuracy.
• Applied data augmentation and image preprocessing to enhance model generalization.
• Achieved an accuracy of 94.5% on the validation dataset using optimized hyperparameters.
• Designed a simple GUI application to classify user-uploaded flower images in real time.

Conclusion

The DCNN-based flower classification model successfully demonstrates the application of deep learning in image recognition tasks. Its high accuracy and efficient performance highlight its potential for real-world deployment in educational and environmental monitoring tools.

Limitation

The model's generalizability may be limited when applied to rare flower species or images taken under significantly different environmental conditions. Also, high computational resources are required for training the model.

Future Work

Future enhancements may include integrating a broader dataset with more flower species, exploring transfer learning with advanced architectures like EfficientNet, and deploying the model on mobile devices for field research.