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What is IJCRTAS02022 about?

This paper presents a Convolutional Neural Network (CNN) approach for automating flower recognition using the Kaggle Flowers dataset, achieving a classification accuracy of 99.09%. The model incorporates data augmentation and a user-friendly interface for real-time classification, making it applicable in fields such as botany and retail. The study highlights the effectiveness of CNNs in image classification and suggests future work to enhance dataset variability and include more flower species.

Author

sm2nict

Language

EN