Keywords: Recognition, of
This study develops a Convolutional Neural Network (CNN) model to accurately recognize various flower species based on their images. The CNN extracts features and enables effective classification. The research contributes to automated flower recognition systems, benefiting botanical research and horticultural applications. Training deep learning models, especially CNNs, can be computationally intensive and require powerful hardware resources, such as GPUs or TPUs. Limited access to such resources can hinder model training and development. Developing a CNN-based model to accurately recognize and classify various flower species from images, contributing to automated flower recognition systems for botanical research, conservation, and horticultural applications. The Convolutional Neural Network model for recognizing various flowers achieved a training accuracy of 0.7829 and a validation accuracy of 0.7483. The model's training loss was 0.5642, and the validation loss was 0.7215. These results indicate that the model performed well in classifying flower images, although there is room for further improvement.
Aim
To Recognize the various Flower using DL Techniques
Objective
Employ deep learning techniques with ANN to accurately recognize different flower species, enhancing botanical studies.
Develop an ANN model capable of analyzing flower images to classify and identify various botanical species.
Evaluate the ANN's recognition performance, aiming for precise and reliable flower species classification for researchers and enthusiasts.
ANN Technique used
Jupyter, Google Collab
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