We have proposed a ‘Pattern recognition and prediction AI model’ technique to predict as well as recognize various species of flowers. This technique is based on Machine Learning (ML) and a Deep Learning (DL) model. In this, we’ll discuss the technique of flower recognition, whose patterns are created by a DL model. This deep learning model will predict the diversity of flower species. An essential aspect of this model is that the features,extracted in this model, will lay out the patterns accordingly.
In this AI model, some steps need to be followed in order to recognize the species of flowers. This model has three stages: data input, feature extraction, and flower classification.
- The foremost stage is data input in which the pre-trained Convolutional Neural Network (CNN) models are fed with images of flowers. After the data is inserted, it will be processed to remove the unwanted information from the data to acquire accurate results.
- The next stage is to implement the Feature Extraction algorithms. AlexNet and VGG16 models, and fc6 and fc7 layers are implemented for deep feature extraction. Thereafter, the extracted feature vectors are used in a sequence to form a single representation of the images of inserted flower.
- The last stage is the classification, in which, a Support Vector Machine (SVM) classifier is employed for deciding the labels of the flower images.
These are the steps of the Pattern recognition and Prediction technique, in which we recognized the category of flower. The experimental results demonstrated that our proposed technique has achieved an accuracy of 99.95%. Our proposed technique is implemented in Python and Google colabs. This model can also be integrated with Android and iOS apps with the help of various Python APIs.