Flower Recognition using Deep Convolutional Neural Networks

Abstract

This study investigates the suitable model for flower recognition based on deep Convolutional Neural Networks (CNN) with transfer learning approach. The dataset used in the study is a benchmark dataset from Kaggle. The performance of CNN for plant identification using images of flower are investigated using two popular image classification models: AlexNet and VGG16. Results show that CNN is proven to produce outstanding results for object recognition, but its achievement can still be influenced by the type of images and the number of layers of the CNN architecture. The models produced adequate performance rates, with the VGG16 model achieving the best results. AlexNet and VGG16 models achieved the accuracy of 85.69% and 95.02% respectively. This model can be replicated for flower recognition in other areas, especially in our national heritage, Taman Negara which is among the richest flora ecosystem in the world. The significant feature extraction processes were discussed as well, and this is useful for other types of flowers than the trained dataset.