Flower Species Recognition using DenseNet201 and Multilayer Perceptron

Flower species recognition is the task of identifying the species of a flower from an image. It involves using computer vision techniques and machine learning algorithms to analyze the visual features of the flower in the image and match them to a known database of flower species. Flower species recognition is a challenging task due to the variations in color, shape, and size among different flower species. Accurate flower species recognition has important applications in fields such as agriculture, botany, and environmental conservation. In view of this, this research paper presents a deep learning approach for flower species recognition using a combination of DenseNet201 and MLP. The proposed model leverages the strengths of both models for enhanced performance in recognizing flower species. DenseNet201 is known for its ability to capture complex features in images, while MLP is a powerful tool for learning nonlinear relationships between features. The model achieves impressive classification results on multiple datasets, including 94.47% accuracy on Kaggle, 98.23% and 97.35% on Oxford17 for two different protocols, and 79.13% on Oxford102.