Published Paper Details:

The goal of this paper is to identify flower varieties in conjunction with Kaggle Flowers dataset employing Convolutional Neural Networks (CNNs). The dataset contains images of five classes of flowers being, daisy, dandelion, rose, sunflower, and tulip. A classifier based on CNN was developed based on 4323 images and was able to attain a classification accuracy rate of 99.09%. The framework displays an efficient interface where users can upload images of flowers with hopes of obtaining an accurate classification. The model performance was improved by implementing data augmentation and multi-level design of CNN enabling the model to accommodate images variability. This application is useful in areas such as botany, gardening and retail sector because it helps people to use images in identifying and selecting their opportunities faster and more accurately. The project demonstrates the efficiency of CNN networks as applied in image classification and their future use into practice.