Abstract
It is a very hard and a challenging mission to identify different types of flowers as they are very similar. Even expert botanists and gardeners cannot identify some of them accurately. The idea of automating flowers recognition is bewildering as the flowers are not rigid objects and their images can be affected by many External influences. The proposed system use machine learning algorithms to fully automate and increase the accuracy of flower classification. Machine learning model will be used to extract flower’s features automatically, process through different layers of the neural network and finally classify the flower class. The proposed work is based on “Resnet” model, which is used for classification task. Resnet won the first place on ILSVRC 2015. Many enhancements have been made on Resnet model to improve the accuracy. Fine tuning, dropout ratio and class weight are some of the proposed model enhancements. The proposed model reaches 92% accuracy, which is the highest percent till the moment.
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Acknowledgment
This research was supported by Kuwait Foundation for the Advancement of Sciences (KFAS) and College of Graduate Studies, Kuwait University. Special thanks to Prof. Maytham Safar who provided insight and expertise that greatly assisted the research. We would also like to thank the experts who were involved in the validation for this research project.
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Safar, A., Safar, M. (2020). Intelligent Flower Detection System Using Machine Learning. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_33
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