Abstract
Flowers are a plant's most appealing and defining characteristic. As a result, flower recognition can assist in learning more about the plant. Color and shape are the two most distinguishing characteristics of flowers. These characteristics can be used to train the model so that it can recognize an unknown bloom in the future. It can be used to create image-based searching applications in the disciplines of botanical taxonomy, environmental monitoring systems, and multimedia. The paper's goal is to create a machine learning classifier for floral photos from the Oxford-17 dataset. For this, we tested two approaches: develo** a bespoke model from scratch and comparing the accuracies of different pre-trained models. Due to the tiny amount of the dataset, this was a difficult task to solve. The RegNetY 16GF model with pre-trained weights provided the best accuracy. The highest level of accuracy achieved was 93.4%.
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Pandey, S., Sindhuja, B., Nagamanjularani, C.S., Nagarajan, S. (2022). Exploring Transfer Learning Techniques for Flower Recognition Using CNN. In: Shukla, S., Gao, XZ., Kureethara, J.V., Mishra, D. (eds) Data Science and Security. Lecture Notes in Networks and Systems, vol 462. Springer, Singapore. https://doi.org/10.1007/978-981-19-2211-4_35
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