RCSnet——Flower Classification Network Design Based on Transfer Learning and Channel Attention Mechanism
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5588-2_5
Reference23 articles.
1. Bae, K.I., Park, J., Lee, J., et al.: Flower classification with modified multimodal convolutional neural networks. Expert Syst. Appl. 159, 113455 (2020)
2. Hiary, H., Saadeh, H., Saadeh, M., et al.: Flower classification using deep convolutional neural networks. IET Comput. Vis. 12(6), 855–862 (2018)
3. Deepa, S.N., Rasi, D.: FHGSO: flower Henry gas solubility optimization integrated deep convolutional neural network for image classification. Appl. Intell. 53(6), 7278–7297 (2023)
4. Kolarik, N.E., Roopsind, A., Pickens, A., et al.: A satellite-based monitoring system for quantifying surface water and mesic vegetation dynamics in a semi-arid region. Ecol. Ind. 147, 109965 (2023)
5. Kumar, M., Chaudhary, V., Sirohi, U., et al.: Economically viable flower drying techniques to sustain flower industry amid COVID-19 pandemic. Environ. Dev. Sustain., 1–46 (2023)
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