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In the feature extraction, an improved H component with the definition of WGB value is applied to extract the color feature, and a new algorithm based on local binary pattern (LBP) is proposed to enhance the accuracy of texture extraction. Besides this, we replace ReLU with Mish as activation function in the network design, and therefore increase the accuracy by 8% accuracy according to our comparison. The Oxford-102 and Oxford-17 datasets are adopted for benchmarking. The experimental results show that the combination of color features and texture features generates the highest recognition accuracy as 92.56% on Oxford-102 and 93% on Oxford-17. <\/jats:p>","DOI":"10.1142\/s0218126621502819","type":"journal-article","created":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T16:25:56Z","timestamp":1623774356000},"source":"Crossref","is-referenced-by-count":8,"title":["A Multi-Feature Convolution Neural Network for Automatic Flower Recognition"],"prefix":"10.1142","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8176-6336","authenticated-orcid":false,"given":"Juan","family":"Ran","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Tianjin University Renai College, Tianjin, P. R. China"}]},{"given":"Yu","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tianjin University Renai College, Tianjin, P. R. 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