Abstract
In machine learning, image classification plays a very important role in demonstrating any image. Recognition of flower species is based on the geometry, texture, and form of different flowers in the past year. Now, nowadays, flower identification is widely used to recognize medicinal plant species. There are about 400,000 flowering plant species, and modern search engines have the mechanism to search and identify the image containing a flower, but due to millions of flower species worldwide, robustness is lacking. The method of machine learning with CNN is then used to classify the flower species in this proposed research work. With data, we will train the machine learning model, and if any unknown pattern is discovered, then the predictive model will predict the flower species by what it has been gained by the trained data. The built-in camera of the mobile phone will acquire the images of the flower species, and the flower image extraction function is performed using a pretrained network extraction of complex features.
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Bondre, S., Yadav, U. (2022). Automated Flower Species Identification by Using Deep Convolution Neural Network. In: Satapathy, S.C., Peer, P., Tang, J., Bhateja, V., Ghosh, A. (eds) Intelligent Data Engineering and Analytics. Smart Innovation, Systems and Technologies, vol 266. Springer, Singapore. https://doi.org/10.1007/978-981-16-6624-7_1
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DOI: https://doi.org/10.1007/978-981-16-6624-7_1
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