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A Method of CNN Deep Learning for Indonesia Ornamental Plant Classification

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Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 436)

Abstract

Indonesia is considered one of the most biodiverse regions in the world, with 670 mammal species, 1,604 birds, 787 reptiles, and 392 amphibian species as per the IUCN. Flower ornamental plants count among the potential commodities that can be developed both on a small and large scale, as evidenced by the increasing public interest in agribusiness. Many people are still not familiar with the existing types of flower ornamental plants. Several flower ornamental plants only grow or live in some parts of the area. Technological developments can help provide knowledge to the public. The technology currently being widely used is the Deep Learning technique. Deep Learning is a type of artificial neural network algorithm that uses metadata as input and processes it with many hidden layers, using the Convolutional Neural Network (CNN) method. The Convolutional Neural Network (CNN) method can classify objects, essentially images, and recognise them. This study will explore a CNN Deep Learning method that can classify various types of Indonesian ornamental plants object images. The results should pave the way for a prototype that can easily recognise Indonesian ornamental flowers in the future.

Keywords

  • Convolutional Neural Network
  • Deep Learning
  • Ornamental plants

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Acknowledgements

This work is partially supported by the Directorate General of Higher Education, Ministry of Education and Culture, Research and Technology Republic of Indonesia Grant No. 309/E4.1/AK.04.PT/2021 on July 12, 2021, and to the Research Center of Ornamental Plants, as the partner.

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Correspondence to Dewi Agushinta Rahayu .

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Agushinta Rahayu, D., Hustinawaty, Jatnika, I., Lolita, B. (2022). A Method of CNN Deep Learning for Indonesia Ornamental Plant Classification. In: Seyman, M.N. (eds) Electrical and Computer Engineering. ICECENG 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 436. Springer, Cham. https://doi.org/10.1007/978-3-031-01984-5_12

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  • DOI: https://doi.org/10.1007/978-3-031-01984-5_12

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