Anatomy Identification of Bamboo Stems with The Convolutional Neural Networks (CNN) Method

Anatomy Identification of Bamboo Stems with The Convolutional Neural Networks (CNN) Method

asinabu; convolutional neural network; identify bamboo; macroscopic images

Bamboo is important to note that some species of bamboo are protected and considered endangered. However, distinguishing between traded and protected bamboo species or differentiating between bamboo species for various purposes is still a challenge. This requires specialized skills to identify the type of bamboo, and currently, the process can only be carried out in the forest for bamboo that is still in clump form by experienced researchers or officers. However, a study has been conducted to develop an easier and quicker method for identifying bamboo species. The study aims to create an automatic identification system for bamboo stems based on their anatomical structure (ASINABU). The bamboo identification algorithm was developed using macroscopic images of cross-sectioned bamboo stems, and the research method used was the Convolutional Neural Network (CNN). The CNN was designed to identify bamboo species with images taken using a cellphone camera equipped with a lens. The final product is an automatic identification application on Android, which can accurately detect bamboo species with an accuracy of 99.9%.

Dede Rustandi1, Sony Hartono Wijaya2, Mushthofa3, Ratih Damayanti

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5370/893

Computer Science Study Program, Faculty of Mathematics and Natural Sciences, IPB University, Bogor, Indonesia

XX-02-2024

FAJAR BAGUS W

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