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

  • Dede Rustandi IPB University
  • Sony Hartono Wijaya IPB University
  • Mushthofa IPB University
  • Ratih Damayanti IPB University
Keywords: asinabu, convolutional neural network, identify bamboo, macroscopic images


It 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 remains 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 faster method of 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). CNN was designed to identify bamboo species with images taken using a cellphone camera equipped with a lens. The final product is an Android automatic identification application that can detect bamboo species with an accuracy of 99.9%.


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How to Cite
Rustandi, D., Sony Hartono Wijaya, Mushthofa, & Ratih Damayanti. (2024). Anatomy Identification of Bamboo Stems with The Convolutional Neural Networks (CNN) Method. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 8(1), 62 -71.
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