DOI: https://doi.org/10.14710/jtsiskom.2020.13768

Klasifikasi klon teh berbasis deep CNN dengan residual dan densely connections

Tea clone classification using deep CNN with residual and densely connections

1Lembaga Ilmu Pengetahuan Indonesia, Indonesia

2Pusat Penelitian Teh dan Kina, Indonesia

Received: 2 Jun 2020; Revised: 16 Sep 2020; Accepted: 13 Oct 2020; Available online: 19 Oct 2020; Published: 31 Oct 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer under http://creativecommons.org/licenses/by-sa/4.0.

How to cite (IEEE): A. Ramdan, V. Zilvan, E. Suryawati, H. F. Pardede, and V. P. Rahadi, "Klasifikasi klon teh berbasis deep CNN dengan residual dan densely connections," Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 4, pp. 289-296, Oct. 2020. https://doi.org/10.14710/jtsiskom.2020.13768
Citation Format:
Abstract
Tea clone of Gambung series is a superior variety of tea that has high productivity and quality. Smallholder farmers usually plant these clones in the same areas. However, each clone has different productivity or quality, so it is difficult to predict the production quality in the same area. To uniform the variety of clones in an area, smallholder farmers still need experts to identify each plant because one and other clones share the same visual characteristics. We propose a tea clone identification system using deep CNN with skip connection methods, i.e., residual connections and densely connections, to tackle this problem. Our study shows that the proposed method is affected by the hyperparameter setting and the combining feature maps method. For the combining method, the concatenation method on a densely connected network shows better performance than the summation method on a residual connected network.
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Keywords: Gambung tea clone; deep CNN; skip connection; densely connected networks; residual connected networks
Funding: Pusat Penelitian Informatika, Lembaga Ilmu Pengetahuan Indonesia;Pusat Penelitian Teh dan Kina, Gambung, Indonesia

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