Deep CNNBased Detection for Tea Clone Identification

  Ade Ramdan (1*), Endang Suryawati (2), R. Budiarianto Suryo Kusumo (3), Hilman F. Pardede (4), Oka Mahendra (5), Rico Dahlan (6), Fani Fauziah (7), Heri Syahrian (8)

(1) Research Center for Informatics - LIPI - Indonesia
(2) Research Center for Informatics - LIPI - Indonesia
(3) Research Center for Informatics - LIPI - Indonesia
(4) Research Center for Informatics - LIPI - Indonesia
(5) Technical Implementation Unit for Instrumentation Development - LIPI - Indonesia
(6) Research Center for Electronics and Telecommunication - LIPI - Indonesia
(7) Research Institute for Tea and Cinchona - Indonesia
(8) Research Institute for Tea and Cinchona - Indonesia
(*) Corresponding Author

Received: July 30, 2019; Revised: October 14, 2019
Accepted: October 28, 2019; Published: December 31, 2019

How to cite (IEEE): A. Ramdan, E. Suryawati, R. B. Kusumo, H. F. Pardede, O. Mahendra, R. Dahlan, F. Fauziah,  and H. Syahrian, "Deep CNNBased Detection for Tea Clone Identification," Jurnal Elektronika dan Telekomunikasi, vol. 19, no. 2, pp. 45-50, Dec. 2019. doi: 10.14203/jet.v19.45-50


One factor affecting the quality of tea is the selection of plant material that would be planted on the field. Clonal selection is a common way to produce tea with better quality. However, as a natural cross pollination species, tea often consists of various clones or progenies of cross-pollinated process. This commonly occurs on plantations owned by smallholder farmers. To produce a consistent quality tea, the clones or progenies need to be identified. Usually, human experts distinguish the plants from leaves by visual inspection on the physical attributes of the leaves, such as the textures, the bone structures, and the colors. It is very difficult for non-experts or common farmers to do such identifications. In this, we propose a deep learning-based identification of tea clones. We apply deep convolutional neural network (CNN) to identify 3 types of tea clones of Gambung series, a series of tea clones developed at Research Institute of Tea and Cinchona. Our study indicates that the performance of the CNN systems are affected by the depth of the convolutional layers. VGGNet, a popular CNN architectures with 16 layers, achieves better accuracy compared to AlexNet, a CNN with 6 layers.



Convolutional neural network; deep learning; gambung clone series; tea clones identification

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