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Leaf Disease Detection Model in Gayo Coffee Plantations Using Deep Learning

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 20 No. 1 (2026) · pp. 37–46

DOI
10.22146/ijccs.110529
ISSN
2460-7258, 1978-1520
Published
2026-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Coffee is one of the most important tropical plantation commodities, significantly supporting the economy of the Gayo Highlands. Attacks of various diseases can significantly reduce the productivity and quality of Gayo coffee. This study developed a leaf disease detection model in coffee plants using the Convolutional Neural Network (CNN) method. The model developed in this study used two datasets. The first dataset, the Gayo Coffee Leaf Disease (PDKG), comprises 900 images of healthy and diseased leaves collected from Gayo coffee plantations. The acquired images in the PDKG dataset were then preprocessed to improve their image quality. The results of model training and testing on the PDKG dataset showed an accuracy of 0.91. On the public Coffee Leaf Diseases (CLD) dataset, the model achieved an accuracy of 0.95, representing a 7.1% increase compared to previous studies. The resulting model can help local coffee farmers in the Gayo Highlands detect leaf diseases early and manage plant health more efficiently and accurately.

Keywords

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Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Leaf Disease Detection Model in Gayo Coffee Plantations Using Deep Learning</dc:title>
	<dc:creator>Hidayat, Rahmad</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science;Machine Learning;Deep Learning</dc:subject>
	<dc:subject xml:lang="en-US">Leaf Disease Detection, Gayo Coffee Plants, CNN</dc:subject>
	<dc:description xml:lang="en-US">Coffee is one of the most important tropical plantation commodities, significantly supporting the economy of the Gayo Highlands. Attacks of various diseases can significantly reduce the productivity and quality of Gayo coffee. This study developed a leaf disease detection model in coffee plants using the Convolutional Neural Network (CNN) method. The model developed in this study used two datasets. The first dataset, the Gayo Coffee Leaf Disease (PDKG), comprises 900 images of healthy and diseased leaves collected from Gayo coffee plantations. The acquired images in the PDKG dataset were then preprocessed to improve their image quality. The results of model training and testing on the PDKG dataset showed an accuracy of 0.91. On the public Coffee Leaf Diseases (CLD) dataset, the model achieved an accuracy of 0.95, representing a 7.1% increase compared to previous studies. The resulting model can help local coffee farmers in the Gayo Highlands detect leaf diseases early and manage plant health more efficiently and accurately. </dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">Rahmad Hidayat</dc:contributor>
	<dc:contributor xml:lang="en-US">Teknik Informatika</dc:contributor>
	<dc:contributor xml:lang="en-US">Jurusan Teknologi Informasi Dan Komputer, Politeknik Negeri Lhokseumawe</dc:contributor>
	<dc:date>2026-01-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/110529</dc:identifier>
	<dc:identifier>10.22146/ijccs.110529</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 20, No 1 (2026): January; 37-46</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/110529/43428</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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