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Comparison of CNN Models With Transfer Learning in the Classification of Insect Pests

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 17 No. 1 (2023) · pp. 103–114

DOI
10.22146/ijccs.80956
ISSN
2460-7258, 1978-1520
Published
2023-02-23
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Insect pests are an important problem to overcome in agriculture. The purpose of this research is to classify insect pests with the IP-102 dataset using several CNN pre-trained models and choose which model is best for classifying insect pest data. The method used is the transfer learning method with a fine-tuning approach. Transfer learning was chosen because this technique can use the features and weights that have been obtained during the previous training process. Thus, computation time can be reduced and accuracy can be increased. The models used include Xception, MobileNetV3L, MobileNetV2, DenseNet-201, and InceptionV3. Fine-tuning and freeze layer techniques are also used to improve the quality of the resulting model, making it more accurate and better suited to the problem at hand. This study uses 75,222 image data with 102 classes. The results of this study are the DenseNet-201 model with fine-tuning produces an accuracy value of 70%, MobileNetV2 66%, MobileNetV3L 68%, InceptionV3 67%, Xception 69%. The conclusion of this study is that the transfer learning method with the fine-tuning approach produces the highest accuracy value of 70% in the DenseNet-201 model.

Keywords

Collaboration network of the authors

5 authors · 5 co-authorship links · 2 across different organisations

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  • Universitas Muhammadiyah Pontianak
  • Universitas Gadjah Mada
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Reza PulunganUniversitas Gadjah Mada1
Barry Ceasar OctariadiUniversitas Muhammadiyah Pontianak1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

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">Comparison of CNN Models With Transfer Learning in the Classification of Insect Pests</dc:title>
	<dc:creator>Syahputra, Angga Prima</dc:creator>
	<dc:creator>Siregar, Alda Cendekia</dc:creator>
	<dc:creator>Insani, Rachmat Wahid Saleh</dc:creator>
	<dc:subject xml:lang="en-US">Informatics</dc:subject>
	<dc:subject xml:lang="en-US">CNN, transfer learning, fine-tuning, classification, Insect pest</dc:subject>
	<dc:description xml:lang="en-US">Insect pests are an important problem to overcome in agriculture. The purpose of this research is to classify insect pests with the IP-102 dataset using several CNN pre-trained models and choose which model is best for classifying insect pest data. The method used is the transfer learning method with a fine-tuning approach. Transfer learning was chosen because this technique can use the features and weights that have been obtained during the previous training process. Thus, computation time can be reduced and accuracy can be increased. The models used include Xception, MobileNetV3L, MobileNetV2, DenseNet-201, and InceptionV3. Fine-tuning and freeze layer techniques are also used to improve the quality of the resulting model, making it more accurate and better suited to the problem at hand. This study uses 75,222 image data with 102 classes. The results of this study are the DenseNet-201 model with fine-tuning produces an accuracy value of 70%, MobileNetV2 66%, MobileNetV3L 68%, InceptionV3 67%, Xception 69%. The conclusion of this study is that the transfer learning method with the fine-tuning approach produces the highest accuracy value of 70% in the DenseNet-201 model.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2023-02-23</dc:date>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/80956</dc:identifier>
	<dc:identifier>10.22146/ijccs.80956</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 17, No 1 (2023): January; 103-114</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/80956/35785</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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