Rotation-Gamma Correction augmentation on CNN-Dense block for soil image classification

• Autorzy: Maiyanti Sri Indra , Desiani Anita , Lamin Syafrina , Puspitashati , Arhami Muhammad , Gofar Nuni , Cahyana Destika

Identyfikatory

DOI

10.35784/acs-2023-27

• Języki publikacji: EN

Abstrakty

EN

Soil is a solid particle that covers the surface of the earth. Soil can be classified based on its color because the color indicates the nature and condition of the soil. CNN works well for image classification, but it requires large amounts of data. Augmentation is a technique to increase the amount of training data with various transformation techniques to the existing data. Rotation and Gamma Correction can be used simply as an augmentation technique and can reproduce an image with as many image variations as desired from the original image. CNN architecture has a convolution layer and Dense block has dense layers. The addition of Dense blocks to CNN aims to overcome underfitting and overfitting problems. This study proposes a combination of Augmentation and classification. In augmentation, a combination of rotation and Gamma correction techniques is used to reproduce image data. The CNN-Dense block is applied for classification. The soil image classification is grouped based on 5 labels black soil, cinder soil, laterite soil, peat soil, and yellow soil. The performances of the proposed method provide excellent results, where accuracy, precision, recall, and F1-Score performances are above 90%. It can be concluded that the combination of rotation and Gamma Correction as augmentation techniques and CNN-Dense blocks is powerful for use in soil image classification.

Słowa kluczowe

EN

CNN; image classification; gamma correction; rotation; soil

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2023
• Tom: Vol. 19, no 3
• Strony: 96--115
• Opis fizyczny: Bibliogr. 31 poz., fig., tab.

Twórcy

autor

Maiyanti Sri Indra

• Mathematics Departement, Mathematics and Natural Science Faculty, Universitas Sriwijaya

• https://orcid.org/0009-0009-9983-8279

autor

Desiani Anita

• Mathematics Departement, Mathematics and Natural Science Faculty, Universitas Sriwijaya

• https://orcid.org/000-0001-8851-2454

autor

Lamin Syafrina

• Biology Department, Faculty of Mathematics and Natural Science, Universitas Sriwijaya

• https://orcid.org/0009-0007-0499-4013

autor

Puspitashati

• Agriculture Technology Departement, Faculty of Agriculture, Universitas Sriwijaya

• https://orcid.org/0009-0000-5548-3992

autor

Arhami Muhammad

• Informatics Technique Departement, Politeknik Negeri Lhokseumawe, Lhokseumawa, Indonesia

• https://orcid.org/0000-0002-7992-0103

autor

Gofar Nuni

• Soil Departement, Faculty of Agriculture, Universitas Sriwijaya

• https://orcid.org/000-0003-3469-1690

autor

Cahyana Destika

• Research Center for Geospasial, Research Organization for Earth Science and Maritime, the National Research and Innovation Agency of the Republic of Indonesia

• https://orcid.org/0000-0001-8461-0700

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