ROTATION-GAMMA CORRECTION AUGMENTATION ON CNN-DENSE BLOCK FOR SOIL IMAGE CLASSIFICATION
Sri INDRA MAIYANTI
Mathematics Departement, Mathematics and Natural Science Faculty, Universitas Sriwijaya (Indonesia)
https://orcid.org/0009-0009-9983-8279
Anita DESIANI
anita_desiani@unsri.ac.idMathematics Departement, Mathematics and Natural Science Faculty, Universitas Sriwijaya (Indonesia)
https://orcid.org/0000-0001-8851-2454
Syafrina LAMIN
Biology Department, Faculty of Mathematics and Natural Science, Universitas Sriwijaya (Indonesia)
P PUSPITAHATI
Agriculture Technology Departement, Faculty of Agriculture, Universitas Sriwijaya (Indonesia)
Muhammad ARHAMI
Informatics Technique Departement, Politeknik Negeri Lhokseumawe (Indonesia)
Nuni GOFAR
Soil Departement, Faculty of Agriculture, Universitas Sriwijaya (Indonesia)
Destika CAHYANA
Research Center for Geospasial, Research Organization for Earth Science and Maritime, the National Research and Innovation Agency of the Republic of Indonesia (Indonesia)
https://orcid.org/0000-0001-8461-0700
Abstract
Soil is a solid-particle that covers the earth's surface. Soils can be classified based their color. The color can be an indication of soil properties and soil conditions. Soil image classification requires high accuracy and caution. CNN works well on image classification, but CNN requires a large amount of data. Augmentation is one technique to overcome data needs like rotation and improving contrast. Rotation is the movement of rotating the image position randomly to various degrees. Gamma Correction is a method to improve image by decreasing or increasing the contrast. The rotation and Gamma Correction on augmentation can increase the amount of training data from 156 to 2500 soil images data. The classification of soil data is not referred to soil taxonomy system such as Entisols and Histosols but it used arbitrary simple classification based on color. Unfortunately, the weakness of the CNN is vanishing and exploded gradients. Another Deep learning that can overcome vanishing and exploded gradients is dense blocks. This study proposes a combination of Augmentation and CNN-Dense block where in the augmentation a combination of rotation and Gamma-correction techniques is used and Soil image classification based on color is used by the CNN-Dense block. The combination method is able to give excellent results, where all performances accuracy, precisions, recall and F1-Score are above 90%. The combination of rotation and Gamma Correction on augmentation and CNN is a robust method to use in soil image classification based on color.
Supporting Agencies
Keywords:
CNN, Image, Classification, Gamma Correction, Rotation, SoilReferences
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Authors
Sri INDRA MAIYANTIMathematics Departement, Mathematics and Natural Science Faculty, Universitas Sriwijaya Indonesia
https://orcid.org/0009-0009-9983-8279
Authors
Anita DESIANIanita_desiani@unsri.ac.id
Mathematics Departement, Mathematics and Natural Science Faculty, Universitas Sriwijaya Indonesia
https://orcid.org/0000-0001-8851-2454
Authors
Syafrina LAMINBiology Department, Faculty of Mathematics and Natural Science, Universitas Sriwijaya Indonesia
Authors
P PUSPITAHATIAgriculture Technology Departement, Faculty of Agriculture, Universitas Sriwijaya Indonesia
Authors
Muhammad ARHAMIInformatics Technique Departement, Politeknik Negeri Lhokseumawe Indonesia
Authors
Nuni GOFARSoil Departement, Faculty of Agriculture, Universitas Sriwijaya Indonesia
Authors
Destika CAHYANAResearch Center for Geospasial, Research Organization for Earth Science and Maritime, the National Research and Innovation Agency of the Republic of Indonesia Indonesia
https://orcid.org/0000-0001-8461-0700
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