ROTATION-GAMMA CORRECTION AUGMENTATION ON CNN-DENSE BLOCK FOR SOIL IMAGE CLASSIFICATION
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 l...
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| Format: | Article |
| Language: | English |
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Polish Association for Knowledge Promotion
2023-09-01
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| Series: | Applied Computer Science |
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| Online Access: | https://ph.pollub.pl/index.php/acs/article/view/3682 |
| _version_ | 1797649401905152000 |
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| author | Sri INDRA MAIYANTI Anita DESIANI Syafrina LAMIN P PUSPITAHATI Muhammad ARHAMI Nuni GOFAR Destika CAHYANA |
| author_facet | Sri INDRA MAIYANTI Anita DESIANI Syafrina LAMIN P PUSPITAHATI Muhammad ARHAMI Nuni GOFAR Destika CAHYANA |
| author_sort | Sri INDRA MAIYANTI |
| collection | DOAJ |
| description |
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.
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| first_indexed | 2024-03-11T15:45:40Z |
| format | Article |
| id | doaj.art-f9a465e76c654d8faab2502eded8c70c |
| institution | Directory Open Access Journal |
| issn | 2353-6977 |
| language | English |
| last_indexed | 2024-03-11T15:45:40Z |
| publishDate | 2023-09-01 |
| publisher | Polish Association for Knowledge Promotion |
| record_format | Article |
| series | Applied Computer Science |
| spelling | doaj.art-f9a465e76c654d8faab2502eded8c70c2023-10-26T06:34:31ZengPolish Association for Knowledge PromotionApplied Computer Science2353-69772023-09-0119310.35784/acs-2023-27ROTATION-GAMMA CORRECTION AUGMENTATION ON CNN-DENSE BLOCK FOR SOIL IMAGE CLASSIFICATIONSri INDRA MAIYANTI0https://orcid.org/0009-0009-9983-8279Anita DESIANI1https://orcid.org/0000-0001-8851-2454Syafrina LAMIN2P PUSPITAHATI3Muhammad ARHAMI4Nuni GOFAR5Destika CAHYANA6https://orcid.org/0000-0001-8461-0700Mathematics Departement, Mathematics and Natural Science Faculty, Universitas SriwijayaMathematics Departement, Mathematics and Natural Science Faculty, Universitas SriwijayaBiology Department, Faculty of Mathematics and Natural Science, Universitas SriwijayaAgriculture Technology Departement, Faculty of Agriculture, Universitas SriwijayaInformatics Technique Departement, Politeknik Negeri LhokseumaweSoil Departement, Faculty of Agriculture, Universitas SriwijayaResearch Center for Geospasial, Research Organization for Earth Science and Maritime, the National Research and Innovation Agency of the Republic of Indonesia 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. https://ph.pollub.pl/index.php/acs/article/view/3682CNNImageClassificationGamma CorrectionRotationSoil |
| spellingShingle | Sri INDRA MAIYANTI Anita DESIANI Syafrina LAMIN P PUSPITAHATI Muhammad ARHAMI Nuni GOFAR Destika CAHYANA ROTATION-GAMMA CORRECTION AUGMENTATION ON CNN-DENSE BLOCK FOR SOIL IMAGE CLASSIFICATION Applied Computer Science CNN Image Classification Gamma Correction Rotation Soil |
| title | ROTATION-GAMMA CORRECTION AUGMENTATION ON CNN-DENSE BLOCK FOR SOIL IMAGE CLASSIFICATION |
| title_full | ROTATION-GAMMA CORRECTION AUGMENTATION ON CNN-DENSE BLOCK FOR SOIL IMAGE CLASSIFICATION |
| title_fullStr | ROTATION-GAMMA CORRECTION AUGMENTATION ON CNN-DENSE BLOCK FOR SOIL IMAGE CLASSIFICATION |
| title_full_unstemmed | ROTATION-GAMMA CORRECTION AUGMENTATION ON CNN-DENSE BLOCK FOR SOIL IMAGE CLASSIFICATION |
| title_short | ROTATION-GAMMA CORRECTION AUGMENTATION ON CNN-DENSE BLOCK FOR SOIL IMAGE CLASSIFICATION |
| title_sort | rotation gamma correction augmentation on cnn dense block for soil image classification |
| topic | CNN Image Classification Gamma Correction Rotation Soil |
| url | https://ph.pollub.pl/index.php/acs/article/view/3682 |
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