Soil Structure Analysis with Attention: A Deep-Learning-Based Method for 3D Pore Segmentation and Characterization
The pore structure plays a crucial role in soil systems. It affects a range of processes essential for soil ecological functions, such as the transport and retention of water and nutrients, as well as gas exchanges. The mechanical and hydrological characteristics of soil are predominantly determined...
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MDPI AG
2025-01-01
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| author | Italo Francyles Santos da Silva Alan de Carvalho Araújo João Dallyson Sousa de Almeida Anselmo Cardoso de Paiva Aristófanes Corrêa Silva Deane Roehl |
| author_facet | Italo Francyles Santos da Silva Alan de Carvalho Araújo João Dallyson Sousa de Almeida Anselmo Cardoso de Paiva Aristófanes Corrêa Silva Deane Roehl |
| author_sort | Italo Francyles Santos da Silva |
| collection | DOAJ |
| description | The pore structure plays a crucial role in soil systems. It affects a range of processes essential for soil ecological functions, such as the transport and retention of water and nutrients, as well as gas exchanges. The mechanical and hydrological characteristics of soil are predominantly determined by the three-dimensional pore pore-space structure. A precise analysis of pore structure can help specialists understand how these shapes impact plant root activity, leading to better cultivation practices. X-ray computed tomography provides detailed information without destroying the sample. However, manually delineating pore structure and estimating porosity are challenging tasks. This work proposes an automated method for 3D pore segmentation and characterization using convolutional neural networks with attention mechanisms. The method introduces a novel approach that combines attention at both channel and spatial levels, enhancing the segmentation and property estimation, providing valuable insights for a more detailed study of soil conditions. In experiments conducted with a private dataset, the segmentation results achieved mean Dice values of 99.10% ± 0.0004 and mean IoU values of 98.23% ± 0.0008. Additionally, in tests with Phaeozem Albic, the automatic method provided porosity estimates comparable to those obtained by a method based on integral geometry and morphology. |
| format | Article |
| id | doaj-art-7491440d2a9740fea4e2a72ab0e45fcb |
| institution | DOAJ |
| issn | 2624-7402 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj-art-7491440d2a9740fea4e2a72ab0e45fcb2025-08-20T02:44:43ZengMDPI AGAgriEngineering2624-74022025-01-01722710.3390/agriengineering7020027Soil Structure Analysis with Attention: A Deep-Learning-Based Method for 3D Pore Segmentation and CharacterizationItalo Francyles Santos da Silva0Alan de Carvalho Araújo1João Dallyson Sousa de Almeida2Anselmo Cardoso de Paiva3Aristófanes Corrêa Silva4Deane Roehl5Applied Computing Group (NCA), Federal University of Maranhão, São Luís 65085-580, MA, BrazilApplied Computing Group (NCA), Federal University of Maranhão, São Luís 65085-580, MA, BrazilApplied Computing Group (NCA), Federal University of Maranhão, São Luís 65085-580, MA, BrazilApplied Computing Group (NCA), Federal University of Maranhão, São Luís 65085-580, MA, BrazilApplied Computing Group (NCA), Federal University of Maranhão, São Luís 65085-580, MA, BrazilTecgraf Institute, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22453-900, RJ, BrazilThe pore structure plays a crucial role in soil systems. It affects a range of processes essential for soil ecological functions, such as the transport and retention of water and nutrients, as well as gas exchanges. The mechanical and hydrological characteristics of soil are predominantly determined by the three-dimensional pore pore-space structure. A precise analysis of pore structure can help specialists understand how these shapes impact plant root activity, leading to better cultivation practices. X-ray computed tomography provides detailed information without destroying the sample. However, manually delineating pore structure and estimating porosity are challenging tasks. This work proposes an automated method for 3D pore segmentation and characterization using convolutional neural networks with attention mechanisms. The method introduces a novel approach that combines attention at both channel and spatial levels, enhancing the segmentation and property estimation, providing valuable insights for a more detailed study of soil conditions. In experiments conducted with a private dataset, the segmentation results achieved mean Dice values of 99.10% ± 0.0004 and mean IoU values of 98.23% ± 0.0008. Additionally, in tests with Phaeozem Albic, the automatic method provided porosity estimates comparable to those obtained by a method based on integral geometry and morphology.https://www.mdpi.com/2624-7402/7/2/273D pore segmentationsoil characterizationporosity estimationconvolutional neural networksattention mechanismscomputed tomography |
| spellingShingle | Italo Francyles Santos da Silva Alan de Carvalho Araújo João Dallyson Sousa de Almeida Anselmo Cardoso de Paiva Aristófanes Corrêa Silva Deane Roehl Soil Structure Analysis with Attention: A Deep-Learning-Based Method for 3D Pore Segmentation and Characterization AgriEngineering 3D pore segmentation soil characterization porosity estimation convolutional neural networks attention mechanisms computed tomography |
| title | Soil Structure Analysis with Attention: A Deep-Learning-Based Method for 3D Pore Segmentation and Characterization |
| title_full | Soil Structure Analysis with Attention: A Deep-Learning-Based Method for 3D Pore Segmentation and Characterization |
| title_fullStr | Soil Structure Analysis with Attention: A Deep-Learning-Based Method for 3D Pore Segmentation and Characterization |
| title_full_unstemmed | Soil Structure Analysis with Attention: A Deep-Learning-Based Method for 3D Pore Segmentation and Characterization |
| title_short | Soil Structure Analysis with Attention: A Deep-Learning-Based Method for 3D Pore Segmentation and Characterization |
| title_sort | soil structure analysis with attention a deep learning based method for 3d pore segmentation and characterization |
| topic | 3D pore segmentation soil characterization porosity estimation convolutional neural networks attention mechanisms computed tomography |
| url | https://www.mdpi.com/2624-7402/7/2/27 |
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