Mapping of soil sampling sites using terrain and hydrological attributes
Efficient soil sampling is essential for effective soil management and research on soil health. Traditional site selection methods are labor-intensive and fail to capture soil variability comprehensively. This study introduces a deep learning-based tool that automates soil sampling site selection us...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-09-01
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| Series: | Artificial Intelligence in Agriculture |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589721725000479 |
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| author | Tan-Hanh Pham Kristopher Osterloh Kim-Doang Nguyen |
| author_facet | Tan-Hanh Pham Kristopher Osterloh Kim-Doang Nguyen |
| author_sort | Tan-Hanh Pham |
| collection | DOAJ |
| description | Efficient soil sampling is essential for effective soil management and research on soil health. Traditional site selection methods are labor-intensive and fail to capture soil variability comprehensively. This study introduces a deep learning-based tool that automates soil sampling site selection using spectral images. The proposed framework consists of two key components: an extractor and a predictor. The extractor, based on a convolutional neural network (CNN), derives features from spectral images, while the predictor employs self-attention mechanisms to assess feature importance and generate prediction maps. The model is designed to process multiple spectral images and address the class imbalance in soil segmentation.The model was trained on a soil dataset from 20 fields in eastern South Dakota, collected via drone-mounted LiDAR with high-precision GPS. Evaluation on a test set achieved a mean intersection over union (mIoU) of 69.46 % and a mean Dice coefficient (mDc) of 80.35 %, demonstrating strong segmentation performance. The results highlight the model's effectiveness in automating soil sampling site selection, providing an advanced tool for producers and soil scientists. Compared to existing state-of-the-art methods, the proposed approach improves accuracy and efficiency, optimizing soil sampling processes and enhancing soil research. |
| format | Article |
| id | doaj-art-5726a9ff93914f79bd601fcb0aed852e |
| institution | Kabale University |
| issn | 2589-7217 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Agriculture |
| spelling | doaj-art-5726a9ff93914f79bd601fcb0aed852e2025-08-20T03:53:47ZengKeAi Communications Co., Ltd.Artificial Intelligence in Agriculture2589-72172025-09-0115347048110.1016/j.aiia.2025.04.007Mapping of soil sampling sites using terrain and hydrological attributesTan-Hanh Pham0Kristopher Osterloh1Kim-Doang Nguyen2Department of Mechanical and Civil Engineering, Florida Institute of Technology, USADepartment of Agronomy, Horticulture & Plant Science, South Dakota State University, USADepartment of Mechanical and Civil Engineering, Florida Institute of Technology, USA; Corresponding author.Efficient soil sampling is essential for effective soil management and research on soil health. Traditional site selection methods are labor-intensive and fail to capture soil variability comprehensively. This study introduces a deep learning-based tool that automates soil sampling site selection using spectral images. The proposed framework consists of two key components: an extractor and a predictor. The extractor, based on a convolutional neural network (CNN), derives features from spectral images, while the predictor employs self-attention mechanisms to assess feature importance and generate prediction maps. The model is designed to process multiple spectral images and address the class imbalance in soil segmentation.The model was trained on a soil dataset from 20 fields in eastern South Dakota, collected via drone-mounted LiDAR with high-precision GPS. Evaluation on a test set achieved a mean intersection over union (mIoU) of 69.46 % and a mean Dice coefficient (mDc) of 80.35 %, demonstrating strong segmentation performance. The results highlight the model's effectiveness in automating soil sampling site selection, providing an advanced tool for producers and soil scientists. Compared to existing state-of-the-art methods, the proposed approach improves accuracy and efficiency, optimizing soil sampling processes and enhancing soil research.http://www.sciencedirect.com/science/article/pii/S2589721725000479Agriculture precisionDeep learningSoil samplingSpectral imagingSegmentation |
| spellingShingle | Tan-Hanh Pham Kristopher Osterloh Kim-Doang Nguyen Mapping of soil sampling sites using terrain and hydrological attributes Artificial Intelligence in Agriculture Agriculture precision Deep learning Soil sampling Spectral imaging Segmentation |
| title | Mapping of soil sampling sites using terrain and hydrological attributes |
| title_full | Mapping of soil sampling sites using terrain and hydrological attributes |
| title_fullStr | Mapping of soil sampling sites using terrain and hydrological attributes |
| title_full_unstemmed | Mapping of soil sampling sites using terrain and hydrological attributes |
| title_short | Mapping of soil sampling sites using terrain and hydrological attributes |
| title_sort | mapping of soil sampling sites using terrain and hydrological attributes |
| topic | Agriculture precision Deep learning Soil sampling Spectral imaging Segmentation |
| url | http://www.sciencedirect.com/science/article/pii/S2589721725000479 |
| work_keys_str_mv | AT tanhanhpham mappingofsoilsamplingsitesusingterrainandhydrologicalattributes AT kristopherosterloh mappingofsoilsamplingsitesusingterrainandhydrologicalattributes AT kimdoangnguyen mappingofsoilsamplingsitesusingterrainandhydrologicalattributes |