TransLIBS-CRS: Integrating LIBS with transfer learning for accurate cross-regional soil total nitrogen detection
Accurate detection of soil total nitrogen (TN) is crucial for enhancing crop growth and quality. However, soil variability across different regions limits the generalization ability of calibration models. To address this challenge, this study introduces a model named transfer-learning-assisted laser...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S277237552500351X |
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| author | Peng Lin Shixiang Ma Zhizheng Shi Peiying Li Leizi Jiao Hongwu Tian Zhen Xing Chunjiang Zhao Daming Dong |
| author_facet | Peng Lin Shixiang Ma Zhizheng Shi Peiying Li Leizi Jiao Hongwu Tian Zhen Xing Chunjiang Zhao Daming Dong |
| author_sort | Peng Lin |
| collection | DOAJ |
| description | Accurate detection of soil total nitrogen (TN) is crucial for enhancing crop growth and quality. However, soil variability across different regions limits the generalization ability of calibration models. To address this challenge, this study introduces a model named transfer-learning-assisted laser-induced breakdown spectroscopy for cross-regional soil analysis (TransLIBS-CRS), specifically designed to mitigate the low cross-domain prediction accuracy caused by variations in regional soil properties. By fine-tuning with a limited number of target domain samples, this method significantly improves the cross-domain applicability of LIBS data, alleviating the challenges associated with the difficulty of obtaining soil samples from diverse regions. In the task of predicting TN in Guangzhou using the Beijing dataset, the TransLIBS-CRS model achieved optimal performance, with RV2 of 0.846 and RMSEV- of 0.814 g/kg. Further analysis through saliency map and chemometric methods revealed that spectral lines of carbon at 193.0 nm and 247.8 nm play a key role in the quantitative detection of TN. Notably, these spectral features also demonstrated stable predictive contributions when transferred to the Guangzhou soil dataset. This approach offers a feasible solution for large-scale and efficient soil TN detection. |
| format | Article |
| id | doaj-art-9cecc27666f748d2913e9dfb0b9f27b4 |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-9cecc27666f748d2913e9dfb0b9f27b42025-08-20T03:30:04ZengElsevierSmart Agricultural Technology2772-37552025-12-011210111810.1016/j.atech.2025.101118TransLIBS-CRS: Integrating LIBS with transfer learning for accurate cross-regional soil total nitrogen detectionPeng Lin0Shixiang Ma1Zhizheng Shi2Peiying Li3Leizi Jiao4Hongwu Tian5Zhen Xing6Chunjiang Zhao7Daming Dong8College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, ChinaResearch Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, China; Corresponding authors.Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, ChinaResearch Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, ChinaResearch Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, ChinaResearch Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, ChinaResearch Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, China; Corresponding authors.Accurate detection of soil total nitrogen (TN) is crucial for enhancing crop growth and quality. However, soil variability across different regions limits the generalization ability of calibration models. To address this challenge, this study introduces a model named transfer-learning-assisted laser-induced breakdown spectroscopy for cross-regional soil analysis (TransLIBS-CRS), specifically designed to mitigate the low cross-domain prediction accuracy caused by variations in regional soil properties. By fine-tuning with a limited number of target domain samples, this method significantly improves the cross-domain applicability of LIBS data, alleviating the challenges associated with the difficulty of obtaining soil samples from diverse regions. In the task of predicting TN in Guangzhou using the Beijing dataset, the TransLIBS-CRS model achieved optimal performance, with RV2 of 0.846 and RMSEV- of 0.814 g/kg. Further analysis through saliency map and chemometric methods revealed that spectral lines of carbon at 193.0 nm and 247.8 nm play a key role in the quantitative detection of TN. Notably, these spectral features also demonstrated stable predictive contributions when transferred to the Guangzhou soil dataset. This approach offers a feasible solution for large-scale and efficient soil TN detection.http://www.sciencedirect.com/science/article/pii/S277237552500351XLaser-induced breakdown spectroscopyTransfer learningSoil total nitrogenDifferent regionsExplainability |
| spellingShingle | Peng Lin Shixiang Ma Zhizheng Shi Peiying Li Leizi Jiao Hongwu Tian Zhen Xing Chunjiang Zhao Daming Dong TransLIBS-CRS: Integrating LIBS with transfer learning for accurate cross-regional soil total nitrogen detection Smart Agricultural Technology Laser-induced breakdown spectroscopy Transfer learning Soil total nitrogen Different regions Explainability |
| title | TransLIBS-CRS: Integrating LIBS with transfer learning for accurate cross-regional soil total nitrogen detection |
| title_full | TransLIBS-CRS: Integrating LIBS with transfer learning for accurate cross-regional soil total nitrogen detection |
| title_fullStr | TransLIBS-CRS: Integrating LIBS with transfer learning for accurate cross-regional soil total nitrogen detection |
| title_full_unstemmed | TransLIBS-CRS: Integrating LIBS with transfer learning for accurate cross-regional soil total nitrogen detection |
| title_short | TransLIBS-CRS: Integrating LIBS with transfer learning for accurate cross-regional soil total nitrogen detection |
| title_sort | translibs crs integrating libs with transfer learning for accurate cross regional soil total nitrogen detection |
| topic | Laser-induced breakdown spectroscopy Transfer learning Soil total nitrogen Different regions Explainability |
| url | http://www.sciencedirect.com/science/article/pii/S277237552500351X |
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