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Deep learning using inductively coupled plasma spectroscopy spectra accurately predicts various soil physicochemical properties for soil diagnosis
https://jircas.repo.nii.ac.jp/records/2001055
https://jircas.repo.nii.ac.jp/records/2001055410d8662-5836-4192-9b7a-a4b6e64ac11a
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
s41598-025-24274-3 (2.5 MB)
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| Item type | 国際農研デフォルトアイテムタイプ（フル）(1) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2025-12-01 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Deep learning using inductively coupled plasma spectroscopy spectra accurately predicts various soil physicochemical properties for soil diagnosis | |||||||||||
| 言語 | en | |||||||||||
| 作成者 |
Nakamura, Satoshi
× Nakamura, Satoshi
ORCID
0000-0002-0952-5618
× Imaya, Akihiro
ORCID
0009-0003-5727-2154
× Ikazaki, Kenta
ORCID
0000-0001-5460-8570
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| アクセス権 | ||||||||||||
| アクセス権 | open access | |||||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||||
| 権利情報 | ||||||||||||
| 権利情報 | © The Author(s) 2025 | |||||||||||
| 権利情報 | ||||||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by/4.0/deed/en | |||||||||||
| 権利情報 | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | |||||||||||
| 権利情報 | ||||||||||||
| 権利情報 | https://creativecommons.org/licenses/by/4.0/ | |||||||||||
| 主題 | ||||||||||||
| 主題 | Soil diagnosis, Tropics, Inductively coupled plasma spectroscopy, Deep learning | |||||||||||
| 内容記述 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | Improving soil diagnosis-based agriculture can help reduce fertilizer utilization and its environmental impact. However, conventional soil diagnostic methods are time-consuming and expensive, which limits their application. Although various rapid soil testing methods have been suggested, their accuracy remains largely unexplored. Herein, multiple soil parameters were predicted using the spectral data obtained from inductively coupled plasma (ICP) spectroscopy combined with deep learning. We analyzed 1941 soil samples from seven countries with various land-use patterns and histories. All ICP wavelength spectral data from the 1 M NH₄OAc extract were used for deep learning. The targeted soil properties included exchangeable bases (Ca, Mg, K, and Na); pH (H₂O); pH (KCl); electrical conductivity; available P (Bray1-P); exchangeable Al; cation exchange capacity; total carbon, nitrogen, clay, and sand contents. The predicted soil parameters were consistent with the observations. Most soil parameters had determination coefficients (R²) of > 0.9, and the lowest R² (0.81; total carbon) was relatively high. To our knowledge, this is the first study to demonstrate the prediction of multiple soil parameters using the ICP spectra of soil extracts. Our accurate predictions indicate that this method can be applied for precise, affordable, and rapid soil diagnosis, which could enhance soil-diagnosis-based agriculture. | |||||||||||
| 言語 | en | |||||||||||
| 出版者 | ||||||||||||
| 出版者 | Nature Portfolio | |||||||||||
| 日付 | ||||||||||||
| 日付 | 2025-10-13 | |||||||||||
| 日付タイプ | Accepted | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||
| 資源タイプ | journal article | |||||||||||
| 出版タイプ | ||||||||||||
| 出版タイプ | VoR | |||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
| 関連情報 | ||||||||||||
| 関連タイプ | isIdenticalTo | |||||||||||
| 識別子タイプ | DOI | |||||||||||
| 関連識別子 | https://doi.org/10.1038/s41598-025-24274-3 | |||||||||||
| 収録物識別子 | ||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||
| 収録物識別子 | 2045-2322 | |||||||||||
| 書誌情報 |
en : Scientific Reports 巻 15, p. 37753, 発行日 2025-11-20 |
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| 助成情報 | ||||||||||||
| 識別子タイプ | Crossref Funder | |||||||||||
| 助成機関識別子 | https://doi.org/10.13039/501100009472 | |||||||||||
| 助成機関名 | Japan International Research Center for Agricultural Sciences (JIRCAS)(en) | |||||||||||
| 研究課題番号URI | https://www.jircas.go.jp/en/program/prob/b6 | |||||||||||
| 研究課題番号 | 05a1B6 | |||||||||||
| 研究課題名 | Development of soil and crop management technologies to stabilize upland farming systems of African smallholder farmers(en) | |||||||||||
