AIとICP分析の融合による次世代型土壌診断技術を開発～全波長スペクトルで幅広い土壌特性を一括・高精度で予測～

2025-11-25 国際農林水産業研究センター

図1　本法による土壌診断と実測値の関係 (一例)
通常ICPで測定しない項目 (pH、CEC、全炭素) も高精度で予測されています。

＜関連情報＞

• https://www.jircas.go.jp/ja/release/2025/press202520
• https://www.jircas.go.jp/system/files/press/press202520.pdf
• https://www.nature.com/articles/s41598-025-24274-3

誘導結合プラズマ分光スペクトルを用いたディープラーニングは、土壌診断のためのさまざまな土壌の物理化学的特性を正確に予測します Deep learning using inductively coupled plasma spectroscopy spectra accurately predicts various soil physicochemical properties for soil diagnosis

Satoshi Nakamura,Akihiro Imaya & Kenta Ikazaki
Scientific Reports  Published:20 November 2025
DOI:https://doi.org/10.1038/s41598-025-24274-3

Abstract

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.