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Dataset accompanying "Investigating the limits of spectroscopy for the estimation of foliar N and P in apple": Hyperspectral reflectance, foliar nutrient concentrations and associated metadata.

Data in brief · 4 Dec 2025 · 10.1016/j.dib.2025.112343

Abstract

This dataset was generated to support research investigating the use of hyperspectral reflectance for the estimation of foliar nitrogen (N) and phosphorus (P) concentrations in apple ( Malus domestica ) trees. This article and the dataset it describes accompany an original research article submitted to Computers and Electronics in Agriculture entitled "Investigating the limits of spectroscopy for the estimation of foliar N and P in apple" [1]. Data were collected from a controlled potted experiment involving 150 'Golden Delicious' apple trees grown under varying nutrient supply regimes, including full nutrient supply, nitrogen- and phosphorus-deficient treatments, and trees infected with ' Candidatus Phytoplasma mali'. The experiment was conducted over the 2023 growing season at the Laimburg Research Centre in South Tyrol, Italy. All data and the accompanying code for its analysis is freely available in the associated GitHub repository [2]. Spectral data were collected using the Spectral Evolution SR-3500 field spectroradiometer with an attached leaf clip, producing high-resolution hyperspectral reflectance profiles (350-2500 nm) from the adaxial surface of fully expanded leaves. A total of 1189 leaf spectra were recorded and were matched to chemically analysed leaf samples. Corresponding foliar N and P concentrations (and others) were determined through laboratory analysis using the Dumas combustion method for nitrogen and ICP-OES following acid digestion for phosphorus. The dataset includes metadata detailing tree treatments, sampling dates, infection status, and shoot growth metrics. Additionally, R scripts used for data processing, spectral pre-treatment (including multiplicative scatter correction and Savitzky-Golay derivatives), feature selection (VIP and mRMR), and model development are provided. The dataset is suitable for reuse in the development and benchmarking of spectral models for nutrient estimation, especially in the context of field-based or remote sensing applications in horticulture. Its wide range of foliar nutrient values, inclusion of multiple physiological stresses, and detailed documentation make it a valuable resource for researchers working in precision agriculture, plant phenotyping, chemometrics, and hyperspectral data analysis.

Plant phenotyping relevance

リンゴ葉のN・P濃度という植物生理形質を対象に、ハイパースペクトル反射データ、化学分析値、前処理・モデル開発コードを含む再利用可能なデータセットであり、植物フェノタイピング手法の開発・ベンチマークに直接資する。

abstractThis dataset was generated to support research investigating the use of hyperspectral reflectance for the estimation of foliar nitrogen (N) and phosphorus (P) concentrations in apple
abstractThe dataset is suitable for reuse in the development and benchmarking of spectral models for nutrient estimation

Code and data availability

The authors publicly release the paper's own hyperspectral leaf spectra (.sed files), matched foliar N/P concentrations, metadata, and R analysis scripts via a GitHub repository (also archived with Zenodo DOI 10.5281/zenodo.15600557), with explicit public availability and no registration required.

Datasetpublic

Data accessibility Repository name: Github Data identification number: DOI 10.5281/zenodo.15600557 Direct URL to data: https://github.com/HyperspectralCameron/Investigating-the-Limits-of-Spectroscopy-for-the-Estimation-of-Foliar-N-and-P-in-Apple.gitInstructions for accessing these data: All data and code are publicly available through the GitHub repository listed above. The repository includes raw spectral files (.sed), metadata files, and R scripts for pre-processing, modelling, and visualisation. No registration or authentication is required.

Open resource ↗GitHub · DOI 10.5281/zenodo.15600557 · html-lines:84-123
Codepublic

All data and the accompanying code for its analysis is freely available in the associated GitHub repository [2].

Open resource ↗GitHub · html-lines:1-83

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