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Related Experiment Video

Updated: Mar 29, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

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Published on: June 18, 2021

Explainable AI-Based Hyperspectral Classification Reveals Differences in Spectral Response over Phenological Stages.

Rameez Ahsen¹, Pierpaolo Di Bitonto¹, Pierfrancesco Novielli^1,2

¹Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy.

|March 27, 2026

Summary

Optimizing nitrogen fertilization in durum wheat is crucial for yield and environmental protection. Hyperspectral sensing with explainable AI accurately monitors crop nitrogen status, especially at the plot level.

Keywords:

canopy reflectance feature attribution fertilizer management machine learning precision agriculture stratification strategies

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Area of Science:

Agricultural Science
Remote Sensing
Data Science

Background:

Optimizing nitrogen (N) fertilization is vital for durum wheat production, balancing yield and grain quality with environmental sustainability.
Hyperspectral sensing offers a non-destructive method for monitoring crop N status, but faces challenges from high-dimensional data and phenological variations.
Understanding spatial autocorrelation in field data is crucial for accurate nitrogen classification.

Purpose of the Study:

To evaluate hyperspectral-based nitrogen status classification in durum wheat under Mediterranean conditions.
To identify key spectral regions contributing to nitrogen classification using explainable artificial intelligence (AI).
To assess the influence of nitrogen stratification strategies and phenological stages on classification accuracy.

Main Methods:

A field experiment in Southern Italy utilized ten N fertilization rates (0-180 kg N ha^-1).
Canopy reflectance was measured at booting and heading stages using georeferenced sampling.
Random Forest, SVM-RBF, and XGBoost classifiers were employed with spatially independent cross-validation, and SHAP analysis identified key spectral regions.

Main Results:

Classification accuracy varied significantly with nitrogen stratification strategy and phenological stage.
Binary Low-High stratification yielded the highest sample-level accuracy (up to 0.78 at booting).
Plot-level aggregation substantially improved performance, reaching 1.00 at heading; red, red-edge, and near-infrared wavelengths were key predictors.

Conclusions:

Explainable AI provides a robust framework for hyperspectral nitrogen monitoring in durum wheat.
Nitrogen stratification strategies and phenological stage are critical factors for accurate classification.
Plot-level analysis enhances the reliability of hyperspectral-based nitrogen status assessment.