Predicting Soil Properties from Hyperspectral Satellite Images
The AI4EO HYPERVIEW challenge seeks machine learning methods that predict agriculturally relevant soil parameters (K, Mg, P2O5, pH) from airborne hyperspectral images. We present a hybrid model fusing Random Forest and K- nearest neighbor regressors that exploit the average spectral reflectance, as well as derived features such as gradients, wavelet coefficients, and Fourier transforms. The solution is computationally lightweight and improves upon the challenge baseline by 21.9%, with the first place on the public leader- board. In addition, we discuss neural network architectures and potential future improvements.
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Datasets
HYPERVIEW
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Seeing Beyond the Visible | HYPERVIEW | RF + KNN | normalized MSE | 0.78113 | # 1 |
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