AI4EO Hyperview: A Spectralnet3d and Rnnplus Approach for Sustainable Soil Parameter Estimation on Hyperspectral Image Data

Abstract

The goal of the #Hyperview challenge is to use Hyperspectral Imaging (HSI) to predict the soil parameters potassium (K), phosphorus pentoxide (P2O5), magnesium (Mg) and the pH value. These are relevant parameters to determine the need of fertilization in agriculture. With this knowledge, fertilizers can be applied in a targeted way rather than in a prophylactic way which is the current procedure of choice.In this context we introduce two different approaches to solve this regression task based on 3D CNNs with Huber loss regression (SpectralNet3D) and on 1D RNNs. Both methods show distinct advantages with a peak challenge metric score of 0.808 on provided validation data.