A deep scalable neural architecture for soil properties estimation from spectral information

Abstract

In this paper we propose an adaptive deep neural architecture for the prediction of multiple soil characteristics from the analysis of hyperspectral signatures. The proposed method overcomes the limitations of previous methods in the state of art: (i) it allows to predict multiple soil variables at once; (ii) it is based on a flexible neural architecture capable of automatically adapting to the spectral library under analysis. As an additional feature, our method permits to backtrace the spectral bands that most contribute to the estimation of a given variable. The proposed architecture is experimented on LUCAS, a large laboratory dataset and on a dataset achieved by simulating PRISMA hyperspectral sensor. Results, compared with other state-of-the-art methods confirm the effectiveness of the proposed solution. In fact, the proposed method achieves an overall R2 of about 0.75. In detail, pHCaCl2 and CaCO3 are predicted with an R2 above 0.90, pHH2O, OC, N, clay, sand, CEC, silt are predicted with an R2 between 0.89 and 0.70, K and P with an R2 respectively of 0.54 and 0.37. The proposed method overcomes, in terms of R2, the state of the art of about 15% on the LUCAS dataset and of about 24% on the PRISMA simulated dataset.