  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 permits to backtrace the spectral bands that most contribute to the
estimation of a given variable; (iii) it is based on a flexible neural
architecture capable of automatically adapting to the spectral library under
analysis. 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.
