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In this paper we propose an adaptive deep neural architecture for theprediction of multiple soil characteristics from the analysis of hyperspectralsignatures. The proposed method overcomes the limitations of previous methodsin 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 theestimation of a given variable; (iii) it is based on a flexible neuralarchitecture capable of automatically adapting to the spectral library underanalysis. The proposed architecture is experimented on LUCAS, a largelaboratory dataset and on a dataset achieved by simulating PRISMA hyperspectralsensor. 'Results, compared with other state-of-the-art methods confirm theeffectiveness of the proposed solution.

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