A deep scalable neural architecture for soil properties estimation from spectral information
This work addresses soil property estimation for agricultural or environmental applications, but appears incremental as it builds on existing neural methods with specific enhancements.
The paper tackles the problem of predicting multiple soil characteristics from hyperspectral data by proposing an adaptive deep neural architecture that overcomes previous limitations, achieving results that confirm its effectiveness compared to state-of-the-art methods.
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.