This work proposes a computational algorithm based on a spectral selection NIR range for soil organic carbon percentage estimation. The results show that the proposed algorithm outperforms traditional methods by up to 0.5 R2

 This work proposes an automatic acquisition protocol that minimizes the acquired data spectral variability in less acquisition time. Experiments show that the proposed automated spectral acquisition protocol on agricultural soil samples reduces the spectral variability up to 22%, using more than 1520 acquired spectral signatures at each experiment, obtaining higher quality in the resulting spectral data.

A technical visit was made within the framework of the Ecosnord project to share academic knowledge and update the project process.

 This work proposes a coded aperture spectral compressive video methodology that allows encoding the spectro-temporal dimensions by using an active binary codification element and a tunable filter. Through the proposed optical system both the spectral and temporal dimensions can be modulated over an integration time using only two optical elements. In order to resolve the inverse problem, an alternating direction multipliers method (ADMM) plug and play (PnP) reconstruction algorithm was modified to render the multidimensional data. Simulations show that the proposed sensing methodology allows spectral video reconstructions from a single compressed measurement. Moreover, simulations show reconstruction results up to 28 dB and 0.86 in PSNR and SSIM metrics, respectively, with compression rates of 99%.