Ms. Concepción Mira Rodado Profile

Concepción Mira Rodado

at TEPRO Consultores Agrícolas SL

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Proceedings Article | 12 September 2021 Presentation + Paper

MORERA: latest Earth observation system to translate big data to agriculture

Ángel Alvaro, Jose Sobrino, Concepción Mira, Victoria Gonzalez-Dugo, Tomás Belenguer, Andrés Cifuentes, Javier Moreno

Proceedings Volume 11856, 1185603 (2021) https://doi.org/10.1117/12.2596842

KEYWORDS: Agriculture, Sensors, Artificial intelligence, Remote sensing, Evolutionary algorithms, Data modeling, Climate change, Calibration, Satellites, Nanoelectromechanical systems

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The MORERA program has recently been selected as one of the “Missions Science and Innovation” from the Spanish CDTI, an innovative program targeting solutions for deep social problems through innovation. The main Spanish industry is Agriculture (11% GDP), but this sector is threatened by climate change, as 34% of the Spanish irrigated surface is considered out of balance. Difficulty of providing useful and fully processed information to the end-users for supporting their decisions severely affect the optimization of the resources. Well informed decisions optimize resources and costs, maximizing productivity. To solve this problem, MORERA involves in a unique project the complete value chain, from sensor to user, thanks to a solid consortium, and it is based on three pillars: - Final personalized irrigation requirements that will be directly provided to the user using a mobile device. - Artificial intelligence techniques will be used to combine all relevant data to build a final watering recommendation. - A compact and highly specific freeform optical instrument will be used to estimate evapotranspiration data at farm level with required TIR bandwidth and spatial resolution. Since no present instrument fulfills these requirements, it will be developed in the framework of the project. The MORERA concept can be extrapolated to many remote sensing applications, and to take advantage of this, it has been conceived as a modular system, where each module may be adapted with minor impact. This first system is focused on providing precise irrigation and fertilization recommendations, as well as self-learning yield estimations.

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