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Advisor(s)
Abstract(s)
In Portugal, the olive groves soil has been subject to severe degradation due to harsh farming techniques combined with uncontrollable conditions such as climate changes and steep terrain orography. In this framework, this tendency must be reverted by adopting different farm management policies. The MAN4HEALTH project address this subject in two-folds: one, at an agronomic level, where the soil will be
protected by growing a layer of indigenous plants and other, that resort to the soil’s digitization in order to improve the deployment of fertilizers. This paper addresses the latter and aims to provide an overall description of the architecture of a predictive control system based on the soil’s digital twin. In this control paradigm, an artificial intelligence layer will follow the entire cultivation process through the development of a soil’s digital twin. This model will be able to describe the spatial and temporal dynamics of fertilization policies and be included within a model predictive control strategy in order to both decrease the concentration of
chemicals released into the soil and promote the economic income of the farmer. In particular, this paper tackles one of the phases of this project where soil digitization must be carried out in order to feed the data to
the digital twin. In particular, the description of the sensor network and the data management architecture.
Description
Keywords
Smart farming Agriculture digitization Digital twins Sensor networks Model predictive control
Citation
Silva, Letícia; Giugge, Romina; Rodríguez-Sedano, Francisco J.; Baptista, Paula; Coelho, João Paulo (2022). Promoting olive groves’s soil quality by a digital twin’s predictive based control: the sensor’s network. In 15th APCA International Conference on Automatic Control and Soft Computing, CONTROLO 2022. Caparica. 930, p. 241 - 250. ISBN 978-3-031-10047-5