This paper presents a case of study of a IoT cloud plat-form composed of a microservices architecture that has been developed to integrate the CRITERIA-1D into the ZENTRA cloud. CRITERIA-1D is an open-source agro-hydrological model developed by ARPAE simulating one-dimensional soil water fluxes, crop development, and crop water needs. CRITERIA-1D comes with a default set of crops and soils that can be used or tuned for a specific scenarios. Taking as input the weather forecasts (i.e., temperatures and precipitations), the model can be used to predict the soil water content and soil water potential at different depths. Along with the design of the implemented solution, this paper presents the process of tuning crop and soil parameters for a specific use case. The results show that the tuned model estimates very well with respect to the measures observed by sensors, paving the way to its application within the larger context of the METER's ZENTRA cloud.
Predict soil moisture into the future: On the integration of CRITERIA-1D into ZENTRA cloud / De Faria B.T.; Aguzzi C.; Bates T.; Campbell C.; Tomei F.; Bittelli M.; Roffia L.. - ELETTRONICO. - (2021), pp. 331-335. (Intervento presentato al convegno 3rd IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor 2021 tenutosi a ita nel 2021) [10.1109/MetroAgriFor52389.2021.9628475].
Predict soil moisture into the future: On the integration of CRITERIA-1D into ZENTRA cloud
Aguzzi C.;Bittelli M.;Roffia L.
2021
Abstract
This paper presents a case of study of a IoT cloud plat-form composed of a microservices architecture that has been developed to integrate the CRITERIA-1D into the ZENTRA cloud. CRITERIA-1D is an open-source agro-hydrological model developed by ARPAE simulating one-dimensional soil water fluxes, crop development, and crop water needs. CRITERIA-1D comes with a default set of crops and soils that can be used or tuned for a specific scenarios. Taking as input the weather forecasts (i.e., temperatures and precipitations), the model can be used to predict the soil water content and soil water potential at different depths. Along with the design of the implemented solution, this paper presents the process of tuning crop and soil parameters for a specific use case. The results show that the tuned model estimates very well with respect to the measures observed by sensors, paving the way to its application within the larger context of the METER's ZENTRA cloud.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
