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Data assimilation for integration of data obtained by wireless sensor networks and a dynamic crop growth model

Grant number: 18/12050-6
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): October 01, 2018
Effective date (End): August 27, 2021
Field of knowledge:Interdisciplinary Subjects
Principal researcher:Luiz Henrique Antunes Rodrigues
Grantee:Monique Pires Gravina de Oliveira
Home Institution: Faculdade de Engenharia Agrícola (FEAGRI). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil


Dynamic crop growth models combined with the vast body of available data have been seen as part of the response to the problem of a more resource efficient Agriculture. Although such models require calibration steps without which their predictive performance may be insufficient to aid in decision making, real-time monitoring may circumvent this need. Dynamic models and satellite images have been combined through the data assimilation technique to reduce prediction errors of state variables related to crop canopy or soil properties. In protected environments, however, where the use of models and sensors allows for monitoring and automation of control systems so that it is possible to optimize environmental conditions aiming at greater profitability of production, there are no applications of assimilation of monitoring data. The objectives of this project are then to determine if it is possible to perform data assimilation with environmental and crop sensing data in a greenhouse, as well as to determine the temporal resolution necessary for its realization and the technological degree required for the approach to be replicated under production conditions. For this, the meteorological factors of a greenhouse with the cultivation of tomato and the growth of the vegetables will be monitored. Through state estimation techniques such as the Extended Kalman Filter and the Ensemble Kalman Filter, data assimilation will be performed on the reduced TOMGRO model. In this way, the growth of the crop may be better characterized, possibly leading to better decision making in the use of water and energy in production. (AU)

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