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Integration of calibration and forcing methods for predicting timely crop states by using AquaCrop-OS model
conference contribution
posted on 2019-11-19, 13:47 authored by Tianxiang Zhang, Jinya Su, Cunjia LiuCunjia Liu, Wen-Hua ChenWen-Hua ChenThis paper presents a framework for predicting canopy states in real time by adopting a recent MATLAB based crop model: AquaCrop-OS. The historical observations are firstly used to estimate the crop sensitive parameters in Bayesian approach. Secondly, the model states will be replaced by updating remotely sensed observations in a sequential way. The final predicted states will be in comparison with the groundtruth and the RMSE of these two are 39.4155 g/ 𝒎𝟐 (calibration method) and 19.3679 g/𝒎𝟐
(calibration with forcing method) concluding that the system is capable of predicting the crop status timely and improve the performance of calibration strategy.
Funding
Newton Fund UK-China Agri-Tech Network Plus managed by Rothamsted Research on behalf of Science and Technology Facilities Council (STFC)
Chinese Scholarship Council (CSC)
History
School
- Aeronautical, Automotive, Chemical and Materials Engineering
Department
- Aeronautical and Automotive Engineering
Published in
UK-RAS19 Conference Proceedings "Embedded Inteligence: Enabling & Supporting RAS Technologies"Pages
108 - 111Source
2nd UK-RAS Robotics and Autonomous Systems Conference, Loughborough, 2019Publisher
UK-RAS NetworkVersion
- AM (Accepted Manuscript)
Publication date
2019-01-24Copyright date
2019Publisher version
Language
- en
Location
Loughborough, UKEvent dates
24th January 2019 - 24th January 2019Depositor
Tianxiang Zhang. Deposit date: 18 November 2019Usage metrics
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