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Integration of calibration and forcing methods for predicting timely crop states by using AquaCrop-OS model

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conference contribution
posted on 19.11.2019 by Tiangxiang Zhang, Jinya Su, Cunjia Liu, Wen-Hua Chen
This 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 - 111

Source

2nd UK-RAS Robotics and Autonomous Systems Conference, Loughborough, 2019

Publisher

UK-RAS Network

Version

AM (Accepted Manuscript)

Publication date

2019-01-24

Copyright date

2019

Language

en

Location

Loughborough, UK

Event dates

24th January 2019 - 24th January 2019

Depositor

Mr Tianxiang Zhang. Deposit date: 18 November 2019

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