Volume 67, 2020CEMRACS 2018 - Numerical and mathematical modeling for biological and medical applications: deterministic, probabilistic and statistical descriptions
|Page(s)||61 - 71|
|Published online||09 June 2020|
Construction of a statistical learning tool based on ordinary differential equations to model the digestive behaviour of ross chickens
1 See-d, 6, rue Henri Becquerel -CP 101, 56038 Vannes Cedex, France
2 Université Bretagne Sud, Laboratoire de Mathématiques de Bretagne Atlantique,UMR CNRS 6205, Campus de Tohannic, Vannes, France
3 Université de Picardie Jules Verne, LAMFA UMR 7352, 33 Rue St Leu, 80000 Amiens, France
4 Sorbonne Université, PR UPMC, Laboratoire de Biologie Intégrative des Modèles Marins, Place Georges Teissier, 29680 Roscoff, France
5 Universitè Paris 13 -Galilèe Institute -LAGA, 99 avenue Jean-Baptiste Clèment - 93430 Villetaneuse, France
Being able to monitor and forecast farm animal performances is a strategic problem in the agronomy industry. We use a Data-Model Coupling approach to build a biomimetic Statistical Learning tool taking into account some aspects of the biological dynamics of the animal body. The objective is to build a tool which is able to assimilate data about daily feed consumption and measured performances.
The model encompasses several sub-models corresponding to compartments and permitting to mimic a kinetic process divided into several steps. Each sub-model contains parameters which can be learnt by using an optimization algorithm and data.
The goal of the ﬁrst application of the model on ﬁeld data was to simulate and predict the growth of chickens.
An experiment was performed during 70 days to collect every day the feed consumption and the weight gain of a male and a female chickens. After the learning of the model parameters, the model shows a very good approximation of the chicken’s weight evolution over time.
© EDP Sciences, SMAI 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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