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Algorithme de prédiction en temps réel de la consommation alimentaire journalière chez la truie en lactation

Abstract : Developing algorithms able to predict daily feed intake is essential for implementing precision-feeding strategies in real time. Given the lack of a mechanistic model to predict feed intake in lactating sows, a new approach that combined real-time prediction with off-line learning of sow feeding behaviours was developed. A database of 39,090 lactations from 6 farms that contained the first 20 post-farrowing feed intake values was used to (i) identify groups of sows with similar feeding behaviour and (ii) test three functions to predict feed intake. The homogeneity of clusters obtained by off-line learning was assessed according to the Silhouette and Calinski-Harabasz scores. The prediction functions were evaluated by calculating mean error (ME) and root mean square error (RMSE) per day and per sow. The clusters with the best homogeneity were obtained by dividing the database into two groups. The trajectory of feed intake of the first group increased continuously during lactation, while that of the second plateaued from day 10 onwards. The ME per sow obtained for these two trajectories using the best function was-0.08 kg/d, and the corresponding RMSE was 1.06 kg/d. Although individual variability was high, the use of trajectories improved the prediction of feed intake. In practice, learning of trajectories may be recalculated regularly, while the real-time prediction function, which requires fewer computing resources, could be embedded into the smart feeder.
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https://hal.inria.fr/hal-03134418
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Submitted on : Monday, February 8, 2021 - 3:11:16 PM
Last modification on : Thursday, January 20, 2022 - 5:27:20 PM
Long-term archiving on: : Sunday, May 9, 2021 - 7:16:01 PM

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  • HAL Id : hal-03134418, version 1

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Raphaël Gauthier, Christine Largouët, Laurence Rozé, Jean-Yves Dourmad. Algorithme de prédiction en temps réel de la consommation alimentaire journalière chez la truie en lactation. 53. Journées de la Recherche Porcine, Ifip; Inrae, Feb 2021, En ligne, France. pp.127-132. ⟨hal-03134418⟩

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