Communication Dans Un Congrès Année : 2024

Data preparation for prediction and anomaly detection in precision beekeeping

Sid Ahmed Metidji
  • Fonction : Auteur
Jean-Charles Huet
Lamine Bougueroua

Résumé

Faced with a major decline in the population of a variety of pollinators essential to our food security, it is essential to study their behavior and their evolution. The proposed approach is illustrated as an example of application in the beekeeping sector. Indeed, this sector is affected by a crisis due to the disappearance of bees and the different actors need support to make their decisions. We hope to offer new tools to help beekeepers predict the evolution of their hives, as well as tools to detect any anomalies. To meet these challenges, we have been looking for usable hive data. We have found data on line over a long period, but it needs to be completed and resampled. The dataset covers weight, internal humidity and internal temperature. In this article, we describe how these data have been prepared for prediction and anomaly detection by adding the external data like external temperature. Time-series resampling and interpolation techniques were used to help in this process. At the end of this process, the datasets are structured, cleaned and completed: they are ready to use in intelligent models.

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Dates et versions

hal-04811223 , version 1 (29-11-2024)

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Sid Ahmed Metidji, Jean-Charles Huet, Lamine Bougueroua. Data preparation for prediction and anomaly detection in precision beekeeping. 28th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2024), KES, Sep 2024, Séville, Spain. pp.4008-4017, ⟨10.1016/j.procs.2024.09.175⟩. ⟨hal-04811223⟩
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