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Development of an Algorithm for Predicting Broiler Shipment Weight in a Smart Farm Environment

Author

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  • Bohyeok Lee

    (Graduate School of Artificial Intelligence, Jeonju University, Jeonju-si 55069, Republic of Korea)

  • Juwhan Song

    (Graduate School of Artificial Intelligence, Jeonju University, Jeonju-si 55069, Republic of Korea
    Artificial Intelligence Research Center, Jeonju University, Jeonju-si 55069, Republic of Korea)

Abstract

The weight information of broilers is important for understanding the growth progress of broilers and adjusting the breeding schedule, and predicting the broiler live weight at the time of shipment is an important task for producing high-quality broilers that meet consumer demand. To this end, we plan to analyze the broiler weight data automatically measured in a smart broiler house with an intelligent system and conduct a study to predict the weight until the time of shipment. To estimate the accurate daily body weight representative value of broiler body weight data, the K-means clustering method and the kernel density estimation method were applied, and the growth trends generated by each method were used as training data for the Prophet predictor, double exponential smoothing predictor, ARIMA predictor, and Gompertz growth model. The experimental results showed that the K-means + Prophet predictor model recorded the best prediction performance among the algorithm combinations proposed in this paper. The prediction results of the algorithm presented in this paper can analyze the growth progress of broilers in actual broiler houses and can be used as meaningful judgment data for adjusting the breeding schedule considering the time of shipment.

Suggested Citation

  • Bohyeok Lee & Juwhan Song, 2025. "Development of an Algorithm for Predicting Broiler Shipment Weight in a Smart Farm Environment," Agriculture, MDPI, vol. 15(5), pages 1-28, March.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:5:p:539-:d:1603372
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    3. Yumi Oh & Peng Lyu & Sunwoo Ko & Jeongik Min & Juwhan Song, 2024. "Enhancing Broiler Weight Estimation through Gaussian Kernel Density Estimation Modeling," Agriculture, MDPI, vol. 14(6), pages 1-20, May.
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