Time Series Forecast Model Application for Broiler Weight Prediction using Environmental Factors
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| Publicado no: | The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings (2022) |
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The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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| Acesso em linha: | Citation/Abstract |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 2759395348 | ||
| 003 | UK-CbPIL | ||
| 024 | 7 | |a 10.1109/ICECCME55909.2022.9988243 |2 doi | |
| 035 | |a 2759395348 | ||
| 045 | 2 | |b d20220101 |b d20221231 | |
| 084 | |a 228229 |2 nlm | ||
| 100 | 1 | |a Birzniece, Ilze |u Riga Technical University,Department of Artificial Intelligence and Systems Engineering,Riga,Latvia | |
| 245 | 1 | |a Time Series Forecast Model Application for Broiler Weight Prediction using Environmental Factors | |
| 260 | |b The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |c 2022 | ||
| 513 | |a Conference Proceedings | ||
| 520 | 3 | |a Conference Title: 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)Conference Start Date: 2022, Nov. 16 Conference End Date: 2022, Nov. 18 Conference Location: Maldives, MaldivesPredicting the growth of broiler chickens is an essential task in the poultry industry. The data used in the study include both the production environmental indicators (temperature, gas concentration, humidity, and others) and the growth rates of poultry (weight, amount of feed consumed, fall) by analyzing their correlations throughout several production cycles. The proposed approach includes several stages, starting with data pre-processing, broiler weight data augmentation, comparison with a reference model, definition, and detection of uncomfortable and dangerous environmental conditions. For the model-building part, the Long short-term memory (LSTM) artificial neural network is applied. The validation of the forecasting model is done by comparing the forecasted weight provided by the model with the actual weight measurements during the randomly selected bird life cycle and varied environmental conditions. The acquired results showed that the provided forecast accuracy is sufficient for production management. | |
| 653 | |a Production management | ||
| 653 | |a Mathematical models | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Poultry | ||
| 653 | |a Mechatronics | ||
| 653 | |a Environmental indicators | ||
| 653 | |a Environmental factors | ||
| 653 | |a Environmental conditions | ||
| 653 | |a Economic | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Andersone, Ilze |u Riga Technical University,Department of Artificial Intelligence and Systems Engineering,Riga,Latvia | |
| 700 | 1 | |a Nikitenko, Agris |u Riga Technical University,Department of Artificial Intelligence and Systems Engineering,Riga,Latvia | |
| 700 | 1 | |a Balina, Signe |u University of Latvia,Department of Economics,Riga,Latvia | |
| 700 | 1 | |a Kikans, Andris |u Datorzinibu centrs,Riga,Latvia | |
| 773 | 0 | |t The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings |g (2022) | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2759395348/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |