Automated Early Detection of Drops in Commercial Egg Production Using Neural Networks
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Automated Early Detection of Drops in Commercial Egg Production Using Neural NetworksDate
2017-10-17Citation
Ramírez-Morales I, Fernández-Blanco E, Rivero D, Pazos A. Automated early detection of drops in commercial egg production using neural networks. Br Poult Sci. 2017;58(6):739-747
Abstract
[Abstract] 1. The purpose of this work was to support decision-making in poultry farms by performing automatic early detection of anomalies in egg production.
2. Unprocessed data were collected from a commercial egg farm on a daily basis over 7 years. Records from a total of 24 flocks, each with approximately 20 000 laying hens, were studied.
3. Other similar works have required a prior feature extraction by a poultry expert, and this method is dependent on time and expert knowledge.
4. The present approach reduces the dependency on time and expert knowledge because of the automatic selection of relevant features and the use of artificial neural networks capable of cost-sensitive learning.
5. The optimum configuration of features and parameters in the proposed model was evaluated on unseen test data obtained by a repeated cross-validation technique.
6. The accuracy, sensitivity, specificity and positive predictive value are presented and discussed at 5 forecasting intervals. The accuracy of the proposed model was 0.9896 for the day before a problem occurs.
Keywords
Farming systems
Laying hens
Machine learning
Modelling
Production drops
Laying hens
Machine learning
Modelling
Production drops
Editor version
Rights
Atribución 3.0 España This is an accepted manuscript of an article published by Taylor & Francis in British Poultry Science on 2017, avaliable online at Taylor & Francis Online.
ISSN
0007-1668
1466-1799
1466-1799