Early Warning in Egg Production Curves from Commercial Hens: a SVM Approach

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http://hdl.handle.net/2183/18451
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España
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Early Warning in Egg Production Curves from Commercial Hens: a SVM ApproachDate
2016-01-02Citation
Ramírez Morales I, Rivero Cebrián D, Fernández Blanco E, Pazos Sierra A. Early warning in egg production curves from commercial hens: a SVM approach. Comput Electron Agric. 2016;121:169-179
Abstract
[Abstract] Artificial Intelligence allows the improvement of our daily life, for instance, speech and handwritten text recognition, real time translation and weather forecasting are common used applications. In the livestock sector, machine learning algorithms have the potential for early detection and warning of problems, which represents a significant milestone in the poultry industry. Production problems generate economic loss that could be avoided by acting in a timely manner.
In the current study, training and testing of support vector machines are addressed, for an early detection of problems in the production curve of commercial eggs, using farm’s egg production data of 478,919 laying hens grouped in 24 flocks.
Experiments using support vector machines with a 5 k-fold cross-validation were performed at different previous time intervals, to alert with up to 5 days of forecasting interval, whether a flock will experience a problem in production curve. Performance metrics such as accuracy, specificity, sensitivity, and positive predictive value were evaluated, reaching 0-day values of 0.9874, 0.9876, 0.9783 and 0.6518 respectively on unseen data (test-set).
The optimal forecasting interval was from zero to three days, performance metrics decreases as the forecasting interval is increased. It should be emphasized that this technique was able to issue an alert a day in advance, achieving an accuracy of 0.9854, a specificity of 0.9865, a sensitivity of 0.9333 and a positive predictive value of 0.6135. This novel application embedded in a computer system of poultry management is able to provide significant improvements in early detection and warning of problems related to the production curve.
Keywords
Early warning
Drop in egg production
Poultry management
Support vector machines
Machine learning
Drop in egg production
Poultry management
Support vector machines
Machine learning
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Atribución-NoComercial-SinDerivadas 3.0 España
ISSN
0168-1699
1872-7107
1872-7107