  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.
