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.