A supervised machine learning method to detect anomalous real-time broiler breeder body weight data recorded by a precision feeding system

A precision feeding (PF) system is an intelligent computer-controlled feeding system that can be used to feed individual broilers, breeders or layers automatically based on measuring real-time body weight (BW). Vast amounts of real-time BW data can be generated every day when birds visit a PF station. However, anomalous observations occurred in real-time BW observations, which were caused by multiple birds entering the station at the same time, upward or downward variation in scale measurement in the recorded data due to the movement of the bird, or a misread for radio frequency identification tag. Known anomalous data should be removed because they have a negative impact on the interpretation of the data. Manually cleaning the anomalies is accurate, but it is time-consuming and labor-intensive. Statistical methods and unsupervised machine learning methods are effective in detecting anomalies to some extent because they just check data distribution. The current study reported a supervised machine learning method to detect anomalies in real-time BW recorded by the PF system. Real-time BW data of 5 broiler breeders from day 15 to 306 were checked and the anomalies were manually labeled. Variables regarding the statistical distribution of data and features regarding the feeding activity recorded by the PF system in each day were extracted from the dataset. Among the 4 machine learning algorithms including k-nearest neighbor (KNN), random forest classifier (RF), support vector machine (SVM), and artificial neural network (ANN), RF produced the highest F1 score (0.9712) and area under the precisionrecall curve (0.9948). Compared with 4 other common anomaly detection methods including Z-scores, interquartile range (IQR), density-based spatial clustering of applications with noise (DBSCAN), and local outlier factor (LOF), RF had a higher average F1 score (0.9448), which indicated that RF was a more effective anomaly detection algorithm for this type of data.