Application of random forest classification to predict daily oviposition events in broiler breeders fed by precision feeding system

• Published in: Computers and electronics in agriculture 2020-08, Vol.175, p.105526, Article 105526
• Main Authors: You, Jihao, van der Klein, Sasha A.S., Lou, Edmond, Zuidhof, Martin J.
• Format: Article
• Language: English
• Subjects: Algorithms; Binary classification; Body weight; Classification; Datasets; Egg-laying event; Machine learning; Model accuracy; Model testing; Radio frequency identification; Recording; Variable selection
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2020.105526

•A machine learning method was used to predict oviposition events.•A random forest classification model was developed for prediction.•Birds were fed by a precision feeding system which can record visiting information.•Egg laying events were linked to real-time behaviour of free-run broiler breeders.•The overall accuracy of the prediction model was around 85%. In group-housed poultry, hormone and environment modulated variability in the processes of follicle maturation and egg formation make it difficult to predict a daily egg-laying event (oviposition). Recording daily egg laying events has required individual cages or expensive technology such as RFID equipped nests or labor intensive trap nests. The current study implemented the random forest classification algorithm to predict oviposition events of 202 free run Ross 708 broiler breeder hens fed by a precision feeding system from week 21 to 55, based on a dataset recording information of all visits to the station. The raw dataset from the precision feeding system was processed for 6 classes of features (34 features in total) in relation to feeding activity and real-time body weight of birds. The dataset of the features was then combined with a corresponding daily individual oviposition record. The processed data were shuffled and separated into 2 subsets: 90% for training, and 10% for testing. Important features were selected using random forest-recursive feature elimination with 5-fold cross-validation, and 28 features were selected to build a random forest classification model. Overall accuracy of the model using the testing samples was 0.8482, and out-of-bag score was 0.8510. Precision (a measure of purity in retrieving) of no egg-laying and egg-laying, recall (a measure of completeness in retrieving) of no egg-laying and egg-laying were 0.8814, 0.8090, 0.8520 and 0.8453, respectively. The Kappa coefficient of the model was 0.6931, indicating substantial agreement (substantial agreement range: 0.61–0.80). This model was able to identify whether a free run broiler breeder laid an egg or not on a certain day during the laying period with around 85% accuracy.