Applying machine learning techniques on feeding behavior data for early estrus detection in dairy heifers

• Published in: Computers and electronics in agriculture Vol. 179; p. 105855
• Main Authors: Cairo, F.C., Pereira, L.G.R., Campos, M.M., Tomich, T.R., Coelho, S.G., Lage, C.F.A., Fonseca, A.P., Borges, A.M., Alves, B.R.C., Dorea, J.R.R.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.12.2020; Elsevier BV
• Subjects: Accuracy; agriculture; Algorithms; Animal behavior; Artificial insemination; Artificial neural network; Artificial neural networks; Bins; Computer vision; Drinking water; electronics; estrus; estrus detection; Evaluation; feed intake; Feeds; Generalized linear models; Heat detection; Holstein; Learning theory; Machine learning; Model accuracy; neural networks; Precision livestock; prediction; Random forest; Statistical models; Time series; time series analysis; Vision systems; Windows (intervals); Precision livestock; Random forest; Artificial neural network; Heat detection; Machine learning
• DOI: 10.1016/j.compag.2020.105855
• ISSN: 0168-1699

•Earlier and automated detection of estrus can improve on-farm decisions.•Estrus events altered feeding and drinking behavior pattern and feed intake.•Behavioral data generated by electronic bins have not been explored for estrus detection.•Machine learning algorithms were applied to feeding behavior data for estrus detection.•Feeding and drinking behavior data generated by electronic bins can be used to early predict estrus event. The recent advances in sensor technology have allowed accurate predictions of estrus events using animal behavior information. Behavioral variables generated by electronic feed and water bins have not been explored as potential predictors for estrus detection. The objectives of this study were: (i) to evaluate the effect of estrus expression on feed intake and animal behavior (feeding and drinking) and (ii) to develop and evaluate predictive approaches to detect estrus expression using electronic feed and water bins data. Feed intake, animal behavior, and estrus events were measured in 57 Holstein × Gyr heifers (374 ± 21.2 kg and 22.6 ± 0.60 months). Previous to each estrus event, the following covariates were computed: total feed intake (FI, as-fed basis), number of visits at the feed bins (VF) and water bins (VW), time spent eating (TE), and time spent drinking water (TD). Three predictive approaches were evaluated: Generalized Linear Models (GLM), Artificial Neural Network (ANN), and Random Forest (RF). Twelve covariate sets were established to investigate: (ii.a) the prediction quality for estrus detection when long (−174 to 0 h) or short (−24 to 0 h) time series were used as predictors (6 h of time window, with estrus event at 0 h); (ii.b) the ability of machine learning algorithms to predict estrus 6 and 12 h in advance; and (ii.c) the predictive quality for estrus detection when only feeding and drinking behavior data (without intake variables) were included as predictors. The predictive approaches were evaluated through Leave-One-Out Cross-validation. Estrus events altered feeding and drinking behavior patterns, and feed intake. ANN, RF, and GLM presented similar accuracies within covariate sets. There was no benefit of using longer time series for estrus detection. Earlier detection of estrus event (6 and 12 h in advance) reduced model accuracy compared to predictions performed at 0 h. However, ANN and RF showed accuracy values ranging between 75.7% and 96.5%, which indicates a great potential for early estrus detection. The exclusion of feed intake data of the covariate sets did not reduce the accuracy, sensitivity, and specificity of the models for estrus detection. These findings suggest that behavioral data can early predict estrus events, which could be incorporated in sensor technologies capable of generating behavioral information, such as electronic bins, wearable sensors, and computer vision systems.