Abstract
Simple SummaryMonitoring animal activity in production systems is an important tool for obtaining information on health, production, and reproduction. In this study, we evaluated the use of accelerometers with different strategies to predict the grazing behavior of Nelore cattle. This research was conducted in an environment both more challenging and representative of the practices adopted in livestock production systems in Brazil. The results of this study showed that the use of the Random Forest algorithm, together with techniques for resampling the training data of the models, classified the studied behaviors with high accuracy, especially for important, and less frequent activities such as water consumption frequency.Knowledge of animal behavior can be indicative of the well-being, health, productivity, and reproduction of animals. The use of accelerometers to classify and predict animal behavior can be a tool for continuous animal monitoring. Therefore, the aim of this study was to provide strategies for predicting more and less frequent beef cattle grazing behaviors. The behavior activities observed were grazing, ruminating, idle, water consumption frequency (WCF), feeding (supplementation) and walking. Three Machine Learning algorithms: Random Forest (RF), Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC) and two resample methods: under and over-sampling, were tested. Overall accuracy was higher for RF models trained with the over-sampled dataset. The greatest sensitivity (0.808) for the less frequent behavior (WCF) was observed in the RF algorithm trained with the under-sampled data. The SVM models only performed efficiently when classifying the most frequent behavior (idle). The greatest predictor in the NBC algorithm was for ruminating behavior, with the over-sampled training dataset. The results showed that the behaviors of the studied animals were classified with high accuracy and specificity when the RF algorithm trained with the resampling methods was used. Resampling training datasets is a strategy to be considered, especially when less frequent behaviors are of interest.
Highlights
Monitoring and accessing animal behavior are important tasks in ensuring the success of an animal production system
All the procedures used followed the Ethical Principles for Animal Experimentation stated by the National Council for Animal Experiment Control and were approved by the Ethics Committee for Use of Animals (CEUA) of Universidade Estadual Paulista (Unesp), under protocol #001081/2019
The lowest overall accuracy was observed in the Naïve Bayes Classifier (NBC) model model trained trained with with over-sampled over-sampled records, records, which which was was the the only only method method in in which which the the over-sampling showed negative effects on behavior classification, since for the over-sampling showed negative effects on behavior classification, since for the Random Forest (RF) and algorithms the training with over-sampled data promoted the highest results results (Table 1)
Summary
Monitoring and accessing animal behavior are important tasks in ensuring the success of an animal production system. How much time animals spend lying down can help estrus detection in cows [3]. By observing how animals walk or how much time they spend lying down can help to detect and prevent lameness [4]. Monitoring animal behavior is often carried out by human observation or video monitoring, which makes it difficult to obtain data, due to the demand for human resources [5], as well as the fact that sometimes access to the animals is not easy [6]. Using accelerometers that automatically measure the animal’s activity has the potential to obtain this information, especially in extensive systems, where access to the animals is more difficult
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