R. Bras. Zootec.17/Jul/2026;55:e20250201.

Precision livestock

Performance of multiple models under different test strategies in predicting the ingestive behavior of grazing cattle

Lázaro Henrique da Silva , Caio Matheus Leite da Silva , Erick Galani Maziero , Daniel Rume Casagrande , Marina de Arruda Camargo Danes

DOI: 10.37496/rbz5520250201

ABSTRACT

The use of digital technologies and wearable sensors has advanced in precision livestock farming. However, there is still no clear definition of the most suitable models for the use of sensors in grazing animals, especially under tropical systems. Thus, we aimed to compare the performance of six predictive models for grazing cattle behavior and to evaluate different testing strategies. The study was conducted in a pasture area, using nine Tabapuã heifers monitored by triaxial accelerometers attached to the nape, with data recorded every second. Ninety-six 12-h visual observations were performed and combined with sensor data to generate two datasets: grazing vs non-grazing (GNG) and grazing, ruminating, and idling (GRI). The models tested were generalized linear model (GLM), random forest (RF), k-nearest neighbors (KNN), gradient boosting (GB), light gradient boosting (LGB), and artificial neural network (ANN), with hyperparameter optimization performed via Bayesian search. Seven testing strategies were applied, including random holdout (RHO), leave-animal-out (LAO), leave-height-out (LHO), with four different sward heights, and an external test, obtained in a previous study. All models, except GLM, showed good performance (~80% accuracy for GNG); however, the inclusion of three classes reduced the average accuracy by 6.4%. LGB and KNN were the most computationally efficient models, in terms of data process time, while ANN was the most demanding. Testing strategies that reduced data dependence (LAO and LHO) decreased accuracy, highlighting the importance of real-world scenarios for the development of robust commercial applications. It is recommended to balance accuracy and computational cost, prioritizing LGB, GB, or KNN for practical applications. Moreover, testing strategies under different farm conditions or using external datasets should be performed.