Automatic livestock monitoring to identify abnormal behavior on the Balcashi ranch, Chimborazo Province
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Abstract
Livestock farming, an essential economic pillar in rural areas, faces growing challenges in the early detection of abnormal behavior in cattle, such as lameness or isolation, which affect their welfare and productivity. This study presents an automated monitoring system implemented at Hacienda Balcashi (Chimborazo, Ecuador), which integrates environmental sensors for temperature, humidity, precipitation, and movement with machine learning algorithms. Based on the analysis of historical data from 2019–2024, a significant correlation was found between temperature (r = 0.45, p < 0.01), humidity (r = 0.32, p < 0.05), and the occurrence of abnormal behaviors. Notable differences were identified between breeds, with Holstein being the most vulnerable (1.2 events/day), followed by Jersey (0.8) and Criollo (0.5), highlighting the influence of climate adaptability. The logistic regression model achieved an accuracy of 82% and an AUC-ROC of 0.84. This work contributes to precision livestock farming in high Andean contexts, proposing a replicable approach that optimizes animal welfare and operational efficiency and can be scaled up through regional adjustments.
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