Assessing Goats' Fecal Avoidance Using Image Analysis-Based Monitoring - Bonneau, Mathieu; Godard, Xavier; Bambou, Jean-Christophe (Frontiers in Animal Science, 2022)
The recent advances in sensor technologies and data analysis could improve our capacity to acquire long term and individual dataset on animal behavior. In livestock management, this is particularly interesting when behavioral data could be linked to production performances, physiological or genetical information, with the objective of improving animal health and welfare management. In this article we proposed a framework, based on computer vision and deep-learning, to automatically estimate animal location within pasture and the associated risk of gastrointestinal infection. We illustrated our framework for the monitoring of goats allowed to graze an experimental plot, where feces containing gastro-intestinal nematodes infective larvae, were previously dropped in delimited areas. We were able to detect, in average, 87.95% of the goats and to identify the detected individuals with an average sensitivity of 94.9% and an average precision of 94.8%. Four animals were monitored, during two grazing weeks on the same pasture (Trial 1 from April 12th 2021 to 19th, and Trial 2, from June 28th 2021 to July 5th). Using the monitoring framework, we were able to study different component of animal behavior, relating to the risk of infection. First, we monitored the ability of the animal to avoid infected feces on pasture, and showed an important temporal and individual variability. Interestingly, the avoidance behavior of all animals increased during the second grazing week (Trial 2), and the level of increase was correlated with the level of infection during Trial 1 (Pearson’s correlation coefficient equals to 0.93). We also studied the relationship between the time spent on gastro-intestinal nematodes infested areas with the level of infection, but a clear relationship was not found. We characterized social behavior using the inter-individual distance, but no relationship with the level of infection was evidenced. Due to the low number of studied animals, biological results should be interpreted with caution, nevertheless the framework provided here is a new relevant tool to explore the relationship between ruminant behavior and GIN parasitism in routine experiments. And more generally, to monitor behavior of grazing animals.