Recent advances in sensor technologies and data analysis helped monitoring animal behavior over long time periods. This is particularly interesting to study the link between behavior and animal health. In this work, we studied the capacity of Creole goats to avoid feces on pasture. We developed an experimental framework, composed of a small pasture of 12x12=144m2 with two zones of 6x2=12m2 infested with feces, and a monitoring system, based on a time lapse camera, taking pictures every 20s from 6:30 to 18:00. A set of 3,800 images were manually labeled to (i) train a Yolo based CNN, ables to detect goats on the images and (ii) train a resNet50 based CNN, ables to identify the goats present on pasture. We used the framework to monitor the location of four Creole goats, selected for their various colors, to make automatic animal identification easier. We were able to determine when the animals were on the infested areas or not. Goats were allowed to graze for two weeks, separated from more than 2 months. Goats were worm free when grazing started and the level of infection was evaluated after grazing, using fecal egg count. Goats were detected in 88% of the cases and the precision for animal identification was estimated to 95%. Although goats exhibited various level of avoidance, it increased for all goats during the second grazing week, and the level of increase was proportional to the level of infection resulting from the first grazing week.