Affiliation:
1. Fujairah Research Centre
Abstract
Abstract
This study proposed to collect and analyze the daily activities of dromedary camels kept under farm conditions by training a model depending on frames as opposed to long-term windows. Any change in daily activities gives information on the camel’s health status, and an early warning can be generated to issue a signal upon identifying any diseased camels. Five daily activities: eating, standing, sitting, drinking, and sleeping, were recorded, and analyzed in two phases, each of 7 days of continuous video recordings in November and December 2022. Phase 1 was applied on two camels to test the time elapsed for each daily activity and Phase 2 was applied on 4 camels to determine the difference in daily activities between different camel individuals. The average standing and sleeping activities reduced from 9.8 h to 6.0 h, and from 4.3 h to 2.8 h in phases 1 and 2, respectively. While the average sitting and eating activities increased from 6.2 h to 9.9 h and from 3 h to 4.7 h in phases 1 and 2, respectively. The average drinking activity for all tested camels was 43 min in both phases. All camels were active in the eating, drinking, and standing activities during the early morning hours and after 16:00 O’clock till evening. While during noon and early afternoon, they were sitting under shadows. During the evening and nighttime, they mainly sat, occasionally moving their heads, with some standing and walking activities. The average eating, standing, and sleeping activities did not differ between the 4 tested camels throughout the experiment. While the drinking and sitting activities showed an average variation of 25% and 12%, respectively, in between the tested camels. In conclusion, the camel’s daily activities can be monitored by using the deep learning model. This model efficiently monitors and improves the health of camels kept on farms in remote areas beyond human control.
Publisher
Research Square Platform LLC