Artificial intellegence for tickboarn diseases early assessment Sayed Zamiti (ENMV sidi thabet) Amine Mosbah (ENMV sidi thabet) Mourad Ben Said (ENMV sidi thabet) Sajid Ali (Freelancer) Moez Mhadhbi (ENMV sidithabet) Mourad Rekik (ICARDA) Mohamed Aziz Darghouth (ENMV sidi thabet) this project is financed by ICARDA
Tickscan uses state-of-the-art object detection algorithms to recognize different tick species belonging to the Hyalomma genus. Ticks of this genus are amongst the most frequent ectoparasites of livestock in the NENA region, they transmit several pathogens of veterinary and human importance. Tickscan has been trained to recognize two crucial Hyalomma species in Tunisia, Hyalomma dromedarii which has been recently shown to carry the Crimean–Congo hemorrhagic fever virus in Southern Tunisia, and H. scupense which is the vector of T. annulata the agent of tropical theileriosis an important disease of cattle in the NENA region. Tickscan may help better mitigate tick threats through early detection of dynamic population shifts, YOLOv5 model achieved the best detection performance in both tick sexes with an approximate precision (AP) of 0.76 followed by Faster RCNN (AP= 0.69) and SSD (AP=0.49), while DERT and Efficient Det models had the poorest metrics (AP= 0.48 and 0.42), correspondingly.