Detecting stress parameters in dromedary camels using computer vision

Dromedary camels exhibit behavioral responses influenced by both physiological conditions and environmental factors. Poor health, physical or emotional, can manifest as behavioral abnormalities. This study aims to build a video-based stress detection model by analyzing camel behavior under different conditions. Camels from Marmoom Farm, UAE, were observed over eight days: six days included interventions such as blood collection and/or intensive training, and two days followed their typical routine. Video footage was captured from three cameras positioned around the enclosures and pens. Using the YOLOv8 architecture, we developed a model to classify normal behaviors - “standing”, “sitting”, “sleeping” and stress-related behaviors - “distressed sitting”, “moving around uncontrollably”, “pulling on rope”. The model obtained a precision of 0.971, recall of 0.959, mAP50 of 0.985, and mAP50–95 of 0.924. Four camels were closely monitored to analyze correlations between behavioral stress indicators and activities such as blood sampling, race training, and environmental conditions. Results indicate that while high-intensity training often induces stress, individual endurance levels and external factors like weather also significantly influence stress responses. This study presents a novel, automated method for early stress detection in camels, contributing to improved animal welfare and farm management practices. • YOLOv8 is used to detect stress behaviors in dromedary camels via video. • Normal and stress-related behaviors like “pulling on ropes” are identified. • The model achieved high accuracy with 0.971 precision and 0.959 recall. • Results show stress varies with training intensity and environmental factors. • This work supports early stress detection to enhance camel welfare on farms.