Obesity is a prevalent health issue among captive wildlife, but the obesity status of captive Asian elephants (Elephas maximus) in China has not been systematically investigated. This study assessed obesity in 204 captive Asian elephants across 43 facilities by recording seven potentially relevant variables: sex, age, daily feed supply, proportion of high-calorie feed, outdoor enclosure area, outdoor time, and foot disorders. Body condition was evaluated using visual scoring, revealing a high prevalence of overweight and obesity, particularly in zoo-kept elephants. Over 70% of captive Asian elephants in China were classified as overweight or obese. Statistical analysis indicated that insufficient outdoor time is likely a primary contributor to obesity among zoo elephants. We recommend extending outdoor time and regulating the supply of high-calorie feeds (e.g., fruits, vegetables, and pellets) to promote exercise and balance energy intake. Additionally, implementing positive reinforcement training for routine health checks, including foot exams and blood sampling, could improve data collection on elephant health and welfare, ultimately enhancing obesity management and overall animal well-being.

Asian elephants (Elephas maximus) face substantial thermoregulatory constraints because of their large body size, low relative surface area, sparse hair, and lack of functional sweat glands. Reliable body temperature measurement is essential for assessing thermal status and evaluating welfare in both wild and managed populations, but conventional rectal thermometry requires close physical contact, animal training, and repeated manual handling, making high-frequency, continuous, large-scale monitoring impractical. This study developed a non-invasive framework for automatically detecting the outer ear and extracting auricular surface temperature from infrared thermograms. Rectal temperature, regional surface temperatures, ambient temperature, and relative humidity were measured synchronously in eight semi-captive Asian elephants, yielding 425 matched observations. The associations between rectal temperature and the surface temperatures of three anatomical regions (head, outer ear, torso and limbs) were analyzed using repeated-measures correlation accounting for the non-independence of repeated measurements. Mean outer-ear temperature showed the strongest within-individual association with rectal temperature (r_rm = 0.395, p < 0.001), identifying the outer ear as the optimal thermal window for subsequent automated monitoring. Eight lightweight YOLO models—YOLOv5n, YOLOv5s, YOLOv8n, YOLOv8s, YOLO11n, YOLO11s, YOLO26n, and YOLO26s—were trained on 2178 annotated infrared images and evaluated on an independent 194-image test set from extra elephants. Model performance was assessed using detection metrics, inference speed, Bland–Altman agreement, Taylor diagram statistics, and a weighted multi-criteria score with Monte Carlo sensitivity analysis. YOLO11n achieved the best overall performance, with an mAP50 of 0.933 and an inference speed of 164 frames per second. The proposed framework provides an efficient method for automated auricular temperature monitoring and has potential applications in elephant welfare management and remote physiological surveillance.