From Above: Drone-Driven Computer Vision for Reliable Elephant Body Condition Assessment

Assessing individual animal health is essential for detecting early ecological stress that may scale to population-level impacts. Yet, conventional capture-based methods are invasive and logistically challenging, particularly for large mammals. This study evaluates the accuracy of drone-based morphometric measurements as a non-invasive approach for estimating elephants’ Body Condition Index (BCI). Research was conducted in Way Kambas National Park, Sumatra, using a DJI Matrice 300 RTK equipped with a multisensor camera to acquire aerial imagery, primarily from a top-down perspective. Morphometric parameters were extracted through image preprocessing, segmentation, and edge detection using an OpenCV-based Canny algorithm, followed by coordinate and Euclidean distance analyses. Drone-derived measurements were validated against field-based morphometry in captive Sumatran elephants. Linear regression revealed strong agreement between methods, with R2 values ranging from 0.91 to 0.97. Mid-body width showed the highest accuracy (R2 = 0.97, MAPE = 2.66%, RMSE = 2.36), while other body dimensions also performed consistently well. BCI-related morphometric ratios exhibited minimal differences between drone and field measurements, confirming methodological reliability. As an exploratory extension, a preliminary allometric scaling framework was applied to estimate body condition proxies in free-ranging wild elephants except for mid-body width; however, these estimates are model-derived from total body length and should be interpreted as indicative rather than as direct morphometric assessments of body condition. These findings demonstrate that drone-based photogrammetry provides a validated, practical, and non-invasive method for morphometric measurement in captive elephants, with promising but as yet incompletely validated potential for application to wild populations.