Machine Vision-Based Quantification of Colony-Level Homing Adaptation in Apis mellifera Following Hive Entrance Displacement.

Both hive displacement and entrance relocation can challenge honeybee homing navigation, yet the temporal dynamics of colony-level homing behavior following hive entrance displacement remain poorly quantified. To address this, we established a non-invasive automated pipeline integrating YOLO11m-based detection, OC-SORT tracking, and the Homing Rate (HR) to monitor nine honeybee (Apis mellifera) colonies under semi-natural apiary conditions. HR links trajectory endpoints with the experimentally defined valid entrance state and provides a colony-level measure of entrance-targeting accuracy. Horizontal entrance displacement caused a substantial reduction in HR in the treated colonies, whereas the Control Group showed only a small concurrent change. During the subsequent four-day observation period, all six treated colonies displayed a similar dynamic pattern characterized by an initial rapid increase in HR, followed by a slower increase. The asymptotic exponential model provided a better descriptive representation of these temporal dynamics than a linear model. The 14-day observation of Colony A1 further revealed an early increase, a transient decline, and subsequent recovery toward a relatively stable level, indicating that the post-displacement trajectory was not strictly monotonic. Overall, this study provides an automated quantitative pipeline for continuously characterizing colony-level entrance-targeting behavior and its temporal dynamics following hive entrance displacement.