Detecting Plateau Zokor ( Eospalax baileyi ) Mounds in UAV Imagery of Alpine Meadows Using Deep Learning Algorithms

The plateau zokor ( Eospalax baileyi ) is both an ecosystem engineer and a major rodent pest in the alpine meadow of the Qinghai–Tibet Plateau. Through its burrowing and soil‐turnover activities, the species enhances soil aeration, promotes nutrient cycling, and increases microsite heterogeneity that can facilitate plant regeneration. Yet, its extensive mound building also reduces forage availability, alters vegetation structure, and accelerates soil erosion. These contrasting ecological roles make the abundance and positions of mounds key indicators for assessing rodent impacts and guiding grassland management. However, obtaining accurate and spatially explicit information on mound numbers and distribution remains difficult because traditional manual surveys are labor‐intensive, spatially limited, and unsuitable for large‐scale monitoring. UAV‐based imagery provides new opportunities for automated detection, but the small size of mounds, their dense aggregation, and the heterogeneous background of alpine meadows pose substantial challenges for conventional algorithms. To address these limitations, we developed PZM‐YOLO, an improved small‐object detection model based on YOLOv5s that enhances feature extraction and localization accuracy for zokor mounds in complex UAV imagery. We compiled a multi‐season UAV mound‐image dataset and evaluated the model against several mainstream object‐detection networks. PZM‐YOLO achieved a precision of 91.1%, recall of 69.3%, AP 50 of 76.1%, and an F1‐score of 78.7%, outperforming the baseline YOLOv5s. Independent validation using 100 manually annotated UAV images further confirmed its reliability. The proposed framework enables automatic detection of plateau zokor mounds and provides quantitative information on mound abundance and mound position. This framework also offers a transferable approach for detecting indicators of subterranean rodents, such as mounds and holes, supporting ecological impact assessment and grassland restoration planning.