Evaluating Machine Learning-Based Elephant Recognition in Complex African Landscapes Using Drone Imagery

Abstract

Abstract This paper evaluates a machine learning-based approach for identifying and analyzing African bush elephants within complex terrains using high-resolution drone imagery. With human-wildlife conflict posing a significant threat to elephants worldwide, accurate and efficient monitoring techniques are crucial, yet challenging in diverse landscapes. Our study utilizes approximately 3,180 drone-captured images from Kasungu National Park in Malawi, encompassing various terrains including dense forests and open bushlands. These images were systematically preprocessed and analyzed using three distinct ML algorithms: Faster R-CNN, RetinaNet, and Mask R-CNN, each fine-tuned for identification of elephants across different age groups. Comparative performance metrics revealed nuanced strengths and limitations: Faster R-CNN showed notable proficiency in detecting adult elephants, particularly in dense foliage. Mask R-CNN, while less precise overall, demonstrated increased effectiveness in identifying juveniles and infants. RetinaNet, optimized for larger images, showed particular adeptness with adult elephants but less so with younger ones. Despite these promising results, overall recognition rates were lower than ideal, highlighting the complexities of wildlife identification in natural settings. This study not only facilitates the identification and counting of individual elephants but also provides insights into the challenges of applying ML in complex ecological contexts. The derived insights can assist conservationists and park officials in making informed decisions related to wildlife protection and habitat preservation. Furthermore, the study offers a valuable blueprint for integrating AI and machine learning technology into wildlife conservation strategies, presenting a scalable model with potential applications for different species and geographic regions, while acknowledging the need for further refinement to enhance accuracy and reliability in diverse ecological settings.