AntTracker: A low-cost and efficient computer vision approach to research leaf-cutter ants behavior

Abstract

Leaf-cutter ants play a crucial role in agroecosystems, and understanding their behavior is key to developing effective damage control strategies. While several tracking solutions exist for ants in controlled environments or on aerial images, accurately measuring ant behavior in the wild remains a challenge. In this work, we propose a three-stage processing pipeline that segments individual ants, tracks their movement, and classifies whether they are carrying a leaf using a convolutional neural network. The output of the pipeline includes a timestamped record of the activity on the trail, accounting for parameters such as ant velocity, size and if it is going from or to the nest. We use the recently developed portable device AntVideoRecord to register video of a selected ant trail. To validate our approach, we collected a labeled dataset and evaluated each stage using standard metrics, achieving a median F2 score of 83% for segmentation, MOTA of 97% for tracking and F1 of 82% for detecting ants carrying a leaf. We then carried out a larger use case in the wild, demonstrating the effectiveness of our approach in capturing the intricate behaviors of leaf-cutter ants. We believe our method has the potential to inform the development of more effective ant damage control strategies in agroecosystems.