Kan AI spåra enskilda bin i en bikupa med datorseende och förutsäga deras roller ?

Sociala insekter som bin uppvisar komplexa beteenden som beror på individuella och gruppdynamiska processer. Nyligen utvecklade AI-system som tränats på videodata från bikupor kan identifiera och följa specifika bin över tid, även genom ocklusioner. Dessa modeller kan klassificera roller som födosökare, sjuksköterska eller städare baserat på rörelsemönster och interaktioner. Bedriften främjar vår förståelse av kollektiv intelligens och erbjuder verktyg för ekologisk övervakning.

Background

Computer vision has been increasingly applied to the study of bee behavior, enabling researchers to track individual bees within a hive using cameras and machine learning algorithms. These systems analyze movement patterns and interactions, allowing classification of roles such as forager, nurse, or guard bee. Early work established that movement trajectories and social interactions correlate with functional specialization in colonies; for example, foragers exhibit distinct flight patterns and interaction networks compared to nurses, which remain closer to brood cells. By 2018, systems demonstrated the ability to identify and follow specific bees through occlusions using spatio-temporal deep learning models trained on hive video data. These models leverage behavioral signatures—such as path regularity, interaction frequency, and spatial preferences within the hive—to infer roles with reported accuracies above 85% in controlled settings. The approach builds on foundational studies in social insect ethology, which mapped behavioral repertoires using manual observation and RFID tagging, but extends those methods with scalable, non-invasive computer vision. Active research continues to improve occlusion handling, real-time performance, and generalization across hive configurations and bee species. Source: Proceedings of the National Academy of Sciences, 2018.