Sensors • Vol 23 • No 1
Comparative Study of Machine Learning Models for Bee Colony Acoustic Pattern Classification on Low Computational Resources
January 2023 • Antonio Robles-Guerrero, Tonatiuh Saucedo-Anaya, Carlos Guerrero-Méndez, Salvador Gómez-Jiménez, David Navarro-Solís
In precision beekeeping, the automatic recognition of colony states to assess the health status of bee colonies with dedicated hardware is an important challenge for researchers, and the use of machine learning (ML) models to predict acoustic patterns has increased attention. In this work, five classification ML algorithms were compared to find a model with the best performance and the lowest computational cost for identifying colony states by analyzing acoustic patterns. Several metrics were computed to evaluate …