RT Journal Article
T1 A multivariate autoregressive multilayer perceptron model for predicting internal beehive conditions from sensor data
A1 Robustillo, M. Carmen
A1 Senger, Diren
A1 Parra, M. Isabel
A1 Pérez, Carlos J.
K1 Redes neuronales (Informática)
K1 Apicultura
AB The global decline in bee populations poses a critical threat to biodiversity and ecosystem stability, motivating the adoption of precision beekeeping strategies that combine sensor networks with data-driven models to optimise hive management and reduce colony losses. This study introduces a multivariate autoregressive multilayer perceptron (AMLP) model that integrates historical internal hive variables (temperatures, weight, humidity, and pressure) with external climatological data to forecast future states of these endogenous variables. Data were collected from 13 sensor-equipped hives of the BeeObserver project. The AMLP was evaluated against a standard multilayer perceptron (MLP) and a vector autoregressive (VAR) model using 10-fold rolling-window cross-validation. Forecast performance was assessed using two different error metrics for 1- and 3-day horizons. Across all hives, the AMLP reduced the mean percentage error by approximately 6%–7% relative to the MLP and up to 1.3% relative to the VAR, achieving superior predictive accuracy, with statistically significant improvements for most internal variables. By combining autoregressive lags with neural network flexibility, the AMLP captures both temporal dependencies and specific patterns while supporting incremental retraining as new data arrive. This approach provides scalable, adaptive, and real-time prediction of hive dynamics, offering a robust tool for proactive decision-making in precision beekeeping. The results demonstrate that integrating temporal and environmental information through AMLP models enhances predictive accuracy and supports timely interventions, ultimately improving colony health and resilience. These findings highlight the potential of advanced data-driven forecasting models to strengthen sustainable apiculture practices and contribute to the conservation of bee populations.
PB Elsevier
YR 2026
FD 2026-05
LK https://hdl.handle.net/10630/45932
UL https://hdl.handle.net/10630/45932
LA eng
NO Robustillo, M. Carmen, Senger, Diren, Parra, M. Isabel, Pérez, Carlos J. (2026). A multivariate autoregressive multilayer perceptron model for predicting internal beehive conditions from sensor data.   Computers and Electronics in Agriculture. ELsevier, Vol.  246, May,  DOI 10.1016/j.compag.2026.111593
NO Funding for open access charge: Universidad de Málaga / CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 mar 2026