A multivariate autoregressive multilayer perceptron model for predicting internal beehive conditions from sensor data

Abstract

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.