An assessment of stingless beehive climate impact using multivariate recurrent neural networks

A healthy bee colony depends on various elements, including a stable habitat, a sufficient source of food, and favorable weather. This paper aims to assess the stingless beehive climate data and examine the precise short-term forecast model for hive weight output. The dataset was extracted from a single hive, for approximately 36-hours, at every seven seconds time stamp. The result represents the correlation analysis between all variables. The evaluation of root-mean-square error (RMSE), as well as the RMSE performance from various types of topologies, are tested on four different forecasting window sizes. The proposed forecast model considers seven of input vectors such as hive weight, an inside temperature, inside humidity, outside temperature, outside humidity, the dewpoint, and bee count. The various network architecture examined for minimal RMSE are long short-term memory (LSTM) and gated recurrent units (GRU). The LSTM1X50 topology was found to be the best fit while analyzing several forecasting windows sizes for the beehive weight forecast. The results obtained indicate a significant unusual symptom occurring in the stingless bee colonies, which allow beekeepers to make decisions with the main objective of improving the colony’s health and propagation.