Beekeeping suitability prediction based on an adaptive neuro-fuzzy inference system and apiary level data

Abstract

The study employs a predictive modelling approach using a fuzzy inference system to assess the beekeeping potential of a geographic area. Specifically, an adaptive neuro-fuzzy inference system with subtractive clustering (ANFIS-SC) was utilized, incorporating six input variables that influence Apis mellifera health and productivity, and field data as the output variable reflecting the state of a colony. The results demonstrate the model’s effectiveness in predicting the suitability of areas for beekeeping. Sensitivity analysis highlighted the significant effects of relative humidity on the model’s output. The research underscores the importance of data quality, particularly in determining the local land cover quality index (LLCQI), on the outcomes. This study highlights the role of data science in enhancing precision in beekeeping and proposes its integration into management practices to support honey bee health.

Keywords

• Adaptive neuro-fuzzy inference system
• Subtractive clustering
• Suitability prediction
• Beekeeping activity