Using a knowledge representation logic to estimate the availability of Imbrasia epimethea (Lepidoptera: Saturniidae), an important edible insect in Subsaharan Africa

Abstract

Based on monthly abundance patterns, we model the density of Imbrasia epimethea (Drury, 1773), an important edible insect in Africa. Categorical data was collected from various regions in Cameroon, and data analysis techniques were used to infer relationships between environmental variables and the level of insect abundance. Through fuzzy logic modeling, we identified the key environmental factors and rules that influence the density of the insect. To visualize the distribution of I. epimethea across African landscapes, interpolation techniques were used on the study area matrix of geographical coordinates based on the corresponding monthly predictor variables for the most recent available year (2022). The results suggested a clear dynamic across Africa through the different months of the year with potentially overlapping generations with relatively high accuracy (>90%). A clear relationship between regional climatic conditions and the density of I. epimethea could be established across Africa. The models provide insights into the complex dynamics of insect populations and sheds light on the stability and transferability of our results across different African regions (during stability analysis). This research offers a foundation for further investigations on sustainable food production and the promotion of edible insects as a viable protein source.

Keywords

• Fuzzy inference
• Rule-based
• Edible insects
• Africa
• Food security
• Imbrasia epimethea