A Standardized Method for Insect Color Analyses using Open Source Software: AInsectID Version 1.1 Color Merge

The accurate representation of color is important in applications involving species identification. Environmental variations introduce inconsistencies in color perception, affecting the reliability of automated image processing algorithms. In previous work, we developed a hybrid algorithm, AInsectID Version 1.1 Color Merge, to overcome challenges posed by over-segmentation and under-segmentation in insect wing color clustering. We achieved this by using color differences between superpixels to measure homogeneity during the superpixels segmentation process. Nevertheless, our algorithm remains sensitive to environmental effects, affecting its performance and accuracy in color analyses. Here, we introduce a standard imaging method for insect species, as a pre-requisite to analysis in AInsectID Version 1.1 Color Merge. We systematically examine the effects of varying lighting conditions, angle of observation, and working distance in a controlled environment to assess their impact on the performance of the algorithm. We find that by meticulously controlling lighting, working distance, and lighting angle, we develop an evidence-based standard approach to imaging colors that is robust and repeatable. By following our standardized procedure, consistent color analyses are possible under varying environmental conditions. The method was tested using the Delta E2000 (ΔE) color difference metric with a threshold of 1, demonstrating that our standard approach maintains perceptual accuracy within the Just Noticeable Difference (JND) range, while improving the reliability of color analyses of insect wings in diverse environments. Finally, to validate the robustness of our standardization method, we evaluated the certainty of our results at different levels of confidence.