Abstract:
Different non-invasive techniques have been used to determine herbivore diet composition from fecal samples, including micro-histological analysis of epidermal fragments. This method can provide reliable semi-quantitative data through the identification of plant cell structures visualized under an optical microscope. However, this method is highly time-consuming and it requires significant expertise in microscopic identification. Since micro-histological analysis is based on pattern recognition, artificial intelligence (AI) could be used to make this method more time efficient through automated identification and counting of epidermal fragments. We developed a software application based on an AI model that, appropriately trained, could identify and count epidermal fragments from photographed microscope slides. We compared the performance of this model to that of visual identification by a trained observer using in vitro mixtures of fragments from two plant species, Arbutus unedo and Rubia peregrina, with very different epidermal characteristics. Both the human observer and the AI model estimated proportions of plant fragments very close to those of the original mixtures. In addition, once trained, the AI model was over 350 times faster in identifying and counting fragments compared to a human observer. Our study highlights the potential of AI to be applied to the study of herbivore diets for labor-intensive pattern recognition tasks.
