Classification of freshwater macroinvertebrates is a valuable tool in environmental biomonitoring activities, although it can be time-consuming and requires specialized expertise. Macroinvertebrate samples are manually classified using taxonomic resolutions or keys to estimate the level of contamination in an ecosystem. This classification follows a hierarchy in which morphological variations in the different body parts are examined, reflecting adaptations between genotype and environment (phenotype), resulting in estimates of the quality of the resource. Automatic classification has the potential to speed up the bioindication process. However, the complexity of the morphotaxonomic traits of these organisms, the need for adequate databases to address their vast diversity, and the lack of expert confidence in using these techniques have posed significant technical challenges. Despite advances in recent years, especially with deep learning techniques, the complexity of emerging models still needs to accurately capture the fine morphological features that are key to manual taxonomic classification. This paper examines how a semantic detector like YOLO performs when dealing with fine-grained traits in scenarios involving overlapping or hard-to-detect parts, different postures of the organism in images, and the nature and importance of traits in morphotaxonomic classification. Using a multiscale and differential approach, the YOLO model achieved 89% accuracy in recognizing morphological structures, with a recall rate of 94% and a mean average precision (mAP) threshold of 91%. The object detection approach can improve the performance and transparency of opaque methods to human experts, thereby increasing their confidence in model decisions by highlighting critical visual traits in morphotaxonomic classification.