Integrative Approach to Insect Morphology and Systematics: Leveraging Digital Imaging and Machine Learning for Taxonomic Classification

Abstract

knowledge species, how ecosystems work together, and how evolution works requires a deep knowledge of insect anatomy and systematics. The old ways of classifying taxons depend a lot on physical traits, which can be subjective and take a lot of time. With the rise of digital images and machine learning, there are new ways to improve the accuracy and speed of identifying and classifying insects. This research shows a new way to sort bug species into groups based on their physical features that uses both high-resolution digital photos and clever machine learning methods. The study used 3D imaging and automatic feature extraction on a collection of over 10,000 bug species from different orders to get very detailed pictures of their shapes. The pictures were processed to get important details like size, form, and surface roughness, which are needed for correct classification. The study used a number of machine learning models, such as convolutional neural networks (CNNs) and support vector machines (SVMs), to test how well they could identify taxonomic groups. Our results show that both in terms of accuracy and speed, the machine learning models are much better than the old-fashioned morphological methods. The CNN model was especially good at finding bug families more than 95% of the time. This shows that it could be used for large-scale taxonomy studies. We also found certain physical traits that are most useful for species classification. This helped us understand how different bug groups have evolved together. The interdisciplinary method not only makes scientific classification more accurate, but it also makes it easier to study species that aren't well known or aren't reflected in standard assessments. We can rely less on expert taxonomists and speed up the study of biodiversity data by automating the labelling process. This will help with protection efforts and ecology research.