# A Novel Tool for Biodiversity Studies: Earthworm Classification via NGS and Neural Networks
## 1 Introduction
This process not only helps in waste reduction but also promotes soil health by producing valuable organic matter, making it a viable solution for sustainable waste management. One of the most important species for vermiculture is _Eisenia fetida_, commonly known as the compost worm. Its ability to efficiently process a wide range of organic waste into nutrient-rich compost makes it indispensable in vermicomposting systems [15]. _E. fetida_ is particularly valued for its continuous activity throughout the year, rapid growth,and high reproductive rate, all of which contribute to its effectiveness in organic waste decomposition [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33]. The Organization for Economic Cooperation and Development (OECD) recommends _Eisenia fetida_ and _Eisenia andrei_ for conducting tests on acute and subacute soil toxicity. These species are ideal for evaluating the environmental impact of various chemicals and pollutants due to their sensitivity to soil contaminants and their role in the decomposition of organic matter. Their use in toxicity testing helps assess the effects of hazardous substances on soil health and ecological sustainability. _Eisenia fetida_ and _Eisenia andrei_ share similar body length, segment number, and similarities in citellum and tubercula pubertatis shape. However, they can be distinguished in their adult form by differences in pigmentation. The taxonomic distinction between these species was confirmed through electrophoretic techniques by [PERSON] [34]. Historically, the two species were often classified as subspecies due to their differing body pigmentation, but modern studies have solidified their status as separate species based on these morphological and genetic differences. _E. fetida_ is distinct from its relative, _Eisenia andrei_, by its striped appearance and secretion of a yellow fluid when disturbed [35; 36; 37]. [PERSON] et al. [38] described a fluorescent method that allows differentiation between _Eisenia fetida_ and _Eisenia andrei_. Accurate species identification of earthworms remains a critical challenge with significant implications for ecological research and ecotoxicological testing. Misidentification is a widespread issue, as demonstrated by [PERSON] et al. [39], who conducted a comprehensive DNA barcoding analysis of Eisenia fetida and Eisenia andrei across 28 ecotoxicological laboratories. Their results showed that only 61% of specimens were correctly identified, highlighting the urgent need for reliable identification methods. Similar findings were reported by [PERSON] et al. [40], further underlining the practical importance of improving species determination in this group. Novel approaches that can accurately identify earthworm species at all life stages are therefore essential for ensuring data reliability in both scientific and regulatory contexts. The study aimed to differentiate earthworms of the genera Eisenia, Dendrobaena, and Lumbricus using two approaches: RNA sequencing and machine learning models built based on image textures selected from a set of 2172 parameters. Due to using image analysis and machine learning, the procedure of earthworm distinguishing was nondestructive, objective, and inexpensive. The novel aspect regarding non-destructive classification concerns the development of innovative machine learning models involving selected texture parameters from earthworm images in individual color channels R, G, B, L, a, b, X, Y, Z, U, V, and S. The new models containing unique data were developed using traditional machine learning algorithms. Therefore, the most innovative aspect concerns the input data of the models.