# A Novel Tool for Biodiversity Studies: Earthworm Classification via NGS and Neural Networks
## 3 Results and Discussion
However, molecular techniques are expensive and time-consuming [56]. Moreover, correct earthworm identification can require specimen dissection to extract detailed anatomical studies [57]. Therefore, [PERSON] et al. [56] proposed a non-destructive procedure involving digital images and machine vision to classify earthworm species belonging to _E. fetida_ and others based on hand-crafted and deep features. Furthermore, deep learning as a state-of-the-art approach was used by [PERSON] and [PERSON] [58] to detect and sort earthworm cocoons and by [PERSON] et al. [59] to observe earthworm behavior. Figure 5: The ROC curves of earthworm classification using the model built based on selected image textures using SVM for all three classes (**a**), KNN for all three classes (**b**), Ensemble, and Narrow Neural Network for _Dendrobuena_ ssp. (**c**), Ensemble and Narrow Neural Network for _Eisenia fetida_ (**d**), and Ensemble and Narrow Neural Network for _Lumbricus terrestris_ (**e**). In the field of image processing, texture refers to the spatial variation in pixel intensity across an image. It plays a crucial role in conveying meaningful information about the structure of physical objects being analyzed. By examining texture characteristics quantitatively, it is possible to gain insights into the quality of a biological object. Texture analysis involves extracting numerical features from an object's image, allowing for the detection of subtle changes that may not be easily noticeable to the human eye. Even when digital images contain the same number of pixels and share similar color histograms, variations in the spatial distribution of color can result in distinct textures, which help differentiate objects [43, 60, 61]. The approach combining image texture parameters and machine learning algorithms turned out to be useful for non-destructive, objective, and inexpensive distinguishing of earthworms belonging to different genera. The performed study involved samples belonging to the earthworm species from two different locations. Therefore, the obtained results were more universal, as well as free from errors resulting from the influence of environmental conditions. The applied procedure allowed for the correct identification and differentiation of earthworm species without the need to involve qualified experts who can be subjective or expensive, and time-consuming molecular techniques. A flatbed scanner is a relatively inexpensive, popular, and readily available imaging tool. Moreover, an automated vision system can examine many more earthworms than a human at the same time. After preparing the research material, the main steps to distinguish earthworms using image analysis and machine learning were quick, taking less than two minutes to acquire the image, a few minutes for image processing, and a few seconds for building the model and making a decision. In future research, research can be expanded by using deep learning to distinguish earthworms belonging to different species and genera based on the texture parameters of color images.