Identification of plant parasitic nematode species is usually achieved following morphobiometric analysis, which requires a certain level of expertise and remains time consuming. Moreover, molecular and morphological discrimination of a number of emergent or cryptic species is sometimes difficult. Finding a way to achieve morphological characterisation quickly and accurately would greatly advance nematology science. Here, we developed a complete method in order to identify the two quarantine nematode species Globodera pallida and Globodera rostochiensis. First, we chose discriminative metrics on the stylet of nematodes that are able to be used by algorithms in order to build an automated process. Second, we used a custom computer vision algorithm (CCVA) and a convolutional neural network (CNN) to measure our metrics of interest. Third, we compared the CCVA and CNN predictions and their discriminative power to distinguish closely related species. Results show accurate identification of G. pallida and G. rostochiensis with the two methods, despite small-scale divergence (one to five µm depending on the metric used). However, the error rate is higher for Globodera mexicana, suggesting that the algorithms are too specific. Nonetheless, these methods represent a promising novel approach to automated morphological identification of nematodes and Globodera species in particular.