NemaRec: A deep learning-based web application for nematode image identification and ecological indices calculation

Nematodes are ubiquitous in soil representing different trophic levels and occupying a central position in the detritus food web. Nematodes have been widely used for biomonitoring of soil quality and health. However, their application in bio-indicator is limited due to the taxonomic identification is laborious, and largely relying on morphological characters which require substantial prior nematology experiences. In present study, we developed the web application NemaRec using the I-Nema dataset. NemaRec adopts the deep convolutional neural networks approach for nematode image identification, incorporate I-Nema dataset in a user friendly interface, and further extends it to the calculation of feeding types, c-p values, MI and PPI indices for environment evaluation. Within 19 studied genera, the model can properly identify up to 60% genera, 76% of c-p values and 76% feeding types in specimen-based dataset (images taken from microscopy), and 94%–97% in augmented dataset (image treated with random flip and Gaussian noise). The pipeline was further incorporated into a user-friendly web application NemaRec. NemaRec offers high-throughput online identification while simultaneously collect images uploaded by users for future modeling training. To our knowledge this is the first soil nematode image identification system using deep learning approach. NemaRec is available http://168.138.167.251:8080/).