Portable device for enumeration and speciation of food animal parasites

A method comprising:

• receiving a dataset of labeled images, each labeled image in the dataset of labeled images categorized as an image with zero oocytes, an image with oocytes of a parasite species in isolation, or an image with oocytes of parasite species in combination;
• performing at least one cropping or at least one rotation to each image in the dataset of labeled images to produce an expanded training dataset;
• and
• training, using the expanded training dataset, a machine-learning model into a trained machine-learning model that determines speciation of at least one parasite species, a count of oocytes of the at least one parasite species, and an infection status of the at least one parasite species, wherein determining an infection status comprises determining that at least one oocyst of the at least one parasite species is embryonated.

A non-transitory computer-readable medium comprising instructions that are executable by a processor for causing the processor to perform operations comprising:

• receiving an on-site dataset;
• accessing a trained machine-learning model;
• performing at least one cropping on the on-site dataset to produce an expanded on-site dataset;
• applying the trained machine-learning model to the expanded on-site dataset to produce an evaluated on-site dataset;
• reconstructing the evaluated on-site dataset to produce an output, the output comprising an on-site speciation of at least one parasite species, a count of oocytes of the at least one parasite species, and an infection status of the at least one parasite species, wherein the infection status comprises a binary status of an infected status or a non-infected status, and wherein the infected status comprises at least one embryonated oocyte in the oocytes of the at least one parasite species;
• and
• displaying the output.