  The soybean cyst nematode (SCN), Heterodera glycines, is the most damaging
pathogen of soybeans in the United States. To assess the severity of nematode
infestations in the field, SCN egg population densities are determined. Cysts
(dead females) of the nematode must be extracted from soil samples and then
ground to extract the eggs within. Sucrose centrifugation commonly is used to
separate debris from suspensions of extracted nematode eggs. We present a
method using OptiPrep as a density gradient medium with improved separation and
recovery of extracted eggs compared to the sucrose centrifugation technique.
Also, computerized methods were developed to automate the identification and
counting of nematode eggs from the processed samples. In one approach, a
high-resolution scanner was used to take static images of extracted eggs and
debris on filter papers, and a deep learning network was trained to identify
and count the eggs among the debris. In the second approach, a lensless imaging
setup was developed using off-the-shelf components, and the processed egg
samples were passed through a microfluidic flow chip made from double-sided
adhesive tape. Holographic videos were recorded of the passing eggs and debris,
and the videos were reconstructed and processed by custom software program to
obtain egg counts. The performance of the software programs for egg counting
was characterized with SCN-infested soil collected from two farms, and the
results using these methods were compared with those obtained through manual
counting.
