New methods of removing debris and high-throughput counting of cyst nematode eggs extracted from field soil

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To study the degree of damage caused to soybean crops by the soybean cyst nematode (SCN), Heterodera glycines, in the United States, determination of SCN egg population density in field samples is necessary. This normally involves extraction of cysts (dead female nematodes) from the soil sample, followed by grinding to obtain the eggs. Conventionally, sucrose centrifugation is employed to separate the debris from the extracted nematode eggs suspensions. In this paper, a method using OptiPrep is presented as an improved density gradient medium to achieve better separation and recovery of extracted eggs when compared to the sucrose centrifugation technique. Additionally, computerized methods were developed to identify and count the nematode eggs among the debris. In one such approach, a high-resolution scanner was employed to take static images of the extracted eggs and debris on filter papers, and a deep learning network was used to identify and count the eggs. In the second approach, a lensless imaging setup was developed using ready-to-use components, and the processed egg samples were passed through a microfluidic flow chip made from double-sided adhesive tape. Holographic videos were then recorded of the passing eggs and debris, which were reconstructed and processed by a custom software program to obtain the egg counts. The performance of the software programs for egg counting was evaluated with SCN-infested soil from two different farms, and the results of the new methods were compared with those obtained through manual counting.

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