Campus Units

Electrical and Computer Engineering, Plant Pathology and Microbiology

Document Type

Article

Publication Version

Published Version

Publication Date

10-15-2019

Journal or Book Title

PloS ONE

Volume

14

Issue

10

First Page

e0223386

DOI

10.1371/journal.pone.0223386

Abstract

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.

Comments

This article is published as Kalwa U, Legner C, Wlezien E, Tylka G, Pandey S (2019) New methods of removing debris and high-throughput counting of cyst nematode eggs extracted from field soil. PLoS ONE 14(10): e0223386. doi: 10.1371/journal.pone.0223386.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Copyright Owner

Kalwa et al.

Language

en

File Format

application/pdf

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