Campus Units

Electrical and Computer Engineering, Plant Pathology and Microbiology

Document Type

Article

Publication Version

Published Version

Publication Date

2021

Journal or Book Title

Scientific Reports

Volume

11

First Page

3212

DOI

10.1038/s41598-021-82261-w

Abstract

Soybeans are an important crop for global food security. Every year, soybean yields are reduced by numerous soybean diseases, particularly the soybean cyst nematode (SCN). It is difficult to visually identify the presence of SCN in the field, let alone its population densities or numbers, as there are no obvious aboveground disease symptoms. The only definitive way to assess SCN population densities is to directly extract the SCN cysts from soil and then extract the eggs from cysts and count them. Extraction is typically conducted in commercial soil analysis laboratories and university plant diagnostic clinics and involves repeated steps of sieving, washing, collecting, grinding, and cleaning. Here we present a robotic instrument to reproduce and automate the functions of the conventional methods to extract nematode cysts from soil and subsequently extract eggs from the recovered nematode cysts. We incorporated mechanisms to actuate the stage system, manipulate positions of individual sieves using the gripper, recover cysts and cyst-sized objects from soil suspended in water, and grind the cysts to release their eggs. All system functions are controlled and operated by a touchscreen interface software. The performance of the robotic instrument is evaluated using soil samples infested with SCN from two farms at different locations and results were comparable to the conventional technique. Our new technology brings the benefits of automation to SCN soil diagnostics, a step towards long-term integrated pest management of this serious soybean pest.

Comments

This article is published as Legner, C.M., Tylka, G.L. & Pandey, S. Robotic agricultural instrument for automated extraction of nematode cysts and eggs from soil to improve integrated pest management. Sci Rep 11, 3212 (2021). doi: 10.1038/s41598-021-82261-w.

Creative Commons License

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

Copyright Owner

The Author(s)

Language

en

File Format

application/pdf

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