Detection and Quantification of Root-Knot Nematode (Meloidogyne Spp.) Eggs From Tomato Plants Using Image Analysis

Article


Pun, Top Bahadur, Neupane, Arjun, Koech, Richard and Owen, Kirsty J.. 2022. "Detection and Quantification of Root-Knot Nematode (Meloidogyne Spp.) Eggs From Tomato Plants Using Image Analysis ." IEEE Access. 10, pp. 123190-123204. https://doi.org/10.1109/ACCESS.2022.3223707
Article Title

Detection and Quantification of Root-Knot Nematode (Meloidogyne Spp.) Eggs From Tomato Plants Using Image Analysis

ERA Journal ID210567
Article CategoryArticle
AuthorsPun, Top Bahadur, Neupane, Arjun, Koech, Richard and Owen, Kirsty J.
Journal TitleIEEE Access
Journal Citation10, pp. 123190-123204
Number of Pages15
Year2022
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN2169-3536
Digital Object Identifier (DOI)https://doi.org/10.1109/ACCESS.2022.3223707
Web Address (URL)https://ieeexplore.ieee.org/document/9956798?source=authoralert
Abstract

Root-knot nematodes (Meloidogyne spp.; RKN) are major plant-parasitic nematodes that cause significant loss to agricultural production. An accurate assessment of the RKN population density at a field level is crucial for decisions about the application of control measures to minimise yield losses. Traditionally, RKN populations are identified and counted by nematologists using a microscope. This method is a specialised, time-consuming process and prone to errors. In our study, we investigated three semiautomated methods to detect and count RKN eggs using image analysis: contour arc (CA), skeleton structure (SS), and extreme point (EP). These methods were used to automate the length measurement of RKN eggs, and the results were compared with traditional methods of quantification. The EP method produced the highest correlation with the manual length measurement of RKN eggs. Further, these methods were used to detect and count RKN eggs to quantify low and highly cluttered images. We estimated the optimal range of the ratio of each method to detect and count RKN eggs. Overall, the EP method computed using mid-width of RKN eggs revealed better detection and counting of RKN eggs as compared to the SS and CA methods. A counting correlation up to R2=0.905 was obtained. This study found that the difference between mid-width and the average width of RKN eggs and soil particles could be used to discriminate 70-80 % of soil particles. Our research thus contributes a new feature that can be used to discriminate or classify objects in object detection techniques.

Keywordsplant-parasitic nematode; root-knot nematode egg; image analysis; nematode egg detection; computer-vison; medial-axis transform; image segmentation; morphological-analysis; root-knot nematode detection
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020300101. Agricultural biotechnology diagnostics (incl. biosensors)
Byline AffiliationsCentral Queensland University
Centre for Crop Health (Operations)
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