Detection and Quantification of Root-Knot Nematode (Meloidogyne Spp.) Eggs From Tomato Plants Using Image Analysis
Article
Article Title | Detection and Quantification of Root-Knot Nematode (Meloidogyne Spp.) Eggs From Tomato Plants Using Image Analysis |
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ERA Journal ID | 210567 |
Article Category | Article |
Authors | Pun, Top Bahadur, Neupane, Arjun, Koech, Richard and Owen, Kirsty J. |
Journal Title | IEEE Access |
Journal Citation | 10, pp. 123190-123204 |
Number of Pages | 15 |
Year | 2022 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 2169-3536 |
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. |
Keywords | plant-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 Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 300101. Agricultural biotechnology diagnostics (incl. biosensors) |
Byline Affiliations | Central Queensland University |
Centre for Crop Health (Operations) |
https://research.usq.edu.au/item/v096v/detection-and-quantification-of-root-knot-nematode-meloidogyne-spp-eggs-from-tomato-plants-using-image-analysis
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Pun et al 2022 Detection_and_Quantification_Meloidogyne_Spp._Eggs_Tomato_Plants_Image_Analysis IEEE 10, 2169-3536.pdf | ||
License: CC BY 4.0 | ||
File access level: Anyone |
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