Real-time detection and identification of nematode eggs genus and species through optical imaging

Qazi, Farah; Khalid, Asma; Poddar, Arpita; Broadway, David A.; Nadarajah, Arunan; Aburto‐Medina, Arturo; Shahsavari, Esmaeil; Shukla, Ravi; Prawer, S.; Ball, Andrew S.; Tomljenovic‐Hanic, Snjezana

doi:10.1038/s41598-020-63747-5

Cited by 17 publications

(8 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They also reported on the potential of these spectroscopic fingerprints to discriminate between different fungal species within the genus Saccharomyces without the need for fluorescent staining. Qazi et al [ 31 ] built on this and demonstrated that eggs of different helminths revealed characteristic florescence when illuminated at different wavelengths ranging from white light to infrared. They also showed that differences in florescence lifetime values (decay in florescence intensity) were diagnostic of the species considered, Ascaris lumbricoides and A. suum .…”

Section: Morphological and Image-based Analysesmentioning

confidence: 99%

“…They also showed that differences in florescence lifetime values (decay in florescence intensity) were diagnostic of the species considered, Ascaris lumbricoides and A. suum . Qazi et al [ 31 ] concluded that spectroscopic features and lifetime value measurements of autofluorescence in nematodes are promising tools in the taxonomy of these organisms.…”

Section: Morphological and Image-based Analysesmentioning

confidence: 99%

See 1 more Smart Citation

Nematode Identification Techniques and Recent Advances

Bogale

Baniya

DiGennaro

2020

Plants

View full text Add to dashboard Cite

Nematodes are among the most diverse but least studied organisms. The classic morphology-based identification has proved insufficient to the study of nematode identification and diversity, mainly for lack of sufficient morphological variations among closely related taxa. Different molecular methods have been used to supplement morphology-based methods and/or circumvent these problems with various degrees of success. These methods range from fingerprint to sequence analyses of DNA- and/or protein-based information. Image analyses techniques have also contributed towards this success. In this review, we highlight what each of these methods entail and provide examples where more recent advances of these techniques have been employed in nematode identification. Wherever possible, emphasis has been given to nematodes of agricultural significance. We show that these alternative methods have aided nematode identification and raised our understanding of nematode diversity and phylogeny. We discuss the pros and cons of these methods and conclude that no one method by itself provides all the answers; the choice of method depends on the question at hand, the nature of the samples, and the availability of resources.

show abstract

Section: Morphological and Image-based Analysesmentioning

confidence: 99%

Section: Morphological and Image-based Analysesmentioning

confidence: 99%

Nematode Identification Techniques and Recent Advances

Bogale

Baniya

DiGennaro

2020

Plants

View full text Add to dashboard Cite

show abstract

“…The one way-ANOVA test performed on count from CA, TS, and SG method found F-statistic = 4.440 and p-value = 0.004. In addition, three methods were investigated with ratio variation from [19][20][21][22][23][24][25][26][27][28][29][30]. The ratio between 10-35 was found to have the lowest number of misidentified and missed RKN.…”

Section: Resultsmentioning

confidence: 99%

“…Some of the studies focused on detecting nematodes eggs. The author of [25] investigated photoluminescence technology to identify the nematodes genus and species. They found that species of nematode eggs can be discriminated in terms of emission spectrum.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Root-Knot Nematode Infestation in Tomato Using Digital Image Analysis

2021

View full text Add to dashboard Cite

Tomato is the most popular vegetable globally. However, in certain conditions, the vegetable is susceptible to plant parasites such as the root-knot nematode (RKN; Meloidogyne spp.). A proper detection method is required to identify RKN and eliminate related diseases. The traditional manual quantification of RKN using a microscope is a time-consuming and laborious task. This study aims to develop a semi-automated method to discern and quantify RKN based on size using an image analysis method. The length of RKN was assessed using three novel approaches: contour arc (CA), thin structure (TS), and skeleton graph (SG) methods. These lengths were compared with the manual measurement of RKN length. The study showed that the RKN length obtained by manual measurement was highly correlated to the length based on this method, with R2 of 0.898, 0.875, and 0.898 for the CA, TS, and SG methods, respectively. These approaches were further tested to detect RKN on 517 images. The manual and automated counting comparison revealed a coefficient of determination R2 = 0.857, 0.835 and 0.828 for CA, TS, and SG methods, respectively. The one-way ANOVA test on counting revealed F-statistic = 4.440 and p-value = 0.004. The ratio of length to width was investigated further at different ranges. The optimal result was found to occur at ratio range between 10–35. The CA, TS, and SG methods attained the highest R2 of 0.965, 0.958, and 0.973, respectively. This study found that the SG method is most suitable for detecting and counting RKN. This method can be applied to detect RKN or other nematodes on severely infected crops and root vegetables, including sweet potato and ginger. The study significantly helps in quantifying pests for rapid farm management and thus minimise crop and vegetable losses.

show abstract

“…The hybridoma technique becomes more challenging as the number of nematode samples increases. Because the restructure of antigen-site-binding sequences for comparative observations is feasible through the single B cell sequence receptor (ScBCR-Secq), this line of nematode detection algorithms can be reconfigured using next-generation advanced technologies [91].…”

Section: Serological Analysis or Use Of Antibodiesmentioning

confidence: 99%

Advances in Nematode Identification: A Journey from Fundamentals to Evolutionary Aspects

et al. 2022

View full text Add to dashboard Cite

Nematodes are non-segmented roundworms evenly distributed with various habitats ranging to approximately every ecological extremity. These are the least studied organisms despite being the most diversified group. Nematodes are the most critical equilibrium-maintaining factors, having implications on the yield and health of plants as well as well-being of animals. However, taxonomic knowledge about nematodes is scarce. As a result of the lack of precise taxonomic features, nematode taxonomy remains uncertain. Morphology-based identification has proved inefficacious in identifying and exploring the diversity of nematodes, as there are insufficient morphological variations. Different molecular and new evolving methodologies have been employed to augment morphology-based approaches and bypass these difficulties with varying effectiveness. These identification techniques vary from molecular-based targeting DNA or protein-based targeting amino acid sequences to methods for image processing. High-throughput approaches such as next-generation sequencing have also been added to this league. These alternative approaches have helped to classify nematodes and enhanced the base for increased diversity and phylogeny of nematodes, thus helping to formulate increasingly more nematode bases for use as model organisms to study different hot topics about human well-being. Here, we discuss all the methods of nematode identification as an essential shift from classical morphometric studies to the most important modern-day and molecular approaches for their identification. Classification varies from DNA/protein-based methods to the use of new emerging methods. However, the priority of the method relies on the quality, quantity, and availability of nematode resources and down-streaming applications. This paper reviews all currently offered methods for the detection of nematodes and known/unknown and cryptic or sibling species, emphasizing modern-day methods and budding molecular techniques.

show abstract

Real-time detection and identification of nematode eggs genus and species through optical imaging

Cited by 17 publications

References 53 publications

Nematode Identification Techniques and Recent Advances

Nematode Identification Techniques and Recent Advances

Quantification of Root-Knot Nematode Infestation in Tomato Using Digital Image Analysis

Advances in Nematode Identification: A Journey from Fundamentals to Evolutionary Aspects

Contact Info

Product

Resources

About