Data · dataset · 2025
Microscopic Image Dataset of Plant-Parasitic Nematodes
Listed in Teesside University Research Data Repository
This dataset features microscopic images of plant-parasitic nematodes, categorized into 11 genera.
Description
It provides a valuable resource for research on nematode identification and classification, supporting the development of AI-powered identification tools. With annotated samples and diverse image perspectives, this dataset offers a foundation for advancing pest management strategies in agriculture.
Image size: 1280 x 1024 pixel Data format: .jpg Number of images: 1016 images Class: Xiphinema, Meloidogyne, Trichodorus, Hirschmanniella, Hoplolaimus, Criconemoides, Hemicycliophora, Radopholus, Pratylenchus, Criconema, and Helicotylenchus Data source location: Central Java and Yogyakarta, Indonesia How data were acquired: Nematode specimens were collected from the roots of several crops, including rice, tomatoes, melons, chili, and eggplants, which exhibited clear signs of infestation, as well as from the surrounding soil.
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Extraction was performed using the modified Whitehead-Tray method. Observation and identification were carried out using an Olympus CX-31 light microscope at magnifications ranging from 4x to 100x. An optical system connected to both a microscope and laptop via compatible software was used to capture detailed nematode images.
Identification was carried out by expert nematologist based on key morphological features—including the mouth, stylet with its knob, tail, cuticle, and the positioning of the esophagus, intestine, and reproductive organs—following the descriptions provided by Mai and Lyon (1968) and Mekete et al (2012). This dataset supplements research article by N. H. Shabrina, R.A. Lika, S. Indarti, Deep learning models for automatic identification of plant-parasitic nematode, Artif.
Intell. Agric. 7 (2023) 1–12. doi.org/10.1016/j.aiia.2022.12.002. References Mai, W.F., and Lyon, H.H. 1968.
Pictorial key to the genus of plant parasitic nematodes. Third Edition. Cornell University, Ithaca, New York Mekete T., Dababat A., Sekora N., Akyazi F., Abebe E. (comps). 2012.
Identification key for agriculturally important plant-parasitic nematodes Prepared for the International Nematode Diagnosis and Identification Course 2012–A Manual for Nematology. Mexico, D.F.: CIMMYT.
Links
Where it is published
- DOI doi.org/10.17632/cck8yxj3xw.2 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Artificial intelligence · Computer Science & AI · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Image 75%
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/cck8yxj3xw.2 | 10 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:group:4602 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Artificial Intelligence'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:engineering | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:humanities | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[modality].local:modality:image | enrichment · researchdata tees ac uk | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |