Utilizing Image Recognition Methods for the Identification and Counting of Live Nematodes in Petri Dishes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F24%3A73626378" target="_blank" >RIV/61989592:15310/24:73626378 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10569308/metrics#metrics" target="_blank" >https://ieeexplore.ieee.org/document/10569308/metrics#metrics</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICCC62069.2024.10569308" target="_blank" >10.1109/ICCC62069.2024.10569308</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Utilizing Image Recognition Methods for the Identification and Counting of Live Nematodes in Petri Dishes

Popis výsledku v původním jazyce

This article addresses a system designed for identifying and quantifying several species of nematodes, free-living and parasitic, placed on Petri dishes using modern image recognition methods. Traditional methods of counting nematodes are often time-consuming and prone to errors, leading to inaccurate biological and ecological study results. Our method employs advanced machine learning algorithms and computer vision to detect and count live nematodes automatically. The process utilizes algorithms to recognize specific larval characteristics, such as size and shape. This allows for high accuracy and efficiency while minimizing human intervention. The results demonstrate that our approach is robust and precise and can be widely utilized in biological research and applications.

Název v anglickém jazyce

Utilizing Image Recognition Methods for the Identification and Counting of Live Nematodes in Petri Dishes

Popis výsledku anglicky

This article addresses a system designed for identifying and quantifying several species of nematodes, free-living and parasitic, placed on Petri dishes using modern image recognition methods. Traditional methods of counting nematodes are often time-consuming and prone to errors, leading to inaccurate biological and ecological study results. Our method employs advanced machine learning algorithms and computer vision to detect and count live nematodes automatically. The process utilizes algorithms to recognize specific larval characteristics, such as size and shape. This allows for high accuracy and efficiency while minimizing human intervention. The results demonstrate that our approach is robust and precise and can be widely utilized in biological research and applications.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 2024 25th International Carpathian Control Conference (ICCC)

ISBN

979-8-3503-5070-8

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

Piscataway

Místo konání akce

Krynica Zdrój

Datum konání akce

22. 5. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001268679000017