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

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.