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Fertilization

Fertilization

During fertilization, an egg and sperm cell fuse to create a new diploid structure. In humans, the process occurs once the egg has been released from the ovary, and travels into the fallopian tubes. The process requires several key steps: 1) sperm present in the genital tract must locate the egg; 2) once there, sperm need to release enzymes to help them burrow through the protective zona pellucida of the egg; and 3) the membranes of a single sperm cell and egg must fuse, with the sperm...

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Updated: Jun 24, 2025

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

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Published on: March 3, 2018

FEDM: a convolutional neural network based fertilised egg detection model.

Z Gong¹, M Wang¹, J Song¹

¹College of Information Science and Technology, Nanjing Forestry University, Nanjing, China.

British Poultry Science

|June 3, 2024

Summary

This summary is machine-generated.

A new AI model, FEDM, accurately identifies fertilised goose eggs using YOLOv5, improving detection rates and offering a cost-effective solution for the poultry industry. This technology enhances hatching success and economic value.

Keywords:

C3_DCNv3 Dyhead Fertilised egg detection MPDIoU SENet YOLOv5

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Area of Science:

Agricultural Science
Computer Vision
Artificial Intelligence

Background:

Goose egg production is economically significant globally, with fertilised egg quality vital for poultry industry success.
Current fertilised egg identification methods are costly and have strict environmental requirements, hindering widespread adoption.
A low-cost, scalable system for identifying fertilised eggs is needed to support the poultry industry.

Purpose of the Study:

To develop an improved, low-cost object detection model for identifying fertilised goose eggs suitable for large-scale application.
To enhance the feature selection, detection capabilities, and representation capacity of existing object detection models.
To simplify the calculation process of the loss function for improved efficiency.

Main Methods:

An enhanced object detection model, FEDM, was developed based on the YOLOv5 architecture.
The YOLOv5 network was improved with the SENet attention mechanism and C3_DCNv3 for better feature selection and blood vessel detection.
Dyhead was integrated to boost the object detection head's capacity, and MPDIoU replaced the original loss function.

Main Results:

The FEDM model achieved an average precision of 96.7% on an augmented dataset, a 5.5% improvement over YOLOv5s.
FEDM demonstrated superior detection performance across various shooting angles and maintained high detection speeds compared to YOLOv5.
Significant advancements were observed in the detection rate of fourth-day fertilised eggs using FEDM.

Conclusions:

The FEDM model offers a cost-effective and efficient solution for identifying fertilised goose eggs, addressing limitations of current methods.
The enhanced model provides practical application value by enabling savings and optimizing space utilisation in large-scale testing.
FEDM's improved accuracy and speed contribute to the sustained development and economic value of the global poultry industry.