Search research articles

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Determination of milk yield in water buffaloes using multi-class logistic regression and machine learning methods.

Tropical animal health and production·2025

Same author

Automated Classification System Based on YOLO Architecture for Body Condition Score in Dairy Cows.

Veterinary sciences·2024

Same author

Determination of Non-Digestible Parts in Dairy Cattle Feces Using U-NET and F-CRN Architectures.

Veterinary sciences·2023

See all related articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Aug 24, 2025

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

Deep Learning Based Egg Fertility Detection.

Kerim Kürşat Çevik¹, Hasan Erdinç Koçer², Mustafa Boğa³

¹Faculty of Applied Sciences, Akdeniz University, Antalya 07070, Turkey.

Veterinary Sciences

|October 26, 2022

Summary

Deep learning models accurately identify fertile chicken eggs using incubator images. This automated system simplifies fertility classification and segmentation, improving early-stage detection.

Keywords:

Mask R-CNN deep learning egg fertility incubator images

More Related Videos

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

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

Published on: March 3, 2018

Windowing Chicken Eggs for Developmental Studies

Windowing Chicken Eggs for Developmental Studies

Published on: October 1, 2007

Related Experiment Videos

Last Updated: Aug 24, 2025

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

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

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

Published on: March 3, 2018

Windowing Chicken Eggs for Developmental Studies

Windowing Chicken Eggs for Developmental Studies

Published on: October 1, 2007

Area of Science:

Agricultural Science
Computer Vision
Artificial Intelligence

Background:

Fertile egg recognition is crucial for efficient poultry farming.
Current methods for fertility assessment can be labor-intensive and time-consuming.
Automated systems can improve accuracy and speed in egg fertility determination.

Purpose of the Study:

To implement deep learning (DL) for fertile egg recognition from incubator images.
To classify chicken eggs by segmentation and fertility status using a Mask R-CNN approach.
To develop a single DL model for detection, classification, and segmentation of fertile and infertile eggs.

Main Methods:

Utilized a Mask R-CNN-based deep learning model.
Applied the model to images from incubator settings.
Evaluated performance using Average Precision (AP) and Intersection over Union (IoU) metrics.
Tested with datasets of 5 and 18 fertile eggs.

Main Results:

Achieved accurate identification of fertile eggs with an optimal IoU threshold of 0.7.
The system correctly determined all fertile eggs by the third day in both test datasets.
Demonstrated the capability of a single DL model for detection, classification, and segmentation.

Conclusions:

The developed DL system offers a highly successful and efficient solution for fertile egg recognition.
The Mask R-CNN approach provides accurate segmentation and classification of egg fertility.
Future work should focus on optimizing camera and lighting for enhanced segmentation performance.