Chicken weight estimation using deep learning

Boonsong Sutapun; Lawan Sampanporn

doi:10.1117/12.2682031

4 October 2023 Chicken weight estimation using deep learning

Boonsong Sutapun, Lawan Sampanporn

Proceedings Volume 12675, Applications of Machine Learning 2023; 1267516 (2023) https://doi.org/10.1117/12.2682031
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Conference Poster

Abstract

In this work, we utilized deep learning models for depth image regression to predict chicken weights. The dataset consists of annotated 99,427 depth images obtained from 18,706 chickens standing on the weighing scale during the rearing days 21-84. Pretrained models performed regression on the depth image data, including Mobilenet V2, ResNet50 V2, ResNet101 V2, ResNet152 V2, InceptionV3, and Xception. All models performed comparable results regarding mean absolute error (MAE) and mean relative error (MRE); however, Xception performed best with an MAE of 17.2 g and an MRE of 2.52% on the test dataset compared to the reference weight. Based on these results, chicken weight estimation using depth images and deep learning is a promising technique for daily growth rate monitoring for the poultry industry.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Boonsong Sutapun and Lawan Sampanporn "Chicken weight estimation using deep learning", Proc. SPIE 12675, Applications of Machine Learning 2023, 1267516 (4 October 2023); https://doi.org/10.1117/12.2682031

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available