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Updated: Jul 19, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Estimating the Feeding Time of Individual Broilers via Convolutional Neural Network and Image Processing.

Amin Nasiri¹, Ahmad Amirivojdan¹, Yang Zhao²

¹Department of Biosystems Engineering and Soil Science, University of Tennessee, Knoxville, TN 37996, USA.

Animals : an Open Access Journal From MDPI

|August 12, 2023

Summary

Automated broiler feeding time monitoring using a convolutional neural network (CNN) achieved 87.3% accuracy. This technology offers real-time insights for improved poultry farm management and resource utilization.

Keywords:

YOLO broiler feeding time image processing

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Area of Science:

Animal Science
Computer Vision
Agricultural Engineering

Background:

Feeding behavior is a key indicator of broiler welfare and farm management efficiency.
Manual monitoring of poultry behavior is labor-intensive and time-consuming, especially in large-scale operations.
Automated systems are needed to overcome the limitations of visual human observation in poultry farms.

Purpose of the Study:

To develop and evaluate a convolutional neural network (CNN)-based model for automatically estimating individual broiler feeding times.
To assess the accuracy of a novel algorithm in recognizing broiler head positions within feeding pans.
To provide a real-time tool for monitoring broiler feeding behavior in commercial poultry settings.

Main Methods:

A You Only Look Once (YOLO) model was trained on 1500 labeled images to detect broiler heads.
A Euclidean distance-based tracking algorithm was implemented to follow detected broiler heads.
The algorithm determined feeding time by identifying when a broiler's head was positioned inside the feeder.
Performance was evaluated using three 1-minute labeled videos.

Main Results:

The developed algorithm achieved an overall feeding time estimation accuracy of 87.3% per broiler visit to the feeding pan.
The system successfully tracked individual broiler heads in the feeding environment.
The CNN-based approach demonstrated potential for real-time application.

Conclusions:

The proposed automated system accurately estimates broiler feeding time, offering a valuable tool for poultry management.
This technology can enhance the understanding of poultry resource usage and welfare indicators.
The algorithm provides a scalable and efficient alternative to manual behavioral monitoring in broiler farms.