ArticleAnimals : an open access journal from MDPI2023
Estimating the Feeding Time of Individual Broilers via Convolutional Neural Network and Image Processing.
Article in Animals : an open access journal from MDPI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Computer vision models for precision poultry farming: A narrative review of behavioral and welfare monitoring studies.Poultry science · 2026Review
- A Narrative Review on Internet of Things and Artificial Intelligence for Poultry Production.Animals : an open access journal from MDPI · 2026Review
- CF-DETR: A Lightweight Real-Time Model for Chicken Face Detection in High-Density Poultry Farming.Animals : an open access journal from MDPI · 2025Article
- Identifying mating events of group-housed broiler breeders via bio-inspired deep learning models.Poultry science · 2025Article
- A Novel Lightweight Framework for Non-Contact Broiler Face Identification in Intensive Farming.Sensors (Basel, Switzerland) · 2025Article
- Deep Learning Methods for Automatic Identification of Male and Female Chickens in a Cage-Free Flock.Animals : an open access journal from MDPI · 2025Article
- The Posture Detection Method of Caged Chickens Based on Computer Vision.Animals : an open access journal from MDPI · 2024Article
- Field Implementation of Precision Livestock Farming: Selected Proceedings from the 2nd U.S. Precision Livestock Farming Conference.Animals : an open access journal from MDPI · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Feeding behavior is one of the critical welfare indicators of broilers. Hence, understanding feeding behavior can provide important information regarding the usage of poultry resources and insights into farm management. Monitoring poultry behaviors is typically performed based on visual human observation. Despite the successful applications of this method, its implementation in large poultry farms takes time and effort. Thus, there is a need for automated approaches to overcome these challenges. Consequently, this study aimed to evaluate the feeding time of individual broilers by a convolutional neural network-based model. To achieve the goal of this research, 1500 images collected from a poultry farm were labeled for training the You Only Look Once (YOLO) model to detect the broilers' heads. A Euclidean distance-based tracking algorithm was developed to track the detected heads, as well. The developed algorithm estimated the broiler's feeding time by recognizing whether its head is inside the feeder. Three 1-min labeled videos were applied to evaluate the proposed algorithm's performance. The algorithm achieved an overall feeding time estimation accuracy of each broiler per visit to the feeding pan of 87.3%. In addition, the obtained results prove that the proposed algorithm can be used as a real-time tool in poultry farms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.