ArticleAnimals : an open access journal from MDPI2023
Application of Mamdani Fuzzy Inference System in Poultry Weight Estimation.
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 4 papers.
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Who cites it
4 citing papers in PubMed.
- AI-Driven prediction of body weight in chicken genotypes with different growth rates.Tropical animal health and production · 2026Article
- Application of Supervised Machine Learning Techniques and Digital Image Analysis for Predicting Live Weight in Anadolu-T Broilers.Animals : an open access journal from MDPI · 2025Article
- Progress and trends of non-contact detection methods for poultry growth information: A review.Poultry science · 2025Review
- The Posture Detection Method of Caged Chickens Based on Computer Vision.Animals : an open access journal from MDPI · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
Traditional manual weighing systems for birds on poultry farms are time-consuming and may compromise animal welfare. Although automatic weighing systems have been introduced as an alternative, they face limitations in accurately estimating the weight of heavy birds. Therefore, exploring alternative methods that offer improved efficiency and precision is necessary. One promising solution lies in the application of AI, which has the potential to revolutionize various aspects of poultry production and management, making it an indispensable tool for the modern poultry industry. This study aimed to develop an AI approach based on the FL model as a viable solution for estimating poultry weight. By incorporating expert knowledge and considering key input variables such as indoor temperature, indoor humidity, and feed consumption, FL-based models were developed with different configurations using Mamdani inferences and evaluated across eight different rearing periods in Samsun, Türkiye. This study's results demonstrated the effectiveness of FL-based models in estimating poultry weight. The models achieved varying average absolute error values across different age groups of broilers, ranging from 0.02% to 5.81%. These findings suggest that FL-based methods hold promise for accurate and efficient poultry weight estimation. This study opens up avenues for further research in the field, encouraging the exploration of FL-based approaches for improved poultry weight estimation in poultry farming operations.
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Registered trials
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