Application of Mamdani Fuzzy Inference System in Poultry Weight Estimation

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...

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Bibliographic Details
Published in:Animals (Basel) 2023-07, Vol.13 (15), p.2471
Main Authors: Küçüktopçu, Erdem, Cemek, Bilal, Simsek, Halis
Format: Article
Language:English
Subjects:
Accuracy
Animal welfare
Artificial intelligence
Birds
broiler
Costs (Law)
Decision making
defuzzification
expert system
Fuzzy sets
linguistic variables
Linguistics
Livestock farms
Meat products
Neural networks
Poultry
Research methodology
Set theory
Ventilation
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Summary: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.
ISSN:2076-2615
2076-2615
DOI:10.3390/ani13152471