Application of Mamdani Fuzzy Inference System in Poultry Weight Estimation

SIMPLE SUMMARY: With the rapid technological advances, the application of artificial intelligence (AI) has witnessed significant growth in the agricultural industry, specifically in the poultry sector. The use of AI in estimating poultry weight can significantly impact production economics and overall efficiency in the poultry sector. Therefore, this paper presents an innovative AI approach based on the fuzzy logic (FL) method for estimating poultry weight. The FL models were created using expert knowledge and key input variables such as indoor temperature, humidity, and feed consumption. This study’s findings demonstrate that FL-based methods exhibit great promise for achieving accurate and efficient poultry weight estimation. Integrating the FL technique in the poultry industry can bring numerous benefits, including improved decision-making processes, enhanced efficiency, and reduced costs. 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.