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Weighted Mean00:57

Weighted Mean

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
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Related Experiment Video

Updated: Jul 19, 2025

A Do-it-yourself System for Scheduled Feeding of Laboratory Rodents in Their Home Cage
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Application of Mamdani Fuzzy Inference System in Poultry Weight Estimation.

Erdem Küçüktopçu1, Bilal Cemek1, Halis Simsek2

  • 1Department of Agricultural Structures and Irrigation, Ondokuz Mayıs University, Samsun 55139, Türkiye.

Animals : an Open Access Journal From MDPI
|August 12, 2023
PubMed
Summary

Artificial intelligence (AI) using fuzzy logic (FL) models offers a precise way to estimate poultry weight, overcoming limitations of traditional methods. This AI approach improves efficiency and accuracy in poultry farming operations.

Keywords:
artificial intelligencebroilerdefuzzificationexpert systemlinguistic variables

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Area of Science:

  • Agricultural Science
  • Artificial Intelligence
  • Animal Science

Background:

  • Traditional manual weighing systems for poultry are inefficient and can impact animal welfare.
  • Existing automatic weighing systems struggle with accurate weight estimation for heavier birds.
  • There is a need for advanced methods to improve poultry weight estimation efficiency and precision.

Purpose of the Study:

  • To develop and evaluate an Artificial Intelligence (AI) approach using Fuzzy Logic (FL) models for estimating poultry weight.
  • To assess the viability of FL models in poultry production and management.
  • To explore AI as an indispensable tool for modern poultry farming.

Main Methods:

  • Development of FL-based models incorporating expert knowledge.
  • Inclusion of key input variables: indoor temperature, humidity, and feed consumption.
  • Utilization of Mamdani inference for model configuration and evaluation across eight rearing periods.

Main Results:

  • FL-based models demonstrated effectiveness in estimating poultry weight.
  • Average absolute error values ranged from 0.02% to 5.81% across different broiler age groups.
  • Successful application of FL models in a real-world poultry farming setting in Samsun, Türkiye.

Conclusions:

  • FL-based methods show significant promise for accurate and efficient poultry weight estimation.
  • AI, specifically FL, can revolutionize poultry production management.
  • Further research into FL approaches for enhanced poultry weight estimation is encouraged.