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Practical Aspects of Weight Measurement Using Image Processing Methods in Waterfowl Production

Author

Listed:
  • Sandor Szabo

    (University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary)

  • Marta Alexy

    (University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary
    Faculty of Informatics, Eotvos Lorand University, 1117 Budapest, Hungary)

Abstract

Precision poultry farming technologies include the analysis of images of poultry flocks using cameras. In large-scale waterfowl farming, these can be used to determine the individual weight of poultry flocks. In our research in a real farming environment, we investigated the cameras fixed to the metal support structure of the barn, located above the suspended bird scales. Camera images of the bird on the weighing cell, taken from a top view, were matched to the weight data measured by the scale. The algorithm was trained on training data sets from a part of the database, and the results were validated with the other part of the database (Training: 60% Validation: 20% Testing: 20%). Three data science models were compared, and the random forest method achieved the highest accuracy and reliability. Our results show that the random forest method gave the most reliable results for determining the individual weights of birds. We found that the housing environment had a strong influence on the applicability of the data collection and processing technology. We have presented that by analyzing carefully collected images, it is possible to determine the individual weights of birds and thus provide valuable information on it.

Suggested Citation

  • Sandor Szabo & Marta Alexy, 2022. "Practical Aspects of Weight Measurement Using Image Processing Methods in Waterfowl Production," Agriculture, MDPI, vol. 12(11), pages 1-14, November.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:11:p:1869-:d:966446
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    References listed on IDEAS

    as
    1. Liangben Cao & Zihan Xiao & Xianghui Liao & Yuanzhou Yao & Kangjie Wu & Jiong Mu & Jun Li & Haibo Pu, 2021. "Automated Chicken Counting in Surveillance Camera Environments Based on the Point Supervision Algorithm: LC-DenseFCN," Agriculture, MDPI, vol. 11(6), pages 1-15, May.
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