Non-contact method for cattle weight estimation based on deep-learned convolutional neural network

Authors

  • Oleh Iliunin Kharkiv National University of Radio Electronics
  • Oleg Rudenko Kharkiv National University of Radio Electronics
  • Oleg Kobylin Kharkiv National University of Radio Electronics
  • Nataliia Serdiuk Kharkiv National University of Radio Electronics
  • Andrii Cherkasov Kharkiv National University of Radio Electronics

Keywords:

augmentation, data set, deep learned convolutional neural network

Abstract

Improving the efficiency of meat and dairy farming and introducing innova-tive digital technologies into the industry are urgent tasks today. Rapid assessment of cattle weight is the main indicator for adjusting the diet and monitoring animal health. The proposed method uses the geometric dimen-sions of cows from camera images processed by computer technologies and is based on a deep learning convolutional neural network based on the Effi-cientNetB3 and Resnet18 models for segmentation and formation of key points on animal images for further identification of their characteristics. The developed system allows automating the process of measuring cattle weight, which significantly reduces labor costs and increases the economic efficiency of farms. The use of an intelligent information system (IIS) allows obtain-ing more accurate estimates of animal weight compared to traditional meth-ods, which showed an adequacy of RMSE = 0.93% (root mean squared er-ror) on an extended data set. After adaptation, the application can be used to estimate the weight of other farm animals.

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Published

2025-12-05

How to Cite

Iliunin, O. ., Rudenko, O. ., Kobylin, O. ., Serdiuk , N. ., & Cherkasov, A. . (2025). Non-contact method for cattle weight estimation based on deep-learned convolutional neural network. International Congress of Knowledge and Innovation - Ciki, 1(1). Retrieved from https://proceeding.ciki.ufsc.br/index.php/ciki/article/view/1742

Issue

Vol. 1 No. 1 (2025): GFIC-ICKM-CIKI 2025 Abstracts

Section

Full Paper

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