Evaluation of using a depth sensor to estimate the weight of finishing pigs

I. C.F.S. Condotta, T. M. Brown-Brandl, K. O. Silva-Miranda

Research output: Contribution to conferencePaperpeer-review


A method of continuously monitoring weight would aid producers by ensuring that all pigs are healthy (gaining weight) and increasing precision at marketing. Therefore, the objective was to develop an electronic method of obtaining pig weights through depth images. Seven hundred and seventy-two images and weights were acquired from four different ages (8, 12, 16, and 21 weeks) of finishing pigs (a mix of gilts and barrows) of three sire-lines (Landrace, Duroc and Yorkshire). Weights ranged from 10.8 – 125.7 kg. The images were analysed using the MATLAB image processing toolbox and summing the columns to calculate the volume. Sixty percent of the data was used for equation development, and 40% was used for testing. Individual equations for weight predictions by volume were developed for gilts and barrows and for the three sire-lines. A global equation using the combined data was developed and then compared with individual equations using the Efroymson’s algorithm. The results showed that there was no significant difference between the global equation and the individual equations for barrows and gilts (p<0.05), and the global equation was also not different from individual equations for each of the sire lines (p<0.05). In addition, the results from the global equation indicate that volume accounted for 99.05% of the variation in weight. Using the test data set, the global equation predicted weights using volume calculated with an average error of 4.6% or 2.2 kg. Therefore, the results of this study show that the depth sensor would be a reasonable approach to continuously monitor weights.

Original languageEnglish (US)
Number of pages8
StatePublished - 2017
Externally publishedYes
Event8th European Conference on Precision Livestock Farming, 2017 - Nantes, France
Duration: Sep 12 2017Sep 14 2017


Conference8th European Conference on Precision Livestock Farming, 2017


  • Cough analysis
  • Real-time recognition
  • Signal processing
  • Spectral analysis

ASJC Scopus subject areas

  • Animal Science and Zoology


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