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ARS Home » Plains Area » Clay Center, Nebraska » U.S. Meat Animal Research Center » Nutrition, Growth and Physiology » Research » Publications at this Location » Publication #342625
Title: Evaluation of a depth sensor for mass estimation of growing and finishing pigs

Author
item CONDOTTA, ISABELLA - BRAZIL UNIVERSITY
item BROWN-BRANDL, TAMI
item SILVA-MIRANDA, K - BRAZIL UNIVERSITY
item STINN, JOHN - IOWA SELECT FARMS

Submitted to: Biosystems Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/1/2018
Publication Date: 3/31/2018
Citation: Condotta, I.C.F.S., Brown-Brandl, T.M., Silva-Miranda, K.O., Stinn, J.P. 2018. Evaluation of a depth sensor for mass estimation of growing and finishing pigs. Biosystems Engineering. 173:11-18. https://doi.org/10.1016/j.biosystemseng.2018.03.002.
DOI: https://doi.org/10.1016/j.biosystemseng.2018.03.002

Interpretive Summary: Continuously monitoring animal weight would be beneficial to producers by ensure all animals are gaining weight and would help in the precision of marketing pigs. Electronically monitoring weight without moving the pigs to the scale would also make weighing all pigs possible. Therefore, the development of methods for monitoring the physical conditions of animals would improve animal well-being and maximize the profitability of swine production. This research was conducted in order to validate the use of depth images to predict live animal weight in grow-finish pigs. Nine hundred and twenty depth images and weights were collected from a population of grow - finishing pigs (equally divided between barrows and gilts). Three commercial sire lines (Landrace, Duroc, and Yorkshire) were mated to Landrace x Yorkshire. Depth images were used to calculate pigs’ volumes. An equation was developed to predicted pig weight from the calculated volumes. It was determined that one equation could be used to predict the weight regardless of sire lines or sex. The error associated with the weight prediction was approximately 5%.

Technical Abstract: A method of continuously monitoring animal mass would aid producers by ensuring all pigs are gaining mass and would increase the precision of marketing pigs. Therefore, the development of methods for monitoring the physical conditions of animals would improve animal well-being and maximise the profitability of swine production. The objective of this research was to validate the use of depth images in predicting live animal mass. Seven hundred and seventy-two depth images and mass measurements were collected from a population of grow-finish pigs (equally divided between barrows and gilts). Three commercial sire lines (Landrace, Duroc, and Yorkshire) were equally represented. The pigs' volumes were calculated from the depth image. Linear equations were developed to predict mass from volume. Independent equations were developed for both gilts and barrows, each of the three commercial sire lines used, and a global equation for all combined data. Efroymson's algorithm was used to test for differences between the global equation and the two equations for the gilts and barrows and between the three commercial sire lines. 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 no different from individual equations for each of the three sire lines (p < 0.05). The global equation was developed to predict mass from a depth sensor with an R2 of 0.9905. In conclusion, it appears that the depth sensor would be a reasonable approach to continuously monitor pig mass.