Live Weight Prediction of Cattle Based on Deep Regression of RGB-D Images

Alexey Ruchay (), Vitaly Kober, Konstantin Dorofeev, Vladimir Kolpakov, Alexey Gladkov and Hao Guo
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Alexey Ruchay: Federal Research Centre of Biological Systems and Agro-Technologies of the Russian Academy of Sciences, 460000 Orenburg, Russia
Vitaly Kober: Department of Mathematics, Chelyabinsk State University, 454001 Chelyabinsk, Russia
Konstantin Dorofeev: Federal Research Centre of Biological Systems and Agro-Technologies of the Russian Academy of Sciences, 460000 Orenburg, Russia
Vladimir Kolpakov: Federal Research Centre of Biological Systems and Agro-Technologies of the Russian Academy of Sciences, 460000 Orenburg, Russia
Alexey Gladkov: Department of Mathematics, Chelyabinsk State University, 454001 Chelyabinsk, Russia
Hao Guo: College of Land Science and Technology, China Agricultural University, Beijing 100083, China

Agriculture, 2022, vol. 12, issue 11, 1-17

Abstract: Predicting the live weight of cattle helps us monitor the health of animals, conduct genetic selection, and determine the optimal timing of slaughter. On large farms, accurate and expensive industrial scales are used to measure live weight. However, a promising alternative is to estimate live weight using morphometric measurements of livestock and then apply regression equations relating such measurements to live weight. Manual measurements on animals using a tape measure are time-consuming and stressful for the animals. Therefore, computer vision technologies are now increasingly used for non-contact morphometric measurements. The paper proposes a new model for predicting live weight based on augmenting three-dimensional clouds in the form of flat projections and image regression with deep learning. It is shown that on real datasets, the accuracy of weight measurement using the proposed model reaches 91.6%. We also discuss the potential applicability of the proposed approach to animal husbandry.

Keywords: live body weight; prediction; image regression; cattle; deep learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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