3D Computer-vision system for automatically estimating heifer height and body mass

Oron Nir, Yisrael Parmet, Daniel Werner, Gaby Adin, Ilan Halachmi

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

Animal dimensions play a vital role in providing data in support of management decisions regarding livestock. Nevertheless, dairy heifers are still measured manually, a time consuming and stressful task for both the farmer and the animal. This research suggests an approach that utilises a fully automated system to measure a heifer's body. The methodology involves a single low-cost Microsoft Kinect V2 Time-of-Flight 3D sensor, computer vision, machine learning, and object recognition using ellipse fitting with quantile regression as part of the feature extraction phase. The camera was installed at the Volcani Center dairy farm, on the ceiling above a free-walk path between the feeding zone and lying area. Video data of 107 Israeli Holstein heifers were recorded and validated against “gold references” (human-observed body mass, hip height and withers height). The tested system improved the normalised Root Mean Squared Error of estimates over the state of the art models by 70.4%, 69.8% and 42.6% for withers height, hip height, and body mass respectively. The models were also validated on a different dairy farm and yielded similar results. The methodology, may be adapted and applied to other elliptically shaped animal bodies, such as sheep, pigs, horses, and buffalo.

Original languageEnglish
Pages (from-to)4-10
Number of pages7
JournalBiosystems Engineering
Volume173
DOIs
StatePublished - 1 Sep 2018

Keywords

  • Computer vision
  • Dairy heifer body measurement
  • Microsoft Kinect v2
  • Quantile regression
  • Robust ellipse fitting

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