An automatic data acquisition system for acquiring training data for a deep learning algorithm for individual cow intake prediction

R. Bezen, Y. Edan, I. Halachmi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Individual feed intake of dairy cows is an important, currently unavailable, variable in commercial dairies. Earlier systems developed were either costly or unreliable enough for commercial farms. This research developed a low-cost individual feed intake system using RGB-D cameras and deep learning algorithm. Depth and colour images are produced from an RGB-D camera, and are used to build a CNNs (Convolutional Neural Networks) regression model for weight intake prediction. To provide training data, an automatic data acquisition system was designed to collect a wide range of food weights, in different configurations and conditions (indoor, outdoor, direct-sun). The system included a scale and a micro-controller set in the Volcani research dairy facility, an open cowshed with Holstein cows, eating Total Mix Ration. With this setup, 28,761 data were collected over seven days. Additional data were created by data augmentation methods. The model was evaluated on a test-dataset acquired in the same dairy farm. The model was tested for different combinations of training data (direct-sun/outdoor) to evaluate the importance of the data diversity. Per meal, mean absolute and square errors were 0.127 kg, and 0.034 kg2, respectively, the consumed amount of feed measured in range of 0-8 kg. The sensitivity analysis shows that the amount and diversity of data is important for model training. Better results were achieved for the model that was trained with high diversity data. The results suggest that cameras and CNNs are feasible for individual feed intake measurement on the dairy farm.

Original languageEnglish
Title of host publicationPrecision Livestock Farming 2019 - Papers Presented at the 9th European Conference on Precision Livestock Farming, ECPLF 2019
EditorsBernadette O'Brien, Deirdre Hennessy, Laurence Shalloo
Pages284-291
Number of pages8
ISBN (Electronic)9781841706542
StatePublished - 1 Jan 2019
Event9th European Conference on Precision Livestock Farming, ECPLF 2019 - Cork, Ireland
Duration: 26 Aug 201929 Aug 2019

Conference

Conference9th European Conference on Precision Livestock Farming, ECPLF 2019
Country/TerritoryIreland
CityCork
Period26/08/1929/08/19

Keywords

  • 3D camera
  • Deep learning
  • Individual cow feed intake
  • Machine vision
  • Precision livestock farming (PLF)

ASJC Scopus subject areas

  • Animal Science and Zoology

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