title: Short communication: Measuring feed volume and weight by machine vision.
abstract: Individual dairy cow feed intake is closely related to the health and productive output of each cow, with healthy cows generally eating more feed than unhealthy cows. Incorporating the use of an automated system to monitor feed consumption for each cow may be beneficial for dairy farm management. This study examined the use of an inexpensive 3-dimensional video camera to measure feed volume, from which we derived feed weight. Proof-of-concept testing was conducted to determine the effectiveness and capability of the machine vision feed-scanning system and its possible use in feed intake monitoring. Such systems are ideal because they do not impede the workflow of the farm or interrupt feeding behavior. This is an improvement over existing systems that are labor and cost intensive. Our conducted experiments involve measuring feed volume at known weights, up to 22.68 kg, with the resulting volume and weight values analyzed by means of linear and quadratic least squares t-test regression analysis. The effects of feed positioning in the bin and near-range sensor limitations were also examined. The results showed that an estimation of feed weight from 3-dimensional scan of volume measurements could be made to within 0.5 kg of the physically measured feed weight using a digital scale. Future efforts will focus on extending this work to active bunks with multiple cows eating throughout the day and testing total mixed rations of varied composition.
authors.affiliation: Department of Electrical Engineering, University of Kentucky, Lexington 40546.
authors.forename: A N
authors.initials: AN
authors.lastname: Shelley
authors.affiliation: Department of Electrical Engineering, University of Kentucky, Lexington 40546.
authors.forename: D L
authors.initials: DL
authors.lastname: Lau
authors.affiliation: Department of Animal and Food Sciences, University of Kentucky, Lexington 40546.
authors.forename: A E
authors.initials: AE
authors.lastname: Stone
authors.affiliation: Department of Animal and Food Sciences, University of Kentucky, Lexington 40546. Electronic address: jbewley@uky.edu.
authors.forename: J M
authors.initials: JM
authors.lastname: Bewley
mesh_terms: D000821:Animal Feed / Q000032:analysis*; D000818:Animals; D001331:Automation; D001835:Body Weight; D002417:Cattle / Q000502:physiology*; D003612:Dairying / Q000379:methods*; D005247:Feeding Behavior; D005260:Female
keywords: feed intake; machine vision; precision dairy farming; structured light illumination