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Dairy
Special Issues
Advances in Digital Dairy
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Advances in Digital Dairy

A special issue of Dairy (ISSN 2624-862X). This special issue belongs to the section "Dairy Farm System and Management".

Deadline for manuscript submissions: closed (25 December 2022) | Viewed by 11002

Special Issue Editor

Dr. Andrea Pezzuolo

E-Mail Website
Guest Editor
Department of Land, Environment, Agriculture and Forestry, University of Padova, 35020 Legnaro, PD, Italy
Interests: computer vision; neural network; anaerobic digestion; geographic information system; bioenergy; agricultural technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Dairy production will need to increase to meet demand from an increased global population and economic growth. Simultaneously, livestock practices are under increased pressure to decrease their negative social and environmental impacts. Technologies and digital innovations can assist dairy stakeholders with this by improving efficiency, enabling better decisions, and establishing trust. 

This Special Issue aims to publish research articles dedicated to current (and future) technologies and digital innovations in dairy production and processing.

Dr. Andrea Pezzuolo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Dairy is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • precision livestock farming
  • sensors and technologies
  • robotics and automation
  • Internet of Things (IoT)
  • virtual/augmented reality
  • artificial intelligence
  • decision support system (DSS)
  • data analytics
  • supply chain
  • dairy processing

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

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13 pages, 1662 KiB  
Article
Development of Thresholds to Predict Grazing Behaviour of Dairy Cows from Motion Sensor Data and Application in a Pasture-Based Automatic Milking System
by Brendan Cullen, Zelin Li, Saranika Talukder, Long Cheng and Ellen C. Jongman
Dairy 2023, 4(1), 124-136; https://doi.org/10.3390/dairy4010009 - 29 Jan 2023
Cited by 1 | Viewed by 2262
Abstract
The monitoring and measurement of animal behaviour may be valuable for improving animal production and welfare. This study was designed to develop thresholds to predict the grazing, standing, walking, and lying behaviour of dairy cows from motion sensor (IceTag) output. The experiment included [...] Read more.
The monitoring and measurement of animal behaviour may be valuable for improving animal production and welfare. This study was designed to develop thresholds to predict the grazing, standing, walking, and lying behaviour of dairy cows from motion sensor (IceTag) output. The experiment included 29 lactating cows grazed in a pasture-based dairy production system with voluntary cow movement in northern Victoria, Australia. Sensors recorded motion data at 1 min intervals. A total of 5818 min of cow observations were used. Two approaches were developed using (1) the IceTag lying index and steps only and (2) the IceTag lying index, steps, and motion index for each behaviour. Grazing behaviour was best predicted by the second approach, which had a sensitivity of 92% and specificity of 60%. The thresholds were then used to predict cow behaviour during two periods. On average, across both time periods, cows spent 38% of the day grazing, 38% lying, 19% standing, and 5% walking. Predicted individual cow grazing time was positively correlated with both milk production and milking frequency. The thresholds developed were effective at predicting cow behaviours and can be applied to measure behaviour in pasture-based dairy production. Full article
(This article belongs to the Special Issue Advances in Digital Dairy)
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25 pages, 11644 KiB  
Article
Two- and Three-Dimensional Computer Vision Techniques for More Reliable Body Condition Scoring
by Niall O’Mahony, Lenka Krpalkova, Gearoid Sayers, Lea Krump, Joseph Walsh and Daniel Riordan
Dairy 2023, 4(1), 1-25; https://doi.org/10.3390/dairy4010001 - 26 Dec 2022
Cited by 3 | Viewed by 3924
Abstract
This article identifies the essential technologies and considerations for the development of an Automated Cow Monitoring System (ACMS) which uses 3D camera technology for the assessment of Body Condition Score (BCS). We present a comparison of a range of common techniques at the [...] Read more.
This article identifies the essential technologies and considerations for the development of an Automated Cow Monitoring System (ACMS) which uses 3D camera technology for the assessment of Body Condition Score (BCS). We present a comparison of a range of common techniques at the different developmental stages of Computer Vision including data pre-processing and the implementation of Deep Learning for both 2D and 3D data formats commonly captured by 3D cameras. This research focuses on attaining better reliability from one deployment of an ACMS to the next and proposes a Geometric Deep Learning (GDL) approach and evaluating model performance for robustness from one farm to another in the presence of background, farm, herd, camera pose and cow pose variabilities. Full article
(This article belongs to the Special Issue Advances in Digital Dairy)
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Review

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13 pages, 337 KiB  
Review
Mapping Welfare: Location Determining Techniques and Their Potential for Managing Cattle Welfare—A Review
by Gerben Hofstra, Judith Roelofs, Steven Mark Rutter, Elaine van Erp-van der Kooij and Jakob de Vlieg
Dairy 2022, 3(4), 776-788; https://doi.org/10.3390/dairy3040053 - 29 Oct 2022
Cited by 5 | Viewed by 3572
Abstract
Several studies have suggested that precision livestock farming (PLF) is a useful tool for animal welfare management and assessment. Location, posture and movement of an individual are key elements in identifying the animal and recording its behaviour. Currently, multiple technologies are available for [...] Read more.
Several studies have suggested that precision livestock farming (PLF) is a useful tool for animal welfare management and assessment. Location, posture and movement of an individual are key elements in identifying the animal and recording its behaviour. Currently, multiple technologies are available for automated monitoring of the location of individual animals, ranging from Global Navigation Satellite Systems (GNSS) to ultra-wideband (UWB), RFID, wireless sensor networks (WSN) and even computer vision. These techniques and developments all yield potential to manage and assess animal welfare, but also have their constraints, such as range and accuracy. Combining sensors such as accelerometers with any location determining technique into a sensor fusion system can give more detailed information on the individual cow, achieving an even more reliable and accurate indication of animal welfare. We conclude that location systems are a promising approach to determining animal welfare, especially when applied in conjunction with additional sensors, but additional research focused on the use of technology in animal welfare monitoring is needed. Full article
(This article belongs to the Special Issue Advances in Digital Dairy)
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