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Sensors for Animal Health Monitoring and Precision Livestock Farming—2nd Edition
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Sensors for Animal Health Monitoring and Precision Livestock Farming—2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 3443

Special Issue Editors

Dr. Christos Tachtatzis

E-Mail Website
Guest Editor
Department of Electronic and Electrical Engineering, University of Strathclyde, 16 Richmond Street, Glasgow G1 1XQ, UK
Interests: machine learning; partial discharge monitoring; wireless technologies; data analytics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Craig Michie

E-Mail Website
Guest Editor
Department of Electronic & Electrical Engineering, University of Strathclyde, 204 George Street, Glasgow G1 1XW, UK
Interests: wireless sensor networks; Internet-of-Things; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ivan Andonovic

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, University of Strathclyde, Glasgow G1 1XW, UK
Interests: artificial intelligence; internet of things
Special Issues, Collections and Topics in MDPI journals
Dr. Holly Ferguson

E-Mail Website
Guest Editor
Dairy Research and Innovation Centre, Scotland's Rural College (SRUC), Barony Campus, Dumfries DG1 3NE, UK
Interests: dairying; animal-mounted sensors; sensor systems; animal health, welfare and production
Prof. Dr. Carol-Anne Duthie

E-Mail Website
Guest Editor
Beef and Sheep Research Centre, Future Farming Systems, Scotland's Rural College (SRUC), Roslin Institute Building, Easter Bush, Midlothian EH25 9RG, UK
Interests: precision livestock farming

Special Issue Information

Dear Colleagues,

The global population is predicted to increase to 9 billion by 2050. Combined with climate change and pressure on available land, this is placing increasing demands on the agricultural sector to produce high-quality foods efficiently to feed the growing population with minimal environmental footprint. In the dairy sector, the average farm size has grown as farms have combined and consolidated to reap the benefit of scale. While this can be beneficial, increases in farm size mean that farmers have less time to undertake essential routine tasks traditionally carried out by visually observing their herds. Technology has played a key supporting role in this context, enabling farmers to monitor cattle 24 hours per day to monitor herd fertility, health, positive welfare and environmental impact.

Owing to the success of the first volume, this second volume concentrates on non-invasive, non-contact sensing solutions such as video, audio, radar, photonics, etc. This Special Issue will contribute to the state of the art and present precision farming solutions and applications enabled by monitoring technologies within animal welfare and environmental monitoring. The Guest Editors invite papers related to the following topics:

  • Localisation and tracking of animals;
  • Technologies that aid the quantification of greenhouse gases;
  • Machine learning and data processing in agriculture;
  • Image processing and computer vision;
  • Detection of animal behaviour;
  • Measurement of parameters that relate to animal welfare;
  • Technologies that optimise production;
  • Algorithms and data analytics that relate to any of the above.

Dr. Christos Tachtatzis
Prof. Dr. Craig Michie
Prof. Dr. Ivan Andonovic
Dr. Holly Ferguson
Prof. Dr. Carol-Anne Duthie
Guest Editors

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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • computer vision
  • radar
  • audio
  • photonics
  • animal welfare
  • activity monitoring
  • greenhouse gases
  • production efficiency
  • feed additives
  • artificial intelligence
  • machine learning
  • data analytics

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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

17 pages, 4465 KiB  
Article
A Complete Pipeline to Extract Temperature from Thermal Images of Pigs
by Rodania Bekhit and Inonge Reimert
Sensors 2025, 25(3), 643; https://doi.org/10.3390/s25030643 - 22 Jan 2025
Viewed by 562
Abstract
Using deep learning or artificial intelligence (AI) in research with animals is a new interdisciplinary area of research. In this study, we have explored the potential of thermal imaging and AI in pig research. Thermal cameras play a vital role in obtaining and [...] Read more.
Using deep learning or artificial intelligence (AI) in research with animals is a new interdisciplinary area of research. In this study, we have explored the potential of thermal imaging and AI in pig research. Thermal cameras play a vital role in obtaining and collecting a large amount of data, and AI has the capabilities of processing and extracting valuable information from these data. The amount of data collected using thermal imaging is huge, and automation techniques are therefore crucial to find a meaningful interpretation of the changes in temperature. In this paper, we present a complete pipeline to extract temperature automatically from a selected Region of Interest (ROI). This system consists of three stages: the first one checks whether the ROI is completely visible to observe the thermal temperature, and then the second stage uses an encoder–decoder structure of a convolution neural network to segment the ROI, if the condition was met at stage one. In the last stage, the maximum temperature is extracted and saved in an external file. The segmentation model showed good performance, with a mean Pixel Class accuracy of 92.3%, and a mean Intersection over Union of 87.1%. The extracted temperature observed by the model entirely matched the manually observed temperature. The system showed reliable results to be used independently without human intervention to determine the temperature in the selected ROI in pigs. Full article
(This article belongs to the Special Issue Sensors for Animal Health Monitoring and Precision Livestock Farming—2nd Edition)
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19 pages, 476 KiB  
Article
Evaluation of Different Sensor Systems for Classifying the Behavior of Dairy Cows on Pasture
by Barbara Pichlbauer, Jose Maria Chapa Gonzalez, Martin Bobal, Christian Guse, Michael Iwersen and Marc Drillich
Sensors 2024, 24(23), 7739; https://doi.org/10.3390/s24237739 - 3 Dec 2024
Viewed by 718
Abstract
Monitoring animal behavior using sensor technologies requires prior testing under varying conditions because behaviors can differ significantly, such as between grazing and confined cows. This study aimed to validate several sensor systems for classifying rumination and lying behaviors in cows on pasture under [...] Read more.
Monitoring animal behavior using sensor technologies requires prior testing under varying conditions because behaviors can differ significantly, such as between grazing and confined cows. This study aimed to validate several sensor systems for classifying rumination and lying behaviors in cows on pasture under different environmental conditions, compare the sensors’ performance at different time resolutions, and evaluate a correction algorithm for rumination data. Ten Simmental dairy cows were monitored on pasture, each simultaneously equipped with an ear-tag accelerometer (ET), two different leg-mounted accelerometers (LMs), and a noseband sensor (NB). Indirect visual observations using drone-recorded video footage served as the gold standard for validation. The concordance correlation coefficient (CCC) for rumination time was very high for both the ET and NB (0.91–0.96) at a 10 min time resolution. Applying the correction algorithm to 1 min data improved the CCC for the NB from 0.68 to 0.89. For lying time, the CCC was moderate for the ET (0.55) but nearly perfect for both LMs (0.99). In conclusion, both sensors evaluated for classifying rumination are suitable for cows on pasture. We recommend using a correction algorithm for 1 min NB data. For the measurement of lying time, the LMs significantly outperformed the ET. Full article
(This article belongs to the Special Issue Sensors for Animal Health Monitoring and Precision Livestock Farming—2nd Edition)
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30 pages, 23098 KiB  
Article
A Dataset of Visible Light and Thermal Infrared Images for Health Monitoring of Caged Laying Hens in Large-Scale Farming
by Weihong Ma, Xingmeng Wang, Xianglong Xue, Mingyu Li, Simon X. Yang, Yuhang Guo, Ronghua Gao, Lepeng Song and Qifeng Li
Sensors 2024, 24(19), 6385; https://doi.org/10.3390/s24196385 - 2 Oct 2024
Cited by 1 | Viewed by 1534
Abstract
Considering animal welfare, the free-range laying hen farming model is increasingly gaining attention. However, in some countries, large-scale farming still relies on the cage-rearing model, making the focus on the welfare of caged laying hens equally important. To evaluate the health status of [...] Read more.
Considering animal welfare, the free-range laying hen farming model is increasingly gaining attention. However, in some countries, large-scale farming still relies on the cage-rearing model, making the focus on the welfare of caged laying hens equally important. To evaluate the health status of caged laying hens, a dataset comprising visible light and thermal infrared images was established for analyses, including morphological, thermographic, comb, and behavioral assessments, enabling a comprehensive evaluation of the hens’ health, behavior, and population counts. To address the issue of insufficient data samples in the health detection process for individual and group hens, a dataset named BClayinghens was constructed containing 61,133 images of visible light and thermal infrared images. The BClayinghens dataset was completed using three types of devices: smartphones, visible light cameras, and infrared thermal cameras. All thermal infrared images correspond to visible light images and have achieved positional alignment through coordinate correction. Additionally, the visible light images were annotated with chicken head labels, obtaining 63,693 chicken head labels, which can be directly used for training deep learning models for chicken head object detection and combined with corresponding thermal infrared data to analyze the temperature of the chicken heads. To enable the constructed deep-learning object detection and recognition models to adapt to different breeding environments, various data enhancement methods such as rotation, shearing, color enhancement, and noise addition were used for image processing. The BClayinghens dataset is important for applying visible light images and corresponding thermal infrared images in the health detection, behavioral analysis, and counting of caged laying hens under large-scale farming. Full article
(This article belongs to the Special Issue Sensors for Animal Health Monitoring and Precision Livestock Farming—2nd Edition)
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