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AgriEngineering
Special Issues
Recent Advancement in Technology and Management Systems in Precision Livestock...
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Recent Advancement in Technology and Management Systems in Precision Livestock Farming

A special issue of AgriEngineering (ISSN 2624-7402).

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 7558

Special Issue Editors

Dr. Abozar Nasirahmadi

E-Mail Website
Guest Editor
Head of Machine Learning and Smart Systems Group, Department of Agricultural and Biosystems Engineering, University of Kassel, Nordbahnhofstrasse 1a, D-37213 Witzenhausen, Germany
Interests: artificial intelligence; machine vision; signal processing; precision livestock farming; biosystems engineering
Special Issues, Collections and Topics in MDPI journals
apl. Prof. Dr. Eva Gallmann

E-Mail Website
Guest Editor
Livestock System Engineering, Institute of Agricultural Engineering, University of Hohenheim, Garbenstraße 9, D-70599 Stuttgart, Germany (Deputy professorship)
Interests: smart livestock farming; animal welfare; emissions and immissions, climate control, livestock buildings

Special Issue Information

Dear Colleagues,

In the next decades, the global demand for livestock products is expected to further increase due to population growth. The need for improved production monitoring has gained significant attention due to advancement of knowledge and technology, along with human expectations for adequate and high-quality livestock products. The behaviour of livestock can provide information about their barn environmental situation, food and water adequacy, health, welfare, and production efficiency. For many years, human observations of animals have been carried out to assess their behaviour, health, and welfare. In recent years, precision livestock farming (PLF), as a management tool of livestock production based on continuous monitoring and control of production, animal health, and the environmental impact of livestock production, has been developed to supply information to farmers and researchers. The latest advancements in artificial intelligence and sensor technologies makes it possible to utilize smart sensors with the potential to replace/improve human investigation. Therefore, the PLF research goals have been shifted toward multidisciplinary approach domains for the development of sensor-based monitoring technology, smart climate control scenarios, pattern recognition and classification models, process prediction, optimizing, and data fusion algorithms.

This Special Issue focuses on the latest finding in PLF research, engineering, and management solutions in all fields of livestock farming. The intention of this Special Issue is to focus on the most recent advances in the research areas that include, but are not limited to, the following:

  • Animal health
  • Animal welfare
  • Animal behaviour
  • Livestock sensors
  • Machine-vision
  • Signal processing
  • Classification
  • Data mining and fusion
  • CFD modelling
  • Climate control
  • Emission control
  • Machine learning
  • Robotics in livestock farming
  • PLF management systems
  • Wireless communication
  • Internet of Things (IoT)

Dr. Abozar Nasirahmadi
apl. Prof. Dr. Eva Gallmann
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. AgriEngineering 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 1600 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

  • Animal health and welfare
  • Artificial intelligence
  • Livestock farming
  • Livestock management system
  • Sensor-based technology

Published Papers (2 papers)

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Research

15 pages, 4748 KiB  
Article
Investigation of Pig Activity Based on Video Data and Semi-Supervised Neural Networks
by Martin Wutke, Armin Otto Schmitt, Imke Traulsen and Mehmet Gültas
AgriEngineering 2020, 2(4), 581-595; https://doi.org/10.3390/agriengineering2040039 - 4 Dec 2020
Cited by 13 | Viewed by 3536
Abstract
The activity level of pigs is an important stress indicator which can be associated to tail-biting, a major issue for animal welfare of domestic pigs in conventional housing systems. Although the consideration of the animal activity could be essential to detect tail-biting before [...] Read more.
The activity level of pigs is an important stress indicator which can be associated to tail-biting, a major issue for animal welfare of domestic pigs in conventional housing systems. Although the consideration of the animal activity could be essential to detect tail-biting before an outbreak occurs, it is often manually assessed and therefore labor intense, cost intensive and impracticable on a commercial scale. Recent advances of semi- and unsupervised convolutional neural networks (CNNs) have made them to the state of art technology for detecting anomalous behavior patterns in a variety of complex scene environments. In this study we apply such a CNN for anomaly detection to identify varying levels of activity in a multi-pen problem setup. By applying a two-stage approach we first trained the CNN to detect anomalies in the form of extreme activity behavior. Second, we trained a classifier to categorize the detected anomaly scores by learning the potential activity range of each pen. We evaluated our framework by analyzing 82 manually rated videos and achieved a success rate of 91%. Furthermore, we compared our model with a motion history image (MHI) approach and a binary image approach using two benchmark data sets, i.e., the well established pedestrian data sets published by the University of California, San Diego (UCSD) and our pig data set. The results show the effectiveness of our framework, which can be applied without the need of a labor intense manual annotation process and can be utilized for the assessment of the pig activity in a variety of applications like early warning systems to detect changes in the state of health. Full article
(This article belongs to the Special Issue Recent Advancement in Technology and Management Systems in Precision Livestock Farming)
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16 pages, 7801 KiB  
Article
Estimation of Productivity in Dryland Mediterranean Pastures: Long-Term Field Tests to Calibration and Validation of the Grassmaster II Probe
by João Serrano, Shakib Shahidian, Francisco Moral, Fernando Carvajal-Ramirez and José Marques da Silva
AgriEngineering 2020, 2(2), 240-255; https://doi.org/10.3390/agriengineering2020015 - 25 Apr 2020
Cited by 3 | Viewed by 3373
Abstract
The estimation of pasture productivity is of great interest for the management of animal grazing. The standard method of assessing pasture mass requires great effort and expense to collect enough samples to accurately represent a pasture. This work presents the results of a [...] Read more.
The estimation of pasture productivity is of great interest for the management of animal grazing. The standard method of assessing pasture mass requires great effort and expense to collect enough samples to accurately represent a pasture. This work presents the results of a long-term study to calibrate a Grassmaster II capacitance probe to estimate pasture productivity in two phases: (i) the calibration phase (2007–2018), which included measurements in 1411 sampling points in three parcels; and (ii) the validation phase (2019), which included measurements in 216 sampling points in eight parcels. A regression analysis was performed between the capacitance (CMR) measured by the probe and values of pasture green matter and dry matter (respectively, GM and DM, in kg ha−1). The results showed significant correlations between GM and CMR and between DM and CMR, especially in the early stages of pasture growth cycle. The analysis of the data grouped by classes of pasture moisture content (PMC) shows higher correlation coefficients for PMC content >80% (r = 0.775; p < 0.01; RMSE = 4806 kg ha−1 and CVRMSE = 28.1% for GM; r = 0.750; p < 0.01; RMSE = 763 kg ha−1 and CVRMSE = 29.7% for DM), with a clear tendency for the accuracy to decrease when the pasture vegetative cycle advances and, consequently, the PMC decreases. The validation of calibration equations when PMC > 80% showed a good approximation between GM or DM measured and GM or DM predicted (r = 0.959; p < 0.01; RMSE = 3191 kg ha−1; CVRMSE = 23.6% for GM; r = 0.953; p <0.01; RMSE = 647 kg ha−1 and CVRMSE = 27.3% for DM). It can be concluded that (i) the capacitance probe is an expedient tool that can enable the farm manager to estimate pasture productivity with acceptable accuracy and support the decision-making process in the management of dryland pastures; (ii) the more favorable period for the use of this probe in dryland pastures in a Mediterranean climate, such as the Portuguese Alentejo, coincides with the end of winter and beginning of spring (February–March), corresponding to PMC > 80%. Full article
(This article belongs to the Special Issue Recent Advancement in Technology and Management Systems in Precision Livestock Farming)
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