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Agriculture
Special Issues
Application of Sensor Technologies in Livestock Farming
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Application of Sensor Technologies in Livestock Farming

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 700

Special Issue Editors

Dr. Ali Alameer

E-Mail Website
Guest Editor
School of Science, Engineering and Environment, University of Salford Manchester, Salford M5 4WT, UK
Interests: precision livestock farming; machine learning; deep learning; machine/robotic vision; digital signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Taha Mansouri

E-Mail Website
Guest Editor
School of Science, Engineering and Environment, University of Salford Manchester, Salford M5 4WT, UK
Interests: machine learning; deep learning; computer vision; complex systems modelling; explainable AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue explores the role of sensor technologies, e.g., machine vision, in transforming the livestock farming industry. This includes the advancements, challenges, and potential applications of sensor technologies in livestock farming. In addition, this Special Issue focuses on various aspects of sensor technologies, such as data processing and decision-making algorithms, showcasing their effectiveness in livestock management.

The articles will explore how sensor technologies can facilitate the automated monitoring of animal behaviour, disease detection, and identification of individual animals for tracking and sorting purposes. Furthermore, this issue also examines the integration of machine vision with other sensor technologies, such as infrared thermography and RFID, to enhance the overall efficiency and accuracy of data collection.

This Special Issue also addresses challenges associated with implementing sensor technologies in livestock farming, including data management, privacy, and cost-effectiveness. This is to provide insights into potential solutions and emphasise the need for interdisciplinary collaboration to overcome these challenges and fully realise the benefits of sensor technologies in livestock farming.

Dr. Ali Alameer
Dr. Taha Mansouri
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. Agriculture is an international peer-reviewed open access monthly 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

  • sensor technologies
  • livestock farming
  • machine vision
  • animal behaviour
  • disease detection
  • data processing
  • decision-making algorithms
  • automated monitoring
  • deep learning
  • signal processing

Published Papers (1 paper)

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Research

19 pages, 6133 KiB  
Article
A Point Cloud Segmentation Method for Pigs from Complex Point Cloud Environments Based on the Improved PointNet++
by Kaixuan Chang, Weihong Ma, Xingmei Xu, Xiangyu Qi, Xianglong Xue, Zhankang Xu, Mingyu Li, Yuhang Guo, Rui Meng and Qifeng Li
Agriculture 2024, 14(5), 720; https://doi.org/10.3390/agriculture14050720 (registering DOI) - 02 May 2024
Abstract
In animal husbandry applications, segmenting live pigs in complex farming environments faces many challenges, such as when pigs lick railings and defecate within the acquisition environment. The pig’s behavior makes point cloud segmentation more complex because dynamic animal behaviors and environmental changes must [...] Read more.
In animal husbandry applications, segmenting live pigs in complex farming environments faces many challenges, such as when pigs lick railings and defecate within the acquisition environment. The pig’s behavior makes point cloud segmentation more complex because dynamic animal behaviors and environmental changes must be considered. This further requires point cloud segmentation algorithms to improve the feature capture capability. In order to tackle the challenges associated with accurately segmenting point cloud data collected in complex real-world scenarios, such as pig occlusion and posture changes, this study utilizes PointNet++. The SoftPool pooling method is employed to implement a PointNet++ model that can achieve accurate point cloud segmentation for live pigs in complex environments. Firstly, the PointNet++ model is modified to make it more suitable for pigs by adjusting its parameters related to feature extraction and sensory fields. Then, the model’s ability to capture the details of point cloud features is further improved by using SoftPool as the point cloud feature pooling method. Finally, registration, filtering, and extraction are used to preprocess the point clouds before integrating them into a dataset for manual annotation. The improved PointNet++ model’s segmentation ability was validated and redefined with the pig point cloud dataset. Through experiments, it was shown that the improved model has better learning ability across 529 pig point cloud data sets. The optimal mean Intersection over Union (mIoU) was recorded at 96.52% and the accuracy at 98.33%. This study has achieved the automatic segmentation of highly overlapping pigs and pen point clouds. This advancement enables future animal husbandry applications, such as estimating body weight and size based on 3D point clouds. Full article
(This article belongs to the Special Issue Application of Sensor Technologies in Livestock Farming)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Siamese Network for Head-to-Rear Contact Detection in Pigs with Limited Examples
Authors: Taha Mansouri
Affiliation: School of Science, Engineering and Environment, University of Salford Manchester, Salford M5 4WT, United Kingdom

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