Automated Monitoring of Livestock and Poultry with Machine Learning Technology
A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Animal System and Management".
Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 41273
Special Issue Editors
Interests: precision livestock farming; animal welfare and behavior; smart sensing; applied artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: precision livestock management; animal housing and environmental control; applied data analysis in agriculture
Special Issues, Collections and Topics in MDPI journals
Interests: livestock environmental engineering; indoor environment and ventilation; precise ventilation of livestock houses; CFD; environmental control system and strategy
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The livestock and poultry industry producing daily animal protein for humans continues to grow for improved genetics, nutrition, stewardship, and welfare to enhance production efficiency, secure food safety, sufficiency, and sustainability, and feed the growing human population. Maintaining and improving contemporary intensive production systems require substantial natural and human resources and can greatly impact economy, public health, environment, and society. Automated monitoring features developments and applications of continuous, objective, and supportive sensing technologies and computer tools for sustainable and efficient animal production. Recent advancements in computer hardware and machine learning modelling boost the performance of automated monitoring and can assist producers in management decisions and provide early detection and prevention of disease and production inefficiencies. Automated monitoring with machine learning technology offers solutions to the animal industry to address challenges with regard to precision/smart management, environment, nutrition, genetics, big data analytics, real-time monitoring, automation and robotics, welfare assessment, animal tracking, individual identification, behavior recognition, etc.
We are pleased to invite original research and review papers from all over the globe. Contributing papers are expected to address advancement towards production efficiency, safety, and sustainability of the animal industry and explore abovementioned areas through developments and applications of automated monitoring with machine learning technology.
Dr. Guoming Li
Dr. Yijie Xiong
Dr. Hao Li
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. Animals 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 2400 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
- precision poultry farming
- machine learning
- deep learning
- IoT
- sensor
- computer vision
- big data
- robotics
- modelling
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