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Applied Sciences
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Smart Poultry Farm
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Smart Poultry Farm

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 9925

Special Issue Editors

Prof. Dr. Jinming Pan

E-Mail Website
Guest Editor
Department of Biosystems Engineering, Zhejiang University, Hangzhou 310029, China
Interests: intelligent sensoring of animal growth and health information; special intelligent LED lighting for animal production
Dr. Kai Liu

E-Mail Website
Guest Editor
Department of Infectious Diseases and Public Health, Jockey club College of Veterinary Medicine and Life Science, City University of Hong Kong, Hong Kong SAR, China
Interests: precision livestock farming; animal housing and environment management; animal behavior and welfare
Special Issues, Collections and Topics in MDPI journals
Dr. Hongjian Lin

E-Mail Website
Guest Editor
Department of Biosystems Engineering, Zhejiang University, Hangzhou 310029, China
Interests: advanced sensor technologies; heat and mass transfer; computer vision for egg quality and disease detection; waste treatment

Special Issue Information

Dear Colleagues,

The poultry industry approaches an era of precision livestock farming (smart poultry farming), where animal husbandry becomes digitalized and data-driven, enabling smart decision making in optimized production management, improved animal health and welfare, and a maximized production efficiency and profitability (e.g., precision feeding and nutrition, early disease detection and warning, and online health and welfare evaluations). There is no doubt that the Smart Poultry Farm will play an important role in addressing global challenges related to food animal production and sustainability.

At present, smart poultry farms mainly rely on various modern technologies such as agri-robots, IoT (e.g., temperature/RH/ammonia sensors with 5G/LoRa/wideband networks), computer vision (e.g., digital cameras, thermography), sound analyses (e.g., microphones), and machine learning/deep learning to continuously monitor and control a series of environmental and biological parameters critical to the behavior, health, production, reproduction, and welfare of animals.

In this Special Issue, we invite contributions exploring the cutting-edge research and recent advances in the field of Smart Poultry Farming, including using tools (e.g., innovative technologies, digitized operations and production systems, autonomous systems, etc.) to assess, evaluate, and control the sustainability of poultry production systems (e.g., laying hens, broilers, ducks, and geese). Both theoretical and experimental studies are welcome, as well as a limited number of comprehensive reviews.

Prof. Dr. Jinming Pan
Prof. Dr. Kai Liu
Dr. Hongjian Lin
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. Applied Sciences 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

  • smart agriculture
  • precision livestock farming
  • digitalization
  • information management system
  • agri-robot
  • poultry
  • layer
  • broiler
  • egg

Published Papers (2 papers)

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Research

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11 pages, 2121 KiB  
Article
Impact of Waste Tea Litter on NH3 and CO2 Emissions during Broiler Rearing
by Dengfei Jie, Zhanxiang Zhang, Jincheng He, Yafang Zhou and Guangyou Zhu
Appl. Sci. 2022, 12(5), 2559; https://doi.org/10.3390/app12052559 - 01 Mar 2022
Cited by 2 | Viewed by 1620
Abstract
Pollution generated by livestock and poultry rearing is an important environmental issue, and gas emissions during animal production are continuously increasing. A digital rearing chamber inspection system was designed in the present study in order to examine the waste tea litter’s impact on [...] Read more.
Pollution generated by livestock and poultry rearing is an important environmental issue, and gas emissions during animal production are continuously increasing. A digital rearing chamber inspection system was designed in the present study in order to examine the waste tea litter’s impact on the growth performance and harmful gas emissions, such as ammonia (NH3) and carbon dioxide (CO2), during broiler rearing. Broilers were raised without litter and with waste tea litter. According to the results, broiler growth showed little difference between the two groups during the experimental period, and it was concluded that waste tea litter had no impact on broiler growth. Meanwhile, the gas emissions of the waste tea-litter group were lower than the non-bedding-materials group. In detail, the average concentrations of NH3 and CO2 of the non-bedding-materials group were 9.33 ± 3.65 ppm and 797 ± 107 ppm, respectively; while these concentrations in the waste-tea-litter group were 1.01 ± 0.35 ppm and 713 ± 69 ppm, respectively. According to the analysis of the litter properties, it was suggested that waste tea litter can reduce the moisture content in litter, and affect microbial and urease activity due to its low carbon nitrogen ratio (C/N), weak acid, and porous structure characteristics. In conclusion, this study showed the potential of waste tea litter in NH3 and CO2 emission reduction during broiler rearing. Full article
(This article belongs to the Special Issue Smart Poultry Farm)
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Review

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15 pages, 2269 KiB  
Review
Research Progress in the Early Warning of Chicken Diseases by Monitoring Clinical Symptoms
by Pengguang He, Zhonghao Chen, Hongwei Yu, Khawar Hayat, Yefan He, Jinming Pan and Hongjian Lin
Appl. Sci. 2022, 12(11), 5601; https://doi.org/10.3390/app12115601 - 31 May 2022
Cited by 14 | Viewed by 6968
Abstract
Global animal protein consumption has been steadily increasing as a result of population growth and the increasing demand for nutritious diets. The poultry industry provides a large portion of meat and eggs for human consumption. The early detection and warning of poultry infectious [...] Read more.
Global animal protein consumption has been steadily increasing as a result of population growth and the increasing demand for nutritious diets. The poultry industry provides a large portion of meat and eggs for human consumption. The early detection and warning of poultry infectious diseases play a critical role in the poultry breeding and production systems, improving animal welfare and reducing losses. However, inadequate methods for the early detection and prevention of infectious diseases in poultry farms sometimes fail to prevent decreased productivity and even widespread mortality. The health status of poultry is often reflected by its individual physiological, physical and behavioral clinical symptoms, such as higher body temperature resulting from fever, abnormal vocalization caused by respiratory disease and abnormal behaviors due to pathogenic infection. Therefore, the use of technologies for symptom detection can monitor the health status of broilers and laying hens in a continuous, noninvasive and automated way, and potentially assist in the early warning decision-making process. This review summarized recent literature on poultry disease detection and highlighted clinical symptom-monitoring technologies for sick poultry. The review concluded that current technologies are already showing their superiority to manual inspection, but the clinical symptom-based monitoring systems have not been fully utilized for on-farm early detection. Full article
(This article belongs to the Special Issue Smart Poultry Farm)
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