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AgriEngineering
Special Issues
Innovative Technology in Livestock Production
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Innovative Technology in Livestock Production

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

Deadline for manuscript submissions: closed (1 July 2022) | Viewed by 16561

Special Issue Editors

Prof. Irenilza de Alencar Nääs

E-Mail Website
Guest Editor
Agricultural Engineering College, State University of Campinas, Campinas 13083-970, Brazil
Interests: precision animal production and monitoring; modeling in livestock production; the use of artificial intelligence in livestock production and animal monitoring; engineering applied to animal production
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Danilo Florentino Pereira

E-Mail Website
Guest Editor
Department of Management, Development and Technology, School of Science and Engineering, São Paulo State University (UNESP), Av. Domingos da Costa Lopes, 780, Tupã 17602-496, São Paulo, Brazil
Interests: poultry farming; heat stress; animal welfare assessment; image processing; computer vision; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent livestock production technology has guaranteed that large datasets can be retrieved from the individual or grouped animals' movement, behavioral interactions, vocalizations, and physiological responses. Such innovations lead to different ways of interpreting data to present solutions for real-time recording and decision-making. The present Special Issue's aim is to encourage progress towards using new technologies in farm animal production. The main objectives are the application of new technologies in livestock production; the use of automation in animal management and monitoring; the use of simulation, optimization, and modeling; decision support systems applied to livestock farming; computer vision and image processing; precision animal production; the use of the internet of things (IoT) and cloud computing in animal monitoring; development and applications of artificial intelligence (AI) in livestock management; and the use of machine learning in animal production and management. We encourage the use of new concepts and ideas to drive engineering solutions to optimize ongoing and future farm livestock operations.

Prof. Irenilza de Alencar Nääs
Prof. Danilo Florentino Pereira
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

  • smart sensors
  • IT technologies
  • simulation, optimization, and modeling
  • decision support systems
  • image analysis and processing
  • smart animal production
  • artificial intelligence

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Published Papers (4 papers)

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Research

11 pages, 1588 KiB  
Article
A Mobile Application to Follow Up the Management of Broiler Flocks
by Angel Antonio Gonzalez Martinez, Irenilza de Alencar Nääs, Jair Minoro Abe and Danilo Florentino Pereira
AgriEngineering 2021, 3(4), 990-1000; https://doi.org/10.3390/agriengineering3040062 - 17 Dec 2021
Cited by 1 | Viewed by 3093
Abstract
Broiler meat is one of the most consumed meats worldwide. The broiler production system poses several challenges for the producer, including maintaining environmental conditions for rearing. The popularization of mobile devices (smartphones) among people, including those with lower incomes, makes it possible for [...] Read more.
Broiler meat is one of the most consumed meats worldwide. The broiler production system poses several challenges for the producer, including maintaining environmental conditions for rearing. The popularization of mobile devices (smartphones) among people, including those with lower incomes, makes it possible for specialist systems to be developed and used for diverse purposes through Apps (mobile application). The present study proposed the development of a mobile application to help farmers follow up on-farm flock management. We retrieved rearing environment and flock data from commercial broiler farms that complied with broiler-producing standards and followed the breeders’ recommendations. Data were organized and normalized to serve as the basis for the software. We specified a performance index based on the average environment and flock-based data. The language used for the application development was Python compatible with the GNU GPL (General Public License), which has a vast library of ready-made functions. For the graphical interface, we selected Kivy and KivyMD framework. The developed mobile application might help farmers evaluate broiler rearing conditions on-farm during the flock’s growth and grade the flock using a performance index. Full article
(This article belongs to the Special Issue Innovative Technology in Livestock Production)
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15 pages, 2965 KiB  
Article
Modeling and Assessing Heat Transfer of Piglet Microclimates
by Benjamin C. Smith, Brett C. Ramirez and Steven J. Hoff
AgriEngineering 2021, 3(4), 768-782; https://doi.org/10.3390/agriengineering3040048 - 8 Oct 2021
Cited by 4 | Viewed by 4215
Abstract
High piglet pre-weaning mortality rates can be attributed to poor creep area microclimate resulting in negative productivity, welfare, and economic consequences. A piglet mechanistic steady-state thermal balance model was developed using previous models and expanded to assess (a) thermal interactions of multiple pigs [...] Read more.
High piglet pre-weaning mortality rates can be attributed to poor creep area microclimate resulting in negative productivity, welfare, and economic consequences. A piglet mechanistic steady-state thermal balance model was developed using previous models and expanded to assess (a) thermal interactions of multiple pigs and (b) conduction heat transfer. The piglet Effective Environment Temperature (EET) equation was also modified to incorporate piglet age (day 0 to 30) and a conduction heat transfer term. Model parameters were validated with empirical data consisting of the thermal component (dry-bulb temperature, Tdb; mean radiant temperature, TMR; airspeed, U; mat underside temperature, Tm) of the microclimate and dimension data of the piglets (i.e., body weight, length, height, width, and calculated surface area). Model results demonstrate that the common microclimate supplemental heat sources (heat mats and heat lamps; HL) can meet the needs of the piglets. The new EET was more consistent for a novel semi-enclosed heated microclimate (SEHM) in comparison to the HL. This demonstrates the benefit of precision technologies over manually adjusted supplemental heat sources. The experimental data and model results suggest further development of an ideal thermal index for piglet microclimates needs to account for variations of piglet health and body condition to be more applicable in industry. Full article
(This article belongs to the Special Issue Innovative Technology in Livestock Production)
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11 pages, 686 KiB  
Article
The Sequential Behavior Pattern Analysis of Broiler Chickens Exposed to Heat Stress
by Tatiane Branco, Daniella Jorge de Moura, Irenilza de Alencar Nääs, Nilsa Duarte da Silva Lima, Daniela Regina Klein and Stanley Robson de Medeiros Oliveira
AgriEngineering 2021, 3(3), 447-457; https://doi.org/10.3390/agriengineering3030030 - 25 Jun 2021
Cited by 11 | Viewed by 3733
Abstract
Broiler productivity is dependent on a range of variables; among them, the rearing environment is a significant factor for proper well-being and productivity. Behavior indicates the bird’s initial response to an adverse environment and is capable of providing an indicator of well-being in [...] Read more.
Broiler productivity is dependent on a range of variables; among them, the rearing environment is a significant factor for proper well-being and productivity. Behavior indicates the bird’s initial response to an adverse environment and is capable of providing an indicator of well-being in real-time. The present study aims to identify and characterize the sequential pattern of broilers’ behavior when exposed to thermoneutral conditions (TNZ) and thermal stress (HS) by constant heat. The research was carried out in a climatic chamber with 18 broilers under thermoneutral conditions and heat stress for three consecutive days (at three different ages). The behavior database was first analyzed using one-way ANOVA, Tukey test by age, and Boxplot graphs, and then the sequence of the behaviors was evaluated using the generalized sequential pattern (GSP) algorithm. We were able to predict behavioral patterns at the different temperatures assessed from the behavioral sequences. Birds in HS were prostrate, identified by the shorter behavioral sequence, such as the {Lying down, Eating} pattern, unlike TNZ ({Lying down, Walking, Drinking, Walking, Lying down}), which indicates a tendency to increase behaviors (feeding and locomotor activities) that guarantee the better welfare of the birds. The sequence of behaviors ‘Lying down’ followed by ‘Lying laterally’ occurred only in HS, which represents a stressful thermal environment for the bird. Using the pattern mining sequences approach, we were able to identify temporal relationships between thermal stress and broiler behavior, confirming the need for further studies on the use of temporal behavior sequences in environmental controllers. Full article
(This article belongs to the Special Issue Innovative Technology in Livestock Production)
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9 pages, 1462 KiB  
Article
Movement Analysis to Associate Broiler Walking Ability with Gait Scoring
by Danilo Florentino Pereira, Irenilza de Alencar Nääs and Nilsa Duarte da Silva Lima
AgriEngineering 2021, 3(2), 394-402; https://doi.org/10.3390/agriengineering3020026 - 11 Jun 2021
Cited by 5 | Viewed by 4045
Abstract
The genetic development of the commercial broiler has led to body misconfiguration and consequent walking disabilities, mainly at the slaughter age. The present study aimed to identify broiler locomotion ability using image analysis automatically. A total of 40 broilers that were 40 d [...] Read more.
The genetic development of the commercial broiler has led to body misconfiguration and consequent walking disabilities, mainly at the slaughter age. The present study aimed to identify broiler locomotion ability using image analysis automatically. A total of 40 broilers that were 40 d old (male and female) were placed to walk on a specially built runway, and their locomotion was recorded. An image segmentation algorithm was developed, and the coordinates of the bird’s center of mass were extracted from the segmented images for each frame analyzed, and the unrest index (UI) was applied. We calculated the center of mass’s movement of the broiler walking lateral images capturing the bird’s displacement speed in the onward direction. Results indicated that broiler walking speed on the runway tends to decrease with the increase of the gait score. The locomotion did not differ between males or females. The proposed algorithm was efficient in predicting the broiler gait score based on their displacement speed. Full article
(This article belongs to the Special Issue Innovative Technology in Livestock Production)
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