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Agriculture
Special Issues
Modeling of Livestock Breeding Environment and Animal Behavior
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Modeling of Livestock Breeding Environment and Animal Behavior

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1981

Special Issue Editor

Dr. Longshen Liu

E-Mail Website
Guest Editor
College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Interests: precision livestock farming; animal behavior detection; animal disease warning; farming robots; deep neural network; algorithms

Special Issue Information

Dear Colleagues,

With the rapid development of precision livestock farming and continuous advancements in technology, livestock production faces an ongoing need for optimization, particularly in intelligent management and environmental modeling. From a technical standpoint, this involves the development of animal behavior detection systems, disease warning systems, and the application of farming robots and deep neural network algorithms. These technological solutions significantly enhance production efficiency, optimize resource allocation, and reduce costs. However, the introduction of new technologies does not always directly improve environmental conditions or immediately enhance animal welfare. Therefore, accurately assessing environmental stressors through intelligent systems is crucial to ensure that animals live in optimal conditions. By leveraging smart solutions, we can not only maximize production efficiency, but also ensure that animals enjoy healthy and comfortable living environments.

This Special Issue will feature interdisciplinary research from the disciplines of animal science, veterinary science, and agricultural engineering.

Any type of article, including original research, opinion pieces, and reviews, is welcome.

Dr. Longshen Liu
Guest Editor

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 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 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

  • precision livestock farming
  • animal behavior detection
  • animal disease warning
  • farming robots
  • deep neural network
  • algorithms

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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26 pages, 10260 KiB  
Article
Only Detect Broilers Once (ODBO): A Method for Monitoring and Tracking Individual Behavior of Cage-Free Broilers
by Chengcheng Yin, Xinjie Tan, Xiaoxin Li, Mingrui Cai and Weihao Chen
Agriculture 2025, 15(7), 669; https://doi.org/10.3390/agriculture15070669 - 21 Mar 2025
Viewed by 319
Abstract
In commercial poultry farming, automated behavioral monitoring systems hold significant potential for optimizing production efficiency and improving welfare outcomes at scale. The behavioral detection of free-range broilers matters for precision farming and animal welfare. Current research often focuses on either behavior detection or [...] Read more.
In commercial poultry farming, automated behavioral monitoring systems hold significant potential for optimizing production efficiency and improving welfare outcomes at scale. The behavioral detection of free-range broilers matters for precision farming and animal welfare. Current research often focuses on either behavior detection or individual tracking, with few studies exploring their connection. To continuously track broiler behaviors, the Only Detect Broilers Once (ODBO) method is proposed by linking behaviors with identity information. This method has a behavior detector, an individual Tracker, and a Connector. First, by integrating SimAM, WIOU, and DIOU-NMS into YOLOv8m, the high-performance YOLOv8-BeCS detector is created. It boosts P by 6.3% and AP by 3.4% compared to the original detector. Second, the designed Connector, based on the tracking-by-detection structure, transforms the tracking task, combining broiler tracking and behavior recognition. Tests on sort-series trackers show HOTA, MOTA, and IDF1 increase by 27.66%, 28%, and 27.96%, respectively, after adding the Connector. Fine-tuning experiments verify the model’s generalization. The results show this method outperforms others in accuracy, generalization, and convergence speed, providing an effective method for monitoring individual broiler behaviors. In addition, the system’s ability to simultaneously monitor individual bird welfare indicators and group dynamics could enable data-driven decisions in commercial poultry farming management. Full article
(This article belongs to the Special Issue Modeling of Livestock Breeding Environment and Animal Behavior)
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21 pages, 16141 KiB  
Article
The Development of a Sorting System Based on Point Cloud Weight Estimation for Fattening Pigs
by Luo Liu, Yangsen Ou, Zhenan Zhao, Mingxia Shen, Ruqian Zhao and Longshen Liu
Agriculture 2025, 15(4), 365; https://doi.org/10.3390/agriculture15040365 - 8 Feb 2025
Cited by 1 | Viewed by 652
Abstract
As large-scale and intensive fattening pig farming has become mainstream, the increase in farm size has led to more severe issues related to the hierarchy within pig groups. Due to genetic differences among individual fattening pigs, those that grow faster enjoy a higher [...] Read more.
As large-scale and intensive fattening pig farming has become mainstream, the increase in farm size has led to more severe issues related to the hierarchy within pig groups. Due to genetic differences among individual fattening pigs, those that grow faster enjoy a higher social rank. Larger pigs with greater aggression continuously acquire more resources, further restricting the survival space of weaker pigs. Therefore, fattening pigs must be grouped rationally, and the management of weaker pigs must be enhanced. This study, considering current fattening pig farming needs and actual production environments, designed and implemented an intelligent sorting system based on weight estimation. The main hardware structure of the partitioning equipment includes a collection channel, partitioning channel, and gantry-style collection equipment. Experimental data were collected, and the original scene point cloud was preprocessed to extract the back point cloud of fattening pigs. Based on the morphological characteristics of the fattening pigs, the back point cloud segmentation method was used to automatically extract key features such as hip width, hip height, shoulder width, shoulder height, and body length. The segmentation algorithm first calculates the centroid of the point cloud and the eigenvectors of the covariance matrix to reconstruct the point cloud coordinate system. Then, based on the variation characteristics and geometric shape of the consecutive horizontal slices of the point cloud, hip width and shoulder width slices are extracted, and the related features are calculated. Weight estimation was performed using Random Forest, Multilayer perceptron (MLP), linear regression based on the least squares method, and ridge regression models, with parameter tuning using Bayesian optimization. The mean squared error, mean absolute error, and mean relative error were used as evaluation metrics to assess the model’s performance. Finally, the classification capability was evaluated using the median and average weights of the fattening pigs as partitioning standards. The experimental results show that the system’s average relative error in weight estimation is approximately 2.90%, and the total time for the partitioning process is less than 15 s, which meets the needs of practical production. Full article
(This article belongs to the Special Issue Modeling of Livestock Breeding Environment and Animal Behavior)
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21 pages, 7811 KiB  
Article
Research on Broiler Mortality Identification Methods Based on Video and Broiler Historical Movement
by Hongyun Hao, Fanglei Zou, Enze Duan, Xijie Lei, Liangju Wang and Hongying Wang
Agriculture 2025, 15(3), 225; https://doi.org/10.3390/agriculture15030225 - 21 Jan 2025
Viewed by 620
Abstract
The presence of dead broilers within a flock can be significant vectors for disease transmission and negatively impact the overall welfare of the remaining broilers. This study introduced a dead broiler detection method that leverages the fact that dead broilers remain stationary within [...] Read more.
The presence of dead broilers within a flock can be significant vectors for disease transmission and negatively impact the overall welfare of the remaining broilers. This study introduced a dead broiler detection method that leverages the fact that dead broilers remain stationary within the flock in videos. Dead broilers were identified through the analysis of the historical movement information of each broiler in the video. Firstly, the frame difference method was utilized to capture key frames in the video. An enhanced segmentation network, YOLOv8-SP, was then developed to obtain the mask coordinates of each broiler, and an optical flow estimation method was employed to generate optical flow maps and evaluate their movement. An average optical flow intensity (AOFI) index of broilers was defined and calculated to evaluate the motion level of each broiler in each key frame. With the AOFI threshold, broilers in the key frames were classified into candidate dead broilers and active live broilers. Ultimately, the identification of dead broilers was achieved by analyzing the frequency of each broiler being judged as a candidate death in all key frames within the video. We incorporated the parallelized patch-aware attention (PPA) module into the backbone network and improved the overlaps function with the custom power transform (PT) function. The box and mask segmentation mAP of the YOLOv8-SP model increased by 1.9% and 1.8%, respectively. The model’s target recognition performance for small targets and partially occluded targets was effectively improved. False and missed detections of dead broilers occurred in 4 of the 30 broiler testing videos, and the accuracy of the dead broiler identification algorithm proposed in this study was 86.7%. Full article
(This article belongs to the Special Issue Modeling of Livestock Breeding Environment and Animal Behavior)
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Review

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18 pages, 1442 KiB  
Review
Smart Pig Farms: Integration and Application of Digital Technologies in Pig Production
by Katarina Marić, Kristina Gvozdanović, Ivona Djurkin Kušec, Goran Kušec and Vladimir Margeta
Agriculture 2025, 15(9), 937; https://doi.org/10.3390/agriculture15090937 - 25 Apr 2025
Abstract
The prediction that the world population will reach almost 10 billion people by 2050 means an increase in pork production is required. Efforts to meet increased demand have made pig production one of the most technologically advanced branches of production and one which [...] Read more.
The prediction that the world population will reach almost 10 billion people by 2050 means an increase in pork production is required. Efforts to meet increased demand have made pig production one of the most technologically advanced branches of production and one which is growing continuously. Precision Livestock Production (PLF) is an increasingly widespread model in pig farming and describes a management system based on the continuous automatic monitoring and control of production, reproduction, animal health and welfare in real time, as well as the impact of animal husbandry on the environment. Today, a wide range of technologies is available, such as 2D and 3D cameras to assess body weight, behavior and activity, thermal imaging cameras to monitor body temperatures and determine estrus, microphones to monitor vocalizations, various measuring cells to monitor food intake, body weight and weight gain, and many others. By combining and applying the available technologies, it is possible to obtain a variety of data that allow livestock farmers to automatically monitor animals and improve pig health and welfare as well as environmental sustainability. Nevertheless, PLF systems need further research to improve the technologies and create cheap and affordable but accurate models to ensure progress in pig production. Full article
(This article belongs to the Special Issue Modeling of Livestock Breeding Environment and Animal Behavior)
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