Applications of Smart Technology as a Sustainable Strategy in Modern Swine Farming

YOLOv8A-SD: A Segmentation-Detection Algorithm for Overlooking Scenes in Pig Farms

A refined YOLOv8A-SD model is introduced to address pig detection challenges in aerial surveillance of pig farms. The model incorporates the ADown attention mechanism and a dual-task strategy combining detection and segmentation tasks. Testing was conducted using top-view footage from a large-scale pig farm in Sichuan, with 924 images for detection training, 216 for validation, and 2985 images for segmentation training, with 1512 for validation. The model achieved 96.1% Precision and 96.3% mAP50 in detection tasks while maintaining strong segmentation performance (IoU: 83.1%). A key finding reveals that training with original images while applying segmentation preprocessing during testing provides optimal results, achieving exceptional counting accuracy (25.05 vs. actual 25.09 pigs) and simplifying practical deployment. The research demonstrates YOLOv8A-SD's effectiveness in complex farming environments, providing reliable monitoring capabilities for intelligent farm management applications.

A review of sound-based pig monitoring for enhanced precision production

Pig farming is experiencing significant transformations, driven by technological advancements, which have greatly improved management practices and overall productivity. Sound-based technologies are emerging as a valuable tool in enhancing precision pig farming. This review explores the advancements in sound-based technologies and their role in improving precision pig farming through enhanced monitoring of health, behavior, and environmental conditions. When strategically placed on farms, non-invasive technologies such as microphones and sound sensors can continuously collect data without disturbing the animals, making them highly efficient. Farmers using sound data, can monitor key factors such as respiratory conditions, stress levels, and social behaviors, leading to improved animal welfare and optimized production. Advancements in sensor technology and data analytics have enhanced the capabilities of sound-based precision systems in pig farming. The integration of machine learning and artificial intelligence (AI) is further enhancing the capacity to interpret complex sound patterns, enabling the automated detection of abnormal behaviors or health issues. Moreover, sound-based precision technologies offer solutions for improving environmental sustainability and resource management in pig farming. By continuously monitoring ventilation, feed distribution, and other key factors, these systems optimize resource use, reduce energy consumption, and detect stressors such as heat and poor air quality. The integration of sound technologies with other precision farming tools, such as physiological monitoring sensors and automated feeding systems, further enhances farm management and productivity. However, despite the advantages, challenges remain in terms of low accuracy and high initial costs, and further research is needed to improve specificity across different pig breeds and environmental conditions. Nonetheless, acoustic technologies hold immense promise for pig farming, offering enhanced management, an optimized performance, and improved animal welfare. Continued research can refine these tools and address the challenges, paving the way for a more efficient, profitable, and sustainable future for the industry.

Ethical food consumption in the digital age: Consumer attitudes towards digitally monitored animal welfare in pork products

Climate change is an emerging global reality with widespread effects on ecosystems and human communities. However, its significant impact on livestock animals often goes underdiscussed as more focus is given to impact of livestock production on climate change. Implementing high-welfare systems, such as digital monitoring of animals, can help mitigate climate-related challenges by reducing temperature fluctuations and controlling disease spread. Despite the potential benefits, consumer acceptance of this digital innovation remains uncertain. This study examines consumer attitudes toward digitally monitored animal welfare practices, aiming to understand their acceptance and the values they associate with these practices. It investigates the role of digital technology in enhancing consumer decision-making by addressing animal welfare concerns. Using means-end chain theory and Schwartz's value typology, the research explores the motivational layers and product attributes tied to consumer values. Semi-structured interviews with twenty pork consumers revealed hierarchical relationships between product attributes, benefits, and values. Analysis through NVivo 14 and LadderUX software generated themes and a hierarchical value map. The findings indicate that consumers prioritise attributes such as animal diets, stress-free environments, humane processing practices, and health conditions, linking these to both ethical and hedonic values. Intrinsic attributes like product appearance and freshness are crucial for at-home consumption decisions, while sustainable packaging also plays a role. The study also found differences in consumer behaviour based on the consumption context, with ethical decision-making often shifting to restaurateurs when dining out. The research underscores the importance of transparency, ethical practices, and product quality in influencing consumer decisions, providing actionable insights for marketing strategies that promote ethical consumption and improve animal welfare standards.

Proposing a short version of the Unesp-Botucatu pig acute pain scale using a novel application of machine learning technique

Surgical castration of males is carried out on a large scale in the US swine industry and the pain resulting from this procedure can be assessed using the Unesp-Botucatu pig composite acute pain scale (UPAPS). We aim to propose a short version of UPAPS based on the behaviors best-ranked by a random forest algorithm. We used behavioral observations from databases of surgically castrated pre-weaned and weaned pigs. We trained a random forest algorithm using the pain-free (pre-castration) and painful (post-castration) conditions as target variable and the 17 UPAPS pain-altered behaviors as feature variables. We ranked the behaviors by their importance in diagnosing pain. The algorithm was refined using a backward step-up procedure, establishing the Short UPAPS. The predictive capacity of the original and short version of the UPAPS was estimated by the area under the curve (AUC). In refinement, the algorithm with the five best-ranked behaviors had the lowest complexity and predictive capacity equivalent to the algorithm with all behaviors. The AUC of Short UPAPS (89.62%) was statistically equivalent (p = 0.6828) to that of UPAPS (90.58%). In conclusion, the proposed Short UPAPS might facilitate the implementation of a standard operating procedure to monitor and diagnose acute pain post-castration in large-scale systems.

Pig Face Open Set Recognition and Registration Using a Decoupled Detection System and Dual-Loss Vision Transformer

Effective pig farming relies on precise and adaptable animal identification methods, particularly in dynamic environments where new pigs are regularly added to the herd. However, pig face recognition is challenging due to high individual similarity, lighting variations, and occlusions. These factors hinder accurate identification and monitoring. To address these issues under Open-Set conditions, we propose a three-phase Pig Face Open-Set Recognition (PFOSR) system. In the Training Phase, we adopt a decoupled design, first training a YOLOv8-based pig face detection model on a small labeled dataset to automatically locate pig faces in raw images. We then refine a Vision Transformer (ViT) recognition model via a dual-loss strategy-combining Sub-center ArcFace and Center Loss-to enhance both inter-class separation and intra-class compactness. Next, in the Known Pig Registration Phase, we utilize the trained detection and recognition modules to extract representative embeddings from 56 identified pigs, storing these feature vectors in a Pig Face Feature Gallery. Finally, in the Unknown and Known Pig Recognition and Registration Phase, newly acquired pig images are processed through the same detection-recognition pipeline, and the resulting embeddings are compared against the gallery via cosine similarity. If the system classifies a pig as unknown, it dynamically assigns a new ID and updates the gallery without disrupting existing entries. Our system demonstrates strong Open-Set recognition, achieving an AUROC of 0.922, OSCR of 0.90, and F1-Open of 0.94. In the closed set, it attains a precision@1 of 0.97, NMI of 0.92, and mean average precision@R of 0.96. These results validate our approach as a scalable, efficient solution for managing dynamic farm environments with high recognition accuracy, even under challenging conditions.

Data recording and use of data tools for pig health management: perspectives of stakeholders in pig farming

Introduction

Data-driven strategies might combat the spreading of infectious pig disease and improve the early detection of potential pig health problems. The current study aimed to explore individual views on data recording and use of data tools for pig health management by recruiting stakeholders (N = 202) in Spain, Ireland, and the Netherlands.

Methods

Questionnaire focused on current on-farm challenges, current status of data recording on farms, and evaluation of the two mock data tools. Particularly, "benchmarking tool" was designed to visualize individual farm's pig mortality, targeting the management of infectious respiratory and gastrointestinal diseases; and "early-warning tool" was designed to generate an alarm through monitoring coughs in pigs, targeting the management of infectious respiratory diseases.

Results

Results showed that respiratory and gastrointestinal diseases and aggressive behaviors were the most frequently mentioned health challenge and welfare challenge, respectively. Most of the data was more frequently recorded electronically than on paper. In general, the "benchmarking tool" was perceived as useful for the management of infectious respiratory and gastrointestinal diseases, and the "early-warning tool" was evaluated as useful for the management of infectious respiratory diseases. Several barriers to the perceived usefulness of these two tools were identified, such as the lack of contextual information, inconvenience of data input, limited internet access, reliance on one's own experience and observation, technical hurdles, and mistrust of information output. The perceived usefulness of both tools was higher among highly educated participants, and those who reported being integrators and positive toward technology for disease control. Female participants and those who came from integrated farms evaluated the "early-warning tool" as more useful compared to their counterparts. The perceived usefulness of the "early-warning tool" was negatively affected by age and work experience, but positively affected by extensiveness of data recording, positive attitude toward technology, and the current use of technology.

Discussion

In summary, participants showed optimistic views on the use of data tools to support their decision-making and management of infectious pig respiratory and gastrointestinal diseases. It is noteworthy that data tools should not only convey the value of data for informed decision-making but also consider stakeholders' preconditions and needs for data tools.

RGB-based machine vision for enhanced pig disease symptoms monitoring and health management: a review

The growing demands of sustainable, efficient, and welfare-conscious pig husbandry have necessitated the adoption of advanced technologies. Among these, RGB imaging and machine vision technology may offer a promising solution for early disease detection and proactive disease management in advanced pig husbandry practices. This review explores innovative applications for monitoring disease symptoms by assessing features that directly or indirectly indicate disease risk, as well as for tracking body weight and overall health. Machine vision and image processing algorithms enable for the real-time detection of subtle changes in pig appearance and behavior that may signify potential health issues. Key indicators include skin lesions, inflammation, ocular and nasal discharge, and deviations in posture and gait, each of which can be detected non-invasively using RGB cameras. Moreover, when integrated with thermal imaging, RGB systems can detect fever, a reliable indicator of infection, while behavioral monitoring systems can track abnormal posture, reduced activity, and altered feeding and drinking habits, which are often precursors to illness. The technology also facilitates the analysis of respiratory symptoms, such as coughing or sneezing (enabling early identification of respiratory diseases, one of the most significant challenges in pig farming), and the assessment of fecal consistency and color (providing valuable insights into digestive health). Early detection of disease or poor health supports proactive interventions, reducing mortality and improving treatment outcomes. Beyond direct symptom monitoring, RGB imaging and machine vision can indirectly assess disease risk by monitoring body weight, feeding behavior, and environmental factors such as overcrowding and temperature. However, further research is needed to refine the accuracy and robustness of algorithms in diverse farming environments. Ultimately, integrating RGB-based machine vision into existing farm management systems could provide continuous, automated surveillance, generating real-time alerts and actionable insights; these can support data-driven disease prevention strategies, reducing the need for mass medication and the development of antimicrobial resistance.

How do pig veterinarians view technology-assisted data utilisation for pig health and welfare management? A qualitative study in Spain, the Netherlands, and Ireland

BACKGROUND

Application of data-driven strategies may support veterinarians' decision-making, benefitting pig disease prevention and control. However, little is known about veterinarians' need for data utilisation to support their decision-making process. The current study used qualitative methods, specifically focus group discussions, to explore veterinarians' views on data utilisation and their need for data tools in relation to pig health and welfare management in Spain, the Netherlands, and Ireland.

RESULTS

Generally, veterinarians pointed out the potential benefits of using technology for pig health and welfare management, but data is not yet structurally available to support their decision-making. Veterinarians pointed out the challenge of collecting, recording, and accessing data in a consistent and timely manner. Besides, the reliability, standardisation, and the context of data were identified as important factors affecting the efficiency and effectiveness of data utilisation by veterinarians. A user-friendly, adaptable, and integrated data tool was regarded as potentially helpful for veterinarians' daily work and supporting their decision-making. Specifically, veterinarians, particularly independent veterinary practitioners, noted a need for easy access to pig information. Veterinarians such as those working for integrated companies, corporate veterinarians, and independent veterinary practitioners expressed their need for data tools that provide useful information to monitor pig health and welfare in real-time, to visualise the prevalence of endemic disease based on a shared report between farmers, veterinarians, and other professional parties, to support decision-making, and to receive early warnings for disease prevention and control.

CONCLUSIONS

It is concluded that the management of pig health and welfare may benefit from data utilisation if the quality of data can be assured, the data tools can meet veterinarians' needs for decision-making, and the collaboration of sharing data and using data between farmers, veterinarians, and other professional parties can be enhanced. Nevertheless, several notable technical and institutional barriers still exist, which need to be overcome.

A systematic review of life-cycle GHG emissions from intensive pig farming: Accounting and mitigation

Pork accounts for approximately 35 % of the global meat supply, with approximately 747 million tons of CO2e greenhouse gas (GHG) emissions annually. To meet the increasing demand for pork, intensive farming is becoming the priority rearing system owing to its higher productivity. Given the climate transformation ambitions of the pig industry but the lack of knowledge and data, we conducted a systematic review of studies published in the period of 2010-2022 from a life-cycle perspective, with a focus on greenhouse gas emissions accounting and mitigation. The significant variations in systematic harmonized global warming intensities (GWIs) can be primarily attributed to differences in accounting approaches, activity data, technologies and geographical conditions. To understand more, we broke down the entire life cycle and revealed the underlying reasons for modelling mechanisms and data from the main emitters (e.g., feeding, pig rearing, and manure management). These findings are expected to support and improve the transparency, consistency, and comprehensiveness of life-cycle GHG emissions accounting in pig farming. Potential mitigation measures were also reviewed and discussed to provide insights to support the sustainable development of the pig industry.

New Perspective for Macroalgae-Based Animal Feeding in the Context of Challenging Sustainable Food Production

Food production is facing challenging times due to the pandemic, and climate change. With production expected to double by 2050, there is a need for a new paradigm in sustainable animal feed supply. Seaweeds offer a highly valuable opportunity in this regard. Seaweeds are classified into three categories: brown (Phaeophyceae), red (Rhodophyceae), and green (Chlorophyceae). While they have traditionally been used in aquafeed, their demand in the feed market is growing, parallelly increasing according to the food demand. Additionally, seaweeds are being promoted for their nutritional benefits, which contribute to the health, growth, and performance of animals intended for human consumption. Moreover, seaweeds contain biologically active compounds such as polyunsaturated fatty acids, antioxidants (polyphenols), and pigments (chlorophylls and carotenoids), which possess beneficial properties, including antibacterial, antifungal, antiviral, antioxidant, and anti-inflammatory effects and act as prebiotics. This review offers a new perspective on the valorization of macroalgae biomass due to their nutritional profile and bioactive components, which have the potential to play a crucial role in animal growth and making possible new sources of healthy food ingredients.

Prediction of the daily nutrient requirements of gestating sows based on sensor data and machine-learning algorithms

Precision feeding is a strategy for supplying an amount and composition of feed as close that are as possible to each animal's nutrient requirements, with the aim of reducing feed costs and environmental losses. Usually, the nutrient requirements of gestating sows are provided by a nutrition model that requires input data such as sow and herd characteristics, but also an estimation of future farrowing performances. New sensors and automatons, such as automatic feeders and drinkers, have been developed on pig farms over the last decade, and have produced large amounts of data. This study evaluated machine-learning methods for predicting the daily nutrient requirements of gestating sows, based only on sensor data, according to various configurations of digital farms. The data of 73 gestating sows was recorded using sensors such as electronic feeders and drinker stations, connected weight scales, accelerometers, and cameras. Nine machine-learning algorithms were trained on various dataset scenarios according to different digital farm configurations (one or two sensors), to predict the daily metabolizable energy and standardized ileal digestible lysine requirements for each sow. The prediction results were compared to those predicted by the InraPorc model, a mechanistic model for the precision feeding of gestating sows. The scenario predictions were also evaluated with or without the housing conditions and sow characteristics at artificial insemination usually integrated into the InraPorc model. Adding housing and sow characteristics to sensor data improved the mean average percentage error by 5.58% for lysine and by 2.22% for energy. The higher correlation coefficient values for lysine (0.99) and for energy (0.95) were obtained for scenarios involving an automatic feeder system (daily duration and number of visits with or without consumption) only. The scenarios including an automatic feeder combined with another sensor gave good performance results. For the scenarios using sow and housing characteristics and automatic feeder only, the root mean square error was lower with gradient tree boosting (0.91 MJ/d for energy and 0.08 g/d for lysine) compared with those obtained using linear regression (2.75 MJ/d and 1.07 g/d). The results of this study show that the daily nutrient requirements of gestating sows can be predicted accurately using data provided by sensors and machine-learning methods. It paves the way for simpler solutions for precision feeding.

Thermoregulation mechanisms and perspectives for validating thermal windows in pigs with hypothermia and hyperthermia: An overview

Specific anatomical characteristics make the porcine species especially sensitive to extreme temperature changes, predisposing them to pathologies and even death due to thermal stress. Interest in improving animal welfare and porcine productivity has led to the development of various lines of research that seek to understand the effect of certain environmental conditions on productivity and the impact of implementing strategies designed to mitigate adverse effects. The non-invasive infrared thermography technique is one of the tools most widely used to carry out these studies, based on detecting changes in microcirculation. However, evaluations using this tool require reliable thermal windows; this can be challenging because several factors can affect the sensitivity and specificity of the regions selected. This review discusses the thermal windows used with domestic pigs and the association of thermal changes in these regions with the thermoregulatory capacity of piglets and hogs.

Morphometric changes on the vulva from proestrus to oestrus of nulliparous and multiparous HYPERPROLIFIC sows

The aim of this study was to assess whether vulvar morphometric changes occurring in female pigs during proestrus and oestrus could be objective, accurate and predictive indicators of the onset to oestrus and thus performed artificial inseminations at the most appropriate time. For that purpose, pictures of vulvas from 60 hyperprolific females (30 gilts and 30 sows) during proestrus and oestrus were taken once a day. Vulva measurements (area, perimeter, length and width) on these pictures were performed using the image processing ImageJ software. Gilts and sows showed statistical differences (p < .01) in all vulvar morphometric measurements between proestrus and oestrus. Statistical differences in vulvar metrics were detected 24 h before the onset to oestrus, affecting all vulvar measurements in gilts, whereas only vulvar width was affected in sows. The image analysis used in this study may contribute to the development of smart technology in swine farming.

EFSA Panel on Animal Health and Welfare (AHAW), Nielsen SS, Alvarez J, Bicout DJ, Calistri P, Canali E, Drewe JA, Garin‐Bastuji B, Gonzales Rojas JL, Schmidt G, Herskin M, Michel V, Miranda Chueca MÁ, Mosbach‐Schulz O, Padalino B, Roberts HC, Stahl K, Velarde A, Viltrop A, Winckler C, Edwards S, Ivanova S, Leeb C, Wechsler B, Fabris C, Lima E, Mosbach‐Schulz O, Van der Stede Y, Vitali M, Spoolder H. Welfare of pigs on farm

This scientific opinion focuses on the welfare of pigs on farm, and is based on literature and expert opinion. All pig categories were assessed: gilts and dry sows, farrowing and lactating sows, suckling piglets, weaners, rearing pigs and boars. The most relevant husbandry systems used in Europe are described. For each system, highly relevant welfare consequences were identified, as well as related animal-based measures (ABMs), and hazards leading to the welfare consequences. Moreover, measures to prevent or correct the hazards and/or mitigate the welfare consequences are recommended. Recommendations are also provided on quantitative or qualitative criteria to answer specific questions on the welfare of pigs related to tail biting and related to the European Citizen's Initiative 'End the Cage Age'. For example, the AHAW Panel recommends how to mitigate group stress when dry sows and gilts are grouped immediately after weaning or in early pregnancy. Results of a comparative qualitative assessment suggested that long-stemmed or long-cut straw, hay or haylage is the most suitable material for nest-building. A period of time will be needed for staff and animals to adapt to housing lactating sows and their piglets in farrowing pens (as opposed to crates) before achieving stable welfare outcomes. The panel recommends a minimum available space to the lactating sow to ensure piglet welfare (measured by live-born piglet mortality). Among the main risk factors for tail biting are space allowance, types of flooring, air quality, health status and diet composition, while weaning age was not associated directly with tail biting in later life. The relationship between the availability of space and growth rate, lying behaviour and tail biting in rearing pigs is quantified and presented. Finally, the panel suggests a set of ABMs to use at slaughter for monitoring on-farm welfare of cull sows and rearing pigs.

Purchase Intention and Satisfaction of Online Shop Users in Developing Countries during the COVID-19 Pandemic

The aim of the research is to gain an understanding of consumer behavior in developing countries in the electronic environment. For this purpose, the four constructs of the PREVEINCOSA scale were analyzed: purchase intention as the dependent variable and trust, perceived value, and satisfaction as the determining variables of the former. For this purpose, by means of convenience sampling, an online questionnaire was shared with citizens in Mexico, Peru, and Colombia. A total of 330 questionnaires were collected from people who knew or had bought clothes in an online shop of the small company. Structural equation modeling (SEM) was used to validate the model and test the hypotheses. The results indicate that trust and satisfaction directly and positively influence value perception and online purchase intention and that value perception directly and positively influences online purchase intention of the small business consumer in Mexico, Peru, and Colombia. These results may be useful for the small fashion business sector in developing countries since it is observed that the online sales channel is not yet developed, which makes it necessary to develop strategies to reach customers in a more effective way. On the other hand, given the importance of this sector for the economy of developing countries, this study can be useful to governments who can establish public policies to provide training and technical assistance to benefit the development and competitiveness of this sector.