An overview of the current trends in precision pig farming technologies

Tzanidakis, Christos; Simitzis, Panagiotis; Arvanitis, Konstantinos G.; Panagakis, P.

doi:10.1016/j.livsci.2021.104530

Cited by 49 publications

(22 citation statements)

References 116 publications

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“…Besides common parameters such as body temperature, breath, sweat, and movement, biosensors worn by livestock could also be employed to measure biochemical parameters such as glucose levels in blood (Zhang et al., 2021). These items would complement the video and audio surveillance methods already used in “precision livestock farming.” These include cameras, sound systems, and audio in pig farming to track feeding and drinking behaviors and movement, amongst others (Tzanidakis et al., 2021). In addition, computer vision systems are also used in abattoirs to detect pathological lesions and fecal contamination on carcasses (Nastasijević & Vesković Moračanin, 2021).…”

Section: Modern Systems Fed By Numerous Real‐time and Diverse Datamentioning

confidence: 99%

“…These items would complement the video and audio surveillance methods already used in "precision livestock farming." These include cameras, sound systems, and audio in pig farming to track feeding and drinking behaviors and movement, amongst others (Tzanidakis et al, 2021). In addition, computer vision systems are also used in abattoirs to detect pathological lesions and fecal contamination on carcasses (Nastasijević & Vesković Moračanin, 2021).…”

Section: Digital Devicesmentioning

confidence: 99%

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Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools

Mu,

Kleter,

Bouzembrak

et al. 2024

Comp Rev Food Sci Food Safe

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To enhance the resilience of food systems to food safety risks, it is vitally important for national authorities and international organizations to be able to identify emerging food safety risks and to provide early warning signals in a timely manner. This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things as part of early warning and emerging risk identification tools and methods in the food safety domain. There is an ongoing rapid development of systems fed by numerous, real‐time, and diverse data with the aim of early warning and identification of emerging food safety risks. The suitability of big data and AI to support such systems is illustrated by two cases in which climate change drives the emergence of risks, namely, harmful algal blooms affecting seafood and fungal growth and mycotoxin formation in crops. Automation and machine learning are crucial for the development of future real‐time food safety risk early warning systems. Although these developments increase the feasibility and effectiveness of prospective early warning and emerging risk identification tools, their implementation may prove challenging, particularly for low‐ and middle‐income countries due to low connectivity and data availability. It is advocated to overcome these challenges by improving the capability and capacity of national authorities, as well as by enhancing their collaboration with the private sector and international organizations.

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Section: Modern Systems Fed By Numerous Real‐time and Diverse Datamentioning

confidence: 99%

Section: Digital Devicesmentioning

confidence: 99%

Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools

Mu,

Kleter,

Bouzembrak

et al. 2024

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

show abstract

“…There are near-term challenges during technology development, such as establishing validity with large amounts of real-world data from experimental and field settings. Building reliable machine learning models and decision support tools for large-scale pig farms is also nontrivial [6,26,27]. False alarms increase costs, reduce the trust of operators, and decrease efficiency.…”

Section: Machine Intelligence For Swine Behavioral Health Monitoringmentioning

confidence: 99%

“…However, the identification of all relevant indicators is difficult because pig well-being is essentially a multidimensional and complex concept [5]. To accomplish automated behavioral assessment at the single animal resolution, a high-throughput data acquisition system is needed to measure the indicators noninvasively, under varying conditions, and in real time with minimal human intervention [6,7]. Furthermore, data science approaches are needed to identify relative value and relationships amongst the indicators [8,9].…”

Section: Introductionmentioning

confidence: 99%

Behavioral Monitoring Tool for Pig Farmers: Ear Tag Sensors, Machine Intelligence, and Technology Adoption Roadmap

Pandey

Kalwa

Kong

et al. 2021

Animals

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Precision swine production can benefit from autonomous, noninvasive, and affordable devices that conduct frequent checks on the well-being status of pigs. Here, we present a remote monitoring tool for the objective measurement of some behavioral indicators that may help in assessing the health and welfare status—namely, posture, gait, vocalization, and external temperature. The multiparameter electronic sensor board is characterized by laboratory measurements and by animal tests. Relevant behavioral health indicators are discussed for implementing machine learning algorithms and decision support tools to detect animal lameness, lethargy, pain, injury, and distress. The roadmap for technology adoption is also discussed, along with challenges and the path forward. The presented technology can potentially lead to efficient management of farm animals, targeted focus on sick animals, medical cost savings, and less use of antibiotics.

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“…However, this development also leads to a greater distance between the fattener and the individual animal in terms of less time per animal [4]. In light of the trend of keeping "masses of animals" without precise knowledge about individual animals, an individual animal identification (IAI) system represents an option in the context of precision livestock farming [5].…”

Section: Introductionmentioning

confidence: 99%

Economic Advantages of Individual Animal Identification in Fattening Pigs

Thölke

Wolf

2022

Agriculture

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Pig farmers are exposed to strong international competition and confronted with low revenues, while production requirements are increasing. Individual electronic monitoring allows for closer observation of the fattening process, which can be useful for identifying weaknesses in the production process and carrying out more targeted cost management. Consequently, this study aimed to demonstrate a reduction in unit production costs through individual animal identification (IAI) using ultra-high-frequency radio frequency identification (UHF RFID) transponder ear tags. For data collection, day-old suckling piglets were individually marked. During fattening, these piglets (n = 224) were kept in 16 groups (n = 14 in each). Individual body weights and the feed consumption for each pen were recorded until slaughtering. These data were used to perform three hypothetical selection scenarios at nine different times, retrospectively, by selecting individual animals that deviated downwards from the mean value of live weight by 10, 15, or 20%. For each group, two different calculations were performed to highlight potential savings. High potential savings of direct costs (up to EUR 4.56) for female animals and castrates (EUR 5.44) were shown. Nevertheless, costs of individual data collection were EUR 4.06 per pig, and we found that IAI offers advantages for conventional fatteners for future fattening periods to work in an economically sustainable manner.

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An overview of the current trends in precision pig farming technologies

Cited by 49 publications

References 116 publications

Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools

Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools

Behavioral Monitoring Tool for Pig Farmers: Ear Tag Sensors, Machine Intelligence, and Technology Adoption Roadmap

Economic Advantages of Individual Animal Identification in Fattening Pigs

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