Dependable Sensing System for Pig Farming

Ariyadech, Sripong; Bonde, Amelie; Sangpetch, Orathai; Woramontri, Woranun; Siripaktanakon, Wachirawich; Pan, Shijia; Sangpetch, Akkarit; Noh, Hae Young; Zhang, Pei

doi:10.1109/gciot47977.2019.9058398

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…Again, installing the accelerometers is a challenge in and of itself. Various environmental factors like ambient temperature, air movement, moisture, and debris can affect sensors and thermal images [69,87,[148][149][150][151]. An ambient temperature of 20-30 • C is ideal for thermal imaging of animal skin [152,153].…”

Section: Challenges and Considerationsmentioning

confidence: 99%

Technological Tools and Artificial Intelligence in Estrus Detection of Sows—A Comprehensive Review

Sharifuzzaman,

Mun,

Ampode

et al. 2024

Animals

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In animal farming, timely estrus detection and prediction of the best moment for insemination is crucial. Traditional sow estrus detection depends on the expertise of a farm attendant which can be inconsistent, time-consuming, and labor-intensive. Attempts and trials in developing and implementing technological tools to detect estrus have been explored by researchers. The objective of this review is to assess the automatic methods of estrus recognition in operation for sows and point out their strong and weak points to assist in developing new and improved detection systems. Real-time methods using body and vulvar temperature, posture recognition, and activity measurements show higher precision. Incorporating artificial intelligence with multiple estrus-related parameters is expected to enhance accuracy. Further development of new systems relies mostly upon the improved algorithm and accurate data provided. Future systems should be designed to minimize the misclassification rate, so better detection is achieved.

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Section: Challenges and Considerationsmentioning

confidence: 99%

Technological Tools and Artificial Intelligence in Estrus Detection of Sows—A Comprehensive Review

Sharifuzzaman,

Mun,

Ampode

et al. 2024

Animals

View full text Add to dashboard Cite

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“…Figure 2 shows the components of this system, with hardware to the left and software components to the right. The data collection hardware uses geophone sensors [24], which have been proven reliable in the farm environment [2,5]. Measurements from these sensors are aggregated in the barn then uploaded off-site for further computation.…”

Section: Masshog System Overviewmentioning

confidence: 99%

“…They have been used in many settings to provide occupant activity and health monitoring [5,6,8,11,13,20,21]. In the farm environment, this approach has shown sensitivity to pig activities and robustness against the present hazards [2,5]. MassHog builds on this approach, including an existing passive analysis layer to handle piglet growth.…”

Section: Related Workmentioning

confidence: 99%

MassHog: Weight-Sensitive Occupant Monitoring for Pig Pens using Actuated Structural Vibrations

Codling

Bonde

Dong

et al. 2021

Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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In the swine livestock industry, weight tracking is commonly used to track health and growth of pigs. This is especially crucial during the farrowing period, where mother sows and their newborn piglets are housed together to facilitate nursing. Existing weight measurement methods, however, either provide only sporadic snapshots or have scalability and reliability issues in the harsh environment of industrial farms. This paper presents MassHog, a multi-layered system for ubiquitous weight measurement in harsh environments, such as pig pens, using structural vibrations. MassHog combines an existing sensor concept with actuated vibrations to weigh both piglets and adult hogs together. We evaluate the components of this system through testing deployments, including at real-world operating pig farms. Preliminary results show less than 2 kg mean absolute error for adult hogs. CCS CONCEPTS• Applied computing → Agriculture; • Computer systems organization → Sensor networks.

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The Research Progress of Vision-Based Artificial Intelligence in Smart Pig Farming

Wang

Jiang

Qiao

et al. 2022

Sensors

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Pork accounts for an important proportion of livestock products. For pig farming, a lot of manpower, material resources and time are required to monitor pig health and welfare. As the number of pigs in farming increases, the continued use of traditional monitoring methods may cause stress and harm to pigs and farmers and affect pig health and welfare as well as farming economic output. In addition, the application of artificial intelligence has become a core part of smart pig farming. The precision pig farming system uses sensors such as cameras and radio frequency identification to monitor biometric information such as pig sound and pig behavior in real-time and convert them into key indicators of pig health and welfare. By analyzing the key indicators, problems in pig health and welfare can be detected early, and timely intervention and treatment can be provided, which helps to improve the production and economic efficiency of pig farming. This paper studies more than 150 papers on precision pig farming and summarizes and evaluates the application of artificial intelligence technologies to pig detection, tracking, behavior recognition and sound recognition. Finally, we summarize and discuss the opportunities and challenges of precision pig farming.

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Dependable Sensing System for Pig Farming

Cited by 3 publications

References 8 publications

Technological Tools and Artificial Intelligence in Estrus Detection of Sows—A Comprehensive Review

Technological Tools and Artificial Intelligence in Estrus Detection of Sows—A Comprehensive Review

MassHog: Weight-Sensitive Occupant Monitoring for Pig Pens using Actuated Structural Vibrations

The Research Progress of Vision-Based Artificial Intelligence in Smart Pig Farming

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