Transforming the Adaptation Physiology of Farm Animals through Sensors

Neethirajan, Suresh

doi:10.20944/preprints202007.0417.v1

Cited by 26 publications

(1 citation statement)

References 86 publications

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“…Future research directions focus on developing more intelligent adaptive algorithms to accommodate complex behavior pattern requirements. This entails integrating other sensory data [30,31] and employing advanced machine learning methods to build more accurate models, necessitating ongoing improvements in chip technology and algorithm optimization. Such advancements not only enhance behavior recognition but also contribute to comprehensive improvements in horse training, aiming to enhance monitoring of equine health conditions.…”

Section: Discussionmentioning

confidence: 99%

Real-Time Horse Behavior Classification Based on Wearable Devices and Multi-level Threshold Analysis

Chen,

Li,

et al. 2024

Preprint

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Analyzing horse behavior is crucial for assessing training quality, particularly in accurately identifying actions like standing, walking, and running. Research has been conducted both domestically and internationally in this regard; however, challenges persist, including reliance on single sensors, poor real-time performance, and low identification accuracy. In view of these challenges, this study investigated real-time identification of horse behavior based on wearable devices. The system, centered around a microcontroller and utilizing a 4G network as a carrier, employs multi-axis IMU sensors as input sources to perceive horse posture. The study proposed a behavior classification method based on analysis of acceleration thresholds. The method consists primarily of two sequential stages: first, the resultant acceleration interval counting method, which employs a nonlinear segmentation approach for initial behavior classification; and second, the statistical analysis of variance parameters between segments, which when coupled with multi-level threshold processing, achieves a refined classification. Experimental results indicated that the interval counting method alone achieved an accuracy of 87.55%, while for the variance analysis method alone the accuracy was 90.87%. The proposed method, comprising the two stages, reached a classification accuracy of 91.57%, underpinning its usefulness in supporting future equine research.

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Section: Discussionmentioning

confidence: 99%

Real-Time Horse Behavior Classification Based on Wearable Devices and Multi-level Threshold Analysis

Chen,

Li,

et al. 2024

Preprint

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Changes in Sheep Behavior before Lambing

et al. 2021

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The aim of this study was to assess the duration and frequency of behavioral observations of pregnant ewes as they approached lambing. An understanding of behavioral changes before birth may provide opportunities for enhanced visual monitoring at this critical stage in the animal’s life. Behavioral observations for 17 ewes in late pregnancy were recorded during two separate time periods, which were 4 to 6 weeks before lambing and before giving birth. It was normal farm procedure for the sheep to come indoors for 6 weeks of close monitoring before lambing. The behaviors of standing, lying, walking, shuffling and contraction behaviors were recorded for each animal during both time periods. Over both time periods, the ewes spent a large proportion of their time either lying (0.40) or standing (0.42), with a higher frequency of standing (0.40) and shuffling (0.28) bouts than other behaviors. In the time period before giving birth, the frequency of lying and contraction bouts increased and the standing and walking bouts decreased, with a higher frequency of walking bouts in ewes that had an assisted lambing. The monitoring of behavioral patterns, such as lying and contractions, could be used as an alert to the progress of parturition.

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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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Transforming the Adaptation Physiology of Farm Animals through Sensors

Cited by 26 publications

References 86 publications

Real-Time Horse Behavior Classification Based on Wearable Devices and Multi-level Threshold Analysis

Real-Time Horse Behavior Classification Based on Wearable Devices and Multi-level Threshold Analysis

Changes in Sheep Behavior before Lambing

Economic Advantages of Individual Animal Identification in Fattening Pigs

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