Applying a high-precision tracking system to distinguish the spatiotemporal patterns of animal movement in grassland ecology

Li, Wenhuai; Bakker, Jonathan D.; Li, Yanlong; Zheng, Shuxia; Li, Frank Y.

doi:10.1016/j.biocon.2021.109016

Cited by 10 publications

(7 citation statements)

References 18 publications

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“…In addition to improving the algorithm, adding information by wearable devices is also helpful to increase the monitoring precision. The devices, including motion sensors, magnetometers, gyroscopes, and GPS receivers, have been widely used to monitor behavioral patterns in large farming environments (i.e., pastures and barns) ( Chapa et al, 2021 ; Li et al, 2021 ; Nikodem, 2021 ; Perisho and Hajnal, 2021 ; dos Reis et al, 2021 ). Specifically, with the use of tracking collar wore by pastured livestock, grazing behaviors were successfully identified for cattle ( Brennan et al, 2021 ) and sheep ( dos Reis et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, with the use of tracking collar wore by pastured livestock, grazing behaviors were successfully identified for cattle ( Brennan et al, 2021 ) and sheep ( dos Reis et al, 2021 ). The spatial resolution for outdoor studies was further improved to centimeter-level by coupling sensor collars with signal receivers deployed around the farm ( Li et al, 2021 ). In group-housed scenarios, Smartbow (Weibern, Austria), a commercialized ear-tag sensor system, also demonstrated promising results in monitoring complex interactions on reproduction traits in swine cohorts ( Perisho and Hajnal, 2021 ) and feeding behaviors of cows ( Chapa et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

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VTag: a semi-supervised pipeline for tracking pig activity with a single top-view camera

Chen

Morota

Lee

et al. 2022

Journal of Animal Science

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Precision livestock farming has become an important research focus with the rising demand of meat production in the swine industry. Currently, the farming practice is widely conducted by the technology of computer vision (CV), which automates monitoring pig activity solely based on video recordings. Automation is fulfilled by deriving imagery features that can guide CV systems to recognize animals’ body contours, positions, and behavioral categories. Nevertheless, the performance of the CV systems is sensitive to the quality of imagery features. When the CV system is deployed in a variable environment, its performance may decrease as the features are not generalized enough under different illumination conditions. Moreover, most CV systems are established by supervised learning, in which intensive effort in labeling ground truths for the training process is required. Hence, a semi-supervised pipeline, VTag, is developed in this study. The pipeline focuses on long-term tracking of pig activity without requesting any pre-labeled video but a few human supervisions to build a CV system. The pipeline can be rapidly deployed as only one top-view RGB camera is needed for the tracking task. Additionally, the pipeline was released as a software tool with a friendly graphical interface available to general users. Among the presented datasets, the average tracking error was 17.99 centimeters. Besides, with the prediction results, the pig moving distance per unit time can be estimated for activity studies. Lastly, as the motion is monitored, a heat map showing spatial hot spots visited by the pigs can be useful guidance for farming management. The presented pipeline saves massive laborious work in preparing training dataset. The rapid deployment of the tracking system paves the way for pig behavior monitoring.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

VTag: a semi-supervised pipeline for tracking pig activity with a single top-view camera

Chen

Morota

Lee

et al. 2022

Journal of Animal Science

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“…In fact, we believe that a particular value of VP-corrected deadreckoning, is that it will provide important detail about the effects of humans and anthropogenic landscape features on animal movements, a topic that is increasingly germane [128][129][130]. For example, understanding the extent of the permeability of anthropogenic barriers (e.g., fences, roads) and the hazards that they pose to speci c animals [131][132][133] is key to proper livestock and wildlife management [134][135][136][137]. Our work demonstrates that this approach details the intricacies of animal-barrier interactions, including the locations of barrier transgression as well as movement paths pre-, during and post-barrier transgression.…”

Section: The Utility Of Dead-reckoningmentioning

confidence: 99%

How Often Should Dead-Reckoned Animal Movement Paths be Corrected for Drift?

Gunner

Holton

Scantlebury

et al. 2021

Preprint

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BackgroundUnderstanding what animals do in time and space is important for a range of ecological questions, however accurate estimates of how animals use space is challenging. Within the use of animal-attached tags, radio telemetry (including the Global Positioning System (GPS)) is typically used to verify an animal’s location periodically. Straight lines are typically drawn between these ‘Verified Positions’ (VPs) so the interpolation of space-use is limited by the temporal- and spatial resolution of the system’s measurement. As such, parameters such as route-taken and distance travelled can be poorly represented when using VP systems alone. Dead-reckoning has been suggested as a technique to improve the accuracy and resolution of reconstructed movement paths, whilst maximising battery life of VP systems. This typically involves deriving travel vectors from motion sensor systems and periodically correcting path dimensions for drift with simultaneously deployed VP systems. How often paths should be corrected for drift, however, has remained unclear.Methods & ResultsHere, we review the utility of dead-reckoning across four contrasting model species using different forms of locomotion (the African lion Panthera leo, the Red-tailed tropicbird Phaethon rubricauda, the Magellanic penguin Spheniscus magellanicus, and the Imperial cormorant Leucocarbo atriceps). Simulations were performed to examine the extent of dead-reckoning error, relative to VPs, as a function of Verified Position correction (VP correction) rate and the effect of this on estimates of distance moved. Dead-reckoning error was greatest for animals travelling within air and water. We demonstrate how sources of measurement error can arise within VP-corrected dead-reckoned tracks and propose advancements to this procedure to maximise dead-reckoning accuracy.ConclusionsWe review the utility of VP-corrected dead-reckoning according to movement type and consider a range of ecological questions that would benefit from dead-reckoning, primarily concerning animal-barrier interactions and foraging strategies.

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“…In fact, we believe that a particular value of VP-corrected deadreckoning, is that it will provide important detail about the effects of humans and anthropogenic landscape features on animal movements, a topic that is increasingly germane [135][136][137]. For example, understanding the extent of the permeability of anthropogenic barriers (e.g., fences, roads) and the hazards that they pose to specific animals [138][139][140] is key to proper livestock and wildlife management [141][142][143][144]. Our work demonstrates that this approach details the intricacies of animal-barrier interactions, including the locations of barrier transgression as well as movement paths pre-, during and post-barrier transgression.…”

Section: The Utility Of Dead-reckoningmentioning

confidence: 99%

How often should dead-reckoned animal movement paths be corrected for drift?

et al. 2021

View full text Add to dashboard Cite

Background Understanding what animals do in time and space is important for a range of ecological questions, however accurate estimates of how animals use space is challenging. Within the use of animal-attached tags, radio telemetry (including the Global Positioning System, ‘GPS’) is typically used to verify an animal’s location periodically. Straight lines are typically drawn between these ‘Verified Positions’ (‘VPs’) so the interpolation of space-use is limited by the temporal and spatial resolution of the system’s measurement. As such, parameters such as route-taken and distance travelled can be poorly represented when using VP systems alone. Dead-reckoning has been suggested as a technique to improve the accuracy and resolution of reconstructed movement paths, whilst maximising battery life of VP systems. This typically involves deriving travel vectors from motion sensor systems and periodically correcting path dimensions for drift with simultaneously deployed VP systems. How often paths should be corrected for drift, however, has remained unclear. Methods and results Here, we review the utility of dead-reckoning across four contrasting model species using different forms of locomotion (the African lion Panthera leo, the red-tailed tropicbird Phaethon rubricauda, the Magellanic penguin Spheniscus magellanicus, and the imperial cormorant Leucocarbo atriceps). Simulations were performed to examine the extent of dead-reckoning error, relative to VPs, as a function of Verified Position correction (VP correction) rate and the effect of this on estimates of distance moved. Dead-reckoning error was greatest for animals travelling within air and water. We demonstrate how sources of measurement error can arise within VP-corrected dead-reckoned tracks and propose advancements to this procedure to maximise dead-reckoning accuracy. Conclusions We review the utility of VP-corrected dead-reckoning according to movement type and consider a range of ecological questions that would benefit from dead-reckoning, primarily concerning animal–barrier interactions and foraging strategies.

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Applying a high-precision tracking system to distinguish the spatiotemporal patterns of animal movement in grassland ecology

Cited by 10 publications

References 18 publications

VTag: a semi-supervised pipeline for tracking pig activity with a single top-view camera

VTag: a semi-supervised pipeline for tracking pig activity with a single top-view camera

How Often Should Dead-Reckoned Animal Movement Paths be Corrected for Drift?

How often should dead-reckoned animal movement paths be corrected for drift?

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