An IoT based Monitoring System to Detect Animal in the Railway Track using Deep Learning Neural Network

Ranjith, A.; Vijayaragavan, S. P.; Nirmalrani, V.; Muthukumaran, N.

doi:10.1109/icesc54411.2022.9885303

Cited by 14 publications

(4 citation statements)

References 8 publications

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“…Moreover, the system's notification mechanisms, whether through applications or Line notification, are consistent with previous research on application and SMS notifications [28], [31]. The integration of IoT with deep learning in this study aligns with earlier research supporting enhancements in notification systems [31], [32], [33], [34], [35]. Notably, a key distinction is observed, as much of the extant research has yet to progress into application development [28], [32].…”

Section: Discussionsupporting

confidence: 87%

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Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

Saeliw,

Hualkasin,

Puttinaovarat

2024

TEM Journal

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One of the challenges encountered by farmers engaged in livestock farming is the menace posed by stray or ownerless dogs, causing harm to the animals being raised on the farm. This not only adversely affects the health of the animals but also impacts the overall cost associated with their upbringing. Consequently, this research introduces the development of a sophisticated system aimed at preventing threats and intrusions by dogs that pose harm to farm animals. The system leverages Internet of Things (IoT) technology and employs Machine Learning algorithms, specifically Convolutional Neural Network, for real-time tracking and monitoring. The research findings reveal that the developed system demonstrates a high level of efficiency, swiftly and accurately classifying animals entering areas equipped with cameras, achieving an impressive accuracy rate of 92.54%. Furthermore, the system is equipped to promptly notify users and emit deterrent sounds to repel dogs entering the monitored area, enhancing its effectiveness in safeguarding livestock and optimizing farm management practices.

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Section: Discussionsupporting

confidence: 87%

“…Another study proposes animal monitoring along railway lines using IoT combined with deep learning, achieving a classification accuracy of 99.91% [33]. Additionally, the application of IoT and deep learning for monitoring animals in the wild is presented, utilizing hardware such as Raspberry Pi and cameras.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

Saeliw,

Hualkasin,

Puttinaovarat

2024

TEM Journal

View full text Add to dashboard Cite

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“…With the march of technology came the advent of satellite-based monitoring. GPS tags, delivering pinpoint location details at consistent timeframes, reshaped our grasp of extensive migratory paths, habitat preferences, and even interactions within species [11]. Frequently, these electronic tags integrated supplementary sensors that documented environmental conditions or the animal's physiological indicators, granting a rounded perspective of the creature's condition and environment.…”

Section: Resultsmentioning

confidence: 99%

Real-Time Tracking of Wildlife with IoT Solutions in Movement Ecology

Reddy,

Sharma

et al. 2023

J. Adv. Zool.

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Movement ecology has grown increasingly significant in the backdrop of global environmental changes, emphasizing the importance of understanding animal mobility patterns. The integration of Internet of Things (IoT) technology offers transformative potential for real-time wildlife tracking, addressing limitations of traditional methods like radio telemetry. Through IoT devices, researchers can acquire immediate, high-resolution datasets spanning vast distances, capturing multiple data points such as environmental conditions and physiological parameters. Existing implementations range from monitoring elephant movements in Africa to observing bird migrations. However, while promising, challenges like battery longevity, device weight, data management, and animal safety persist. As technological advances emerge, future prospects include more efficient, integrated solutions combining IoT with other technologies, poised to reshape and enrich our understanding of wildlife movement.

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“…Their significance lies not just in the sheer volume of data they can accumulate but in the depth of understanding they provide. Whether it's tracking the migratory patterns of a bird species or monitoring the heart rate of a marine mammal, animal wearables have bridged the gap between observation and understanding [4]. As we delve deeper into this study, the transformative role of IoT in movement ecology becomes increasingly evident [5].…”

Section: Introductionmentioning

confidence: 99%

Animal Wearables and the Transformation of Movement Ecology Through IoT

Srinivas,

Shashikanth,

Raghuram

et al. 2023

J. Adv. Zool.

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The transformative power of the Internet of Things (IoT) has reached the realms of movement ecology, revolutionizing the ways in which we monitor and understand animal movement patterns. This paper delves into the emergence and impact of animal wearables, devices equipped with advanced sensors that collect real-time data on an animal's location, physiological status, and surrounding environment. Historically, tracking animal movement relied heavily on manual methods, such as visual observations and rudimentary tagging. With the advent of IoT, these methods have evolved to incorporate real-time monitoring, improved accuracy, and the simultaneous tracking of multiple animals. Through a series of case studies, we highlight the novel insights into the behavior of migratory birds, marine life, and terrestrial mammals provided by IoT-enhanced wearables. Despite their evident advantages, these wearables also present challenges, from data management issues to concerns about animal welfare and data privacy. Looking forward, the potential for miniaturization, integration with AI, and enhanced connectivity promises even more refined tools for movement ecology research. This paper underscores the profound influence of IoT on movement ecology, heralding a new era of informed conservation and ecological understanding.

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An IoT based Monitoring System to Detect Animal in the Railway Track using Deep Learning Neural Network

Cited by 14 publications

References 8 publications

Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

Real-Time Tracking of Wildlife with IoT Solutions in Movement Ecology

Animal Wearables and the Transformation of Movement Ecology Through IoT

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