A Study on Sensor Based Animal Intrusion Alert System Using Image Processing Techniques

Jeevitha, S.; Kumar, Saurav

doi:10.1109/i-smac47947.2019.9032430

Cited by 19 publications

(3 citation statements)

References 6 publications

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“…The proposed method only detects the intrusion but does not have the repelling mechanism. In [10] we have used the motion detection PIR sensor to detect the animal movement near fields and the ANN. After the movement has been detected the object recognition algorithm is run .…”

Section: Literature Reviewsmentioning

confidence: 99%

Deep Learning and Internet of Things based Animal Intrusion Prevention System

Naganna

2023

IJSREM

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One of the serious problem faced by the farmers all over world especially for the farmers cultivating in the forest regions and in remote areas are unintimated attacks of the animals into the farmland which leads to the destruction of the crops and plantation and overall leads to the loss of production. Sometimes it may also lead to human- animal conflict which is highly dangerous and may harm the lives of either humans or animals. With the latest developments of technologies in Artificial Intelligence and Machine Learning, developing the intelligent model using Neural networks to identity the animals entering the farmland more accurately and then sending some control signals to the microcontroller to produce the loud irritating sound according to the animal identified thereby intruding the animals away from the farmland without the human intervention. Keywords – Deep Learning, Convolutional Neural Network, Image Processing, Internet of Things, Embedded Software.

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Section: Literature Reviewsmentioning

confidence: 99%

Deep Learning and Internet of Things based Animal Intrusion Prevention System

Naganna

2023

IJSREM

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“…Traditionally, farmers have relied on electric fences to prevent animals from entering their fields, aiming to mitigate the effects of these challenges [5]. However, it is crucial to recognize that while the primary objective is safeguarding crops and maintaining productivity, ensuring humans' and animals' health and safety remains the utmost priority in this complex coexistence [6]. In our research, we resolve the challenges posed by industrial expansion into forested areas by implementing an IoT-based detection model linked to cloud computing infrastructure and specialized hardware solutions.…”

Section: Introductionmentioning

confidence: 99%

IoT-Based Object-Detection System to Safeguard Endangered Animals and Bolster Agricultural Farm Security

Raiaan,

Fahad,

Chowdhury

et al. 2023

Future Internet

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Significant threats to ecological equilibrium and sustainable agriculture are posed by the extinction of animal species and the subsequent effects on farms. Farmers face difficult decisions, such as installing electric fences to protect their farms, although these measures can harm animals essential for maintaining ecological equilibrium. To tackle these essential issues, our research introduces an innovative solution in the form of an object-detection system. In this research, we designed and implemented a system that leverages the ESP32-CAM platform in conjunction with the YOLOv8 object-detection model. Our proposed system aims to identify endangered species and harmful animals within farming environments, providing real-time alerts to farmers and endangered wildlife by integrating a cloud-based alert system. To train the YOLOv8 model effectively, we meticulously compiled diverse image datasets featuring these animals in agricultural settings, subsequently annotating them. After that, we tuned the hyperparameter of the YOLOv8 model to enhance the performance of the model. The results from our optimized YOLOv8 model are auspicious. It achieves a remarkable mean average precision (mAP) of 92.44% and an impressive sensitivity rate of 96.65% on an unseen test dataset, firmly establishing its efficacy. After achieving an optimal result, we employed the model in our IoT system and when the system detects the presence of these animals, it immediately activates an audible buzzer. Additionally, a cloud-based system was utilized to notify neighboring farmers effectively and alert animals to potential danger. This research’s significance lies in its potential to drive the conservation of endangered species while simultaneously mitigating the agricultural damage inflicted by these animals.

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“…They offer safe and humane way of controlling animal intrusion and do not affect the human beings due to their operating frequencies that are way above the audible range of the human ear. Various methods are proposed in literature to avoid AVC and implement effective repellents [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Animal Health Monitoring and Intrusion Detection System based on LORAWAN

Mohandass¹

2021

TURCOMAT

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In this paper, two important aspects of wildlife monitoring namely Health monitoring and Animal-vehicle collision (AVC) avoidance are addressed. Wildlife health monitoring is an important requirement in forest management. This necessitates monitoring the wellbeing and tracking of animals. In this work, temperature sensor and heart rate sensor are used to monitor the health condition of elephants. The sensors will give an indication, if the elephant’s body temperature and heart rate are not in range. An animal tracking system using Global Positioning System (GPS) is provided for remotely monitoring animal position. Internet of Things (IoT) technology is used to transmit the collected data from the sensors to a remote station where the data is stored in a database. If there is any critical condition identified, the system alerts the concerned officers/veterinary doctors to provide necessary treatment immediately. AVC is another important issue that affects wildlife heavily. In order to counteract AVC, a unified approach of Smart Animal Intrusion Detection System that monitors the movement of animals by integrating a number of sensors with LoRaWAN network, is proposed in this paper. Performance analysis of the proposed system is investigated using CupCarbon Software.

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A Study on Sensor Based Animal Intrusion Alert System Using Image Processing Techniques

Cited by 19 publications

References 6 publications

Deep Learning and Internet of Things based Animal Intrusion Prevention System

Deep Learning and Internet of Things based Animal Intrusion Prevention System

IoT-Based Object-Detection System to Safeguard Endangered Animals and Bolster Agricultural Farm Security

Animal Health Monitoring and Intrusion Detection System based on LORAWAN

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