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Articles de revues sur le sujet « Real- Time Wildlife Monitoring »

Auteur : Grafiati

Publié le 7 juin 2025

Mis à jour le 2 août 2025

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1

Jagannathan, Preetha, Kalaivanan Saravanan, Subramaniyam Deepajothi, and Sharmila Vadivel. "Federated Learning and Blockchain-Based Collaborative Framework for Real-Time Wild Life Monitoring." Cybernetics and Information Technologies 25, no. 1 (2025): 19–35. https://doi.org/10.2478/cait-2025-0002.

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Abstract Effective wildlife monitoring in hilly and rural areas can protect communities and diminish human-wildlife conflicts. A collaborative framework may overcome challenges like inadequate data integrity and security, declining detection accuracy over time, and delays in critical decision-making. The proposed study aims to develop a real-time wildlife monitoring framework using Federated Learning and blockchain to improve conservation strategies. Min-max normalization enhances training data and Elastic Weight Consolidation (EWC) for real-time adaptation. The improvised YOLOv8+EWC enables r

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Ma, Zhibin, Yanqi Dong, Yi Xia, Delong Xu, Fu Xu, and Feixiang Chen. "Wildlife Real-Time Detection in Complex Forest Scenes Based on YOLOv5s Deep Learning Network." Remote Sensing 16, no. 8 (2024): 1350. http://dx.doi.org/10.3390/rs16081350.

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With the progressively deteriorating global ecological environment and the gradual escalation of human activities, the survival of wildlife has been severely impacted. Hence, a rapid, precise, and reliable method for detecting wildlife holds immense significance in safeguarding their existence and monitoring their status. However, due to the rare and concealed nature of wildlife activities, the existing wildlife detection methods face limitations in efficiently extracting features during real-time monitoring in complex forest environments. These models exhibit drawbacks such as slow speed and

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Gaikwad, Dr Jitendra. "ML Based WildLife Trap Camera System." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 2037–40. http://dx.doi.org/10.22214/ijraset.2024.65557.

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This paper presents a real-time wildlife species classification system leveraging Convolutional Neural Networks (CNNs). The system classifies wildlife images from the Oregon Wildlife dataset and extends its functionality to process live video feeds for species recognition. Using PyTorch and OpenCV, the model achieves robust accuracy on a balanced dataset and demonstrates real-time inference capabilities, making it a potential tool for wildlife monitoring and conservation efforts.

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Wall, Jake, George Wittemyer, Brian Klinkenberg, and Iain Douglas-Hamilton. "Novel opportunities for wildlife conservation and research with real-time monitoring." Ecological Applications 24, no. 4 (2014): 593–601. http://dx.doi.org/10.1890/13-1971.1.

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Anitha Bujunuru, Nanam Shiva Kumar, Pedimalla Nishwanth, and Mylaram Manoj Kumar. "Real-Time Zigbee Sensor Network for Forest Monitoring and Wildlife Conservation." International Journal of Latest Technology in Engineering Management & Applied Science 14, no. 5 (2025): 423–26. https://doi.org/10.51583/ijltemas.2025.140500043.

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Abstract Tree smuggling, especially of high-value species like sandalwood and teak, poses a significant threat to biodiversity and forest ecosystems. These trees are highly sought after for their commercial value, making them frequent targets of illegal logging operations. This illicit activity not only depletes valuable natural resources but also contributes to deforestation and environmental degradation. In response to this growing concern, an IoT-based monitoring system has been developed to detect and prevent such activities. At the core of the system is a Node MCU microcontroller, integra

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THOMAS, SHINEY, George Elsa, Francis Alphonsa, Job Anna, and Maria James Ann. "Wildlife Detection And Recognition Using YOLO V8." International Journal on Emerging Research Areas (IJERA) 04, no. 02 (2025): 81–87. https://doi.org/10.5281/zenodo.14714518.

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The use of YOLOv8 for wildlife detection and recognition has transformed real-time monitoring across diverse environments, particularly in rural, forested, and human-wildlife conflict zones. Its lightweight architecture, efficient feature extraction, and deep learning capabilities make it a preferred tool for wildlife conservation. YOLOv8’s ability to detect and classify animals in real-time has enhanced wildlife population monitoring, reduced risks of human-wildlife encounters, and contributed to biodiversity conservation. A major advancement in YOLOv8 is its ability to perform well und

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A, Ajay. "Intelligent Habitat Surveillance and Protection System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48814.

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Abstract— As mortal-wildlife commerce grows further frequent, and wildlife territories face adding environmental pressures, covering beast get has come pivotal for conservation sweats and ecological exploration. This paper presents an AI-driven Wildlife behavior Monitoring System using computer vision, deep literacy, and YOLOv8 to descry, classify, and dissect wildlife conditioning in real-time. The proposed system directly identifies species and tracks actions similar to feeding, movement, resting, and social relations across different territories. It provides detailed receptivity through spa

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Mangewa, Lazaro J., Patrick A. Ndakidemi, and Linus K. Munishi. "Integrating UAV Technology in an Ecological Monitoring System for Community Wildlife Management Areas in Tanzania." Sustainability 11, no. 21 (2019): 6116. http://dx.doi.org/10.3390/su11216116.

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Unmanned aerial vehicles (UAV) have recently emerged as a new remote sensing aerial platform, and they are seemingly advancing real-time data generation. Nonetheless, considerable uncertainties remain in the extent to which wildlife managers can integrate UAVs into ecological monitoring systems for wildlife and their habitats. In this review, we discuss the recent progress and gaps in UAV use in wildlife conservation and management. The review notes that there is scanty information on UAV use in ecological monitoring of medium-to-large mammals found in groups in heterogeneous habitats. We also

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P, Dr Srinivas Babu. "Implementation of Amphibious Robot for Wildlife Monitoring." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (2024): 2280–85. https://doi.org/10.22214/ijraset.2024.66202.

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This paper presents an amphibious robot designed for wildlife surveillance on land and environmental monitoring across both land and water. The system incorporates AI-driven technologies and integrated sensors for real-time data collection, including water quality parameters such as turbidity, TDS, and pH. Equipped with advanced surveillance capabilities and geofencing for boundary detection, the robot navigates autonomously in challenging terrains. Solar-powered operation ensures sustainability and cost efficiency. This innovative tool supports ecosystem health assessment and aids in environm

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Renuka Moharir, Srushti Khiratkar, Arati Jadhao, Shruti Khaire, and Dr. Amit Welekar. "Real-Time Tiger Detection Using Ml and Sensor Integration for Village Protection." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 03 (2025): 1042–46. https://doi.org/10.47392/irjaeh.2025.0148.

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In this research, we addressed the critical issue of human-wildlife conflict, focusing on tigers entering villages near forested regions. Such incidents endanger human lives and livelihoods while also threatening wildlife conservation efforts. To mitigate this, we developed an Automated Tiger Detection System integrating motion sensors, ultrasonic sensors, thermal cameras, and machine learning algorithms. This system detects tiger presence in real time and sends alerts to villagers and authorities, enabling swift preventive action. We first discussed the limitations of traditional monitoring m

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Radha, Mrs C., Mr M. Madheswaran, Mr M. Lokesh, and Mr M. Mohammad Althaf. "Environmental Monitoring in Internet of Things (IOT)." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 1658–63. http://dx.doi.org/10.22214/ijraset.2024.60086.

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Abstract: The Internet of Things (IoT) connects physical objects to the internet, enabling autonomous data collection and analysis. In environmental monitoring, IoT plays a crucial role in gathering real-time data on parameters like air and water quality, weather, wildlife behavior, and urban heat islands. This abstract explores how IoT is used in environmental monitoring, covering standards, device types, and applications. IoT comprises interconnected devices that collect environmental data and transmit it for analysis, facilitating continuous observation and better resource management. Stand

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Reddy, K. Chenna, Senthil Kumar R, Shivi Sharma, N. Gobi, Srinivas D, and P. Vishnu Prasanth. "Real-Time Tracking of Wildlife with IoT Solutions in Movement Ecology." Journal of Advanced Zoology 44, S-3 (2023): 1122–34. http://dx.doi.org/10.17762/jaz.v44is-5.1191.

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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 monit

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Fergus, Paul, Carl Chalmers, Steven Longmore, and Serge Wich. "Harnessing Artificial Intelligence for Wildlife Conservation." Conservation 4, no. 4 (2024): 685–702. http://dx.doi.org/10.3390/conservation4040041.

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The rapid decline in global biodiversity demands innovative conservation strategies. This paper examines the use of artificial intelligence (AI) in wildlife conservation, focusing on the Conservation AI platform. Leveraging machine learning and computer vision, Conservation AI detects and classifies animals, humans, and poaching-related objects using visual spectrum and thermal infrared cameras. The platform processes these data with convolutional neural networks (CNNs) and transformer architectures to monitor species, including those that are critically endangered. Real-time detection provide

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Tatiraju, Tatiraju, K. Dhineshkumar, Haritima Mishra, and Chandra Sekar P. "Machine Learning-Enhanced Wireless Sensor Networks for Real-Time Environmental Monitoring." International Journal of BIM and Engineering Science 10, no. 1 (2025): 18–25. http://dx.doi.org/10.54216/ijbes.100103.

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Wireless Sensor Networks (WSNs) are pivotal for real-time environmental monitoring, providing valuable data on variables like temperature, humidity, and pollution levels. However, ensuring timely and accurate data transmission and analysis remains a challenge due to resource constraints in WSNs. This study introduces a machine learning-enhanced WSN framework that leverages predictive algorithms for efficient data processing and anomaly detection in real time. By integrating machine learning models, the system can predict environmental trends, detect sensor faults, and identify unusual events,

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Lee, Seunghyeon, Youngkeun Song, and Sung-Ho Kil. "Feasibility Analyses of Real-Time Detection of Wildlife Using UAV-Derived Thermal and RGB Images." Remote Sensing 13, no. 11 (2021): 2169. http://dx.doi.org/10.3390/rs13112169.

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Wildlife monitoring is carried out for diverse reasons, and monitoring methods have gradually advanced through technological development. Direct field investigations have been replaced by remote monitoring methods, and unmanned aerial vehicles (UAVs) have recently become the most important tool for wildlife monitoring. Many previous studies on detecting wild animals have used RGB images acquired from UAVs, with most of the analyses depending on machine learning–deep learning (ML–DL) methods. These methods provide relatively accurate results, and when thermal sensors are used as a supplement, e

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Mou, Chao, Tengfei Liu, Chengcheng Zhu, and Xiaohui Cui. "WAID: A Large-Scale Dataset for Wildlife Detection with Drones." Applied Sciences 13, no. 18 (2023): 10397. http://dx.doi.org/10.3390/app131810397.

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Drones are widely used for wildlife monitoring. Deep learning algorithms are key to the success of monitoring wildlife with drones, although they face the problem of detecting small targets. To solve this problem, we have introduced the SE-YOLO model, which incorporates a channel self-attention mechanism into the advanced real-time object detection algorithm YOLOv7, enabling the model to perform effectively on small targets. However, there is another barrier; the lack of publicly available UAV wildlife aerial datasets hampers research on UAV wildlife monitoring algorithms. To fill this gap, we

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R, Deepika, Shalini P, Sona Saran S, Sruthi S, Suvarnamala T, and Poongothai M. "Agro Guard Edge AI - Development of Sustainable IoT Framework for Wildlife Intrusion Detection." Journal of Electrical Engineering and Automation 6, no. 4 (2025): 325–42. https://doi.org/10.36548/jeea.2024.4.005.

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The rising global population and increasing food demand have placed immense pressure on agriculture, particularly in regions like the Marudhamalai foothills in Coimbatore district, where farmers face frequent crop damage caused by wildlife intrusions, such as wild boars and deer. To address these challenges, a wildlife intrusion detection system has been developed to safeguard crops, enhance agricultural productivity, and enable coexistence with wildlife. The system combines a laser detection setup with an AI-CAM that employs lightweight deep learning algorithms for real-time animal detection

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Samuel, Wealth Abiola, Irunokhai Eric Aghiomesi, Ademola Samuel Adedeji, et al. "Revolutionalizing Forest Monitoring and Conservation Using Internet of Things (IoT): The Challenges and the Opportunities." Asian Journal of Research in Agriculture and Forestry 9, no. 4 (2023): 276–82. http://dx.doi.org/10.9734/ajraf/2023/v9i4256.

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Over some decades, advancement has come to the way forest is being monitored. The process of monitoring the forest area have advanced beyond human-power into technology. Nigeria forests have experience degeneration from illegal logging to encroachment resulting from population growth and agricultural activities. The wildlife species are endangered due to human illegal activities in the forest area. Manual monitoring has achieved some results in keeping the forest safe but there is a need for more effective and real time forest monitoring. Therefore, digital technology should be analyzed for mo

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Zhang, Hongping, Jie Jiang, Dong Wei, and Jie Jiang. "A WILDLIFE MONITORING SYSTEM BASED ON TIANDITU AND BEIDOU: IN CASE OF THE TIBETAN ANTELOPE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4 (June 13, 2016): 259–62. http://dx.doi.org/10.5194/isprs-archives-xli-b4-259-2016.

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Positioning and tracking wildlife is already being an effective way to collect biological information for research and species of wildlife protection. The common technologies of tracking wildlife are divided into several categories, such as radio tracking technology, GPS tracking system, radio frequency identification technology (RFID), and SIM card based technology. Some positive results achieved from these technologies, but there are some problems in location accuracy, price of the system. Taking the case of the protection of the Tibetan antelope, this paper introduces a wildlife monitoring

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Zhang, Hongping, Jie Jiang, Dong Wei, and Jie Jiang. "A WILDLIFE MONITORING SYSTEM BASED ON TIANDITU AND BEIDOU: IN CASE OF THE TIBETAN ANTELOPE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4 (June 13, 2016): 259–62. http://dx.doi.org/10.5194/isprsarchives-xli-b4-259-2016.

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Positioning and tracking wildlife is already being an effective way to collect biological information for research and species of wildlife protection. The common technologies of tracking wildlife are divided into several categories, such as radio tracking technology, GPS tracking system, radio frequency identification technology (RFID), and SIM card based technology. Some positive results achieved from these technologies, but there are some problems in location accuracy, price of the system. Taking the case of the protection of the Tibetan antelope, this paper introduces a wildlife monitoring

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Jean, Devin, Jesse Turner, Will Hedgecock, György Kalmár, George Wittemyer, and Ákos Lédeczi. "Animal-Borne Adaptive Acoustic Monitoring." Journal of Sensor and Actuator Networks 14, no. 4 (2025): 66. https://doi.org/10.3390/jsan14040066.

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Animal-borne acoustic sensors provide valuable insights into wildlife behavior and environments but face significant power and storage constraints that limit deployment duration. We present a novel adaptive acoustic monitoring system designed for long-term, real-time observation of wildlife. Our approach combines low-power hardware, configurable firmware, and an unsupervised machine learning algorithm that intelligently filters acoustic data to prioritize novel or rare sounds while reducing redundant storage. The system employs a variational autoencoder to project audio features into a low-dim

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Razek, Adel. "Matching Role of Observation and its Replication Model in Managing Intelligent Paradigms and Monitoring Natural and Artificial Complexities." Digital Technologies Research and Applications 3, no. 2 (2024): 176–82. http://dx.doi.org/10.54963/dtra.v3i2.280.

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This contribution aims to shed light on the character of the observation-modeling link, and the role of the matching of its faces, in the management of different events. These include intelligent theories and digital tools, as well as the complexity of dynamic processes of natural and artificial phenomena. Such matching in the link could be practiced in offline or real-time mode. Offline mode mainly concerns the governance of intelligent theories and digital tools mimicking physical paradigms. Real-time mode concerns dynamic processes involving a significant degree of complexity. This exists i

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Shital A. Birari. "IoT Based Anti-Poaching System for Trees and Wildlife Monitoring System in Remote Area." Journal of Information Systems Engineering and Management 10, no. 43s (2025): 1181–91. https://doi.org/10.52783/jisem.v10i43s.8538.

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Illegal poaching and deforestation pose significant threats to biodiversity, leading to the decline of wildlife populations and environmental degradation. To address this issue, we propose an IoT-based Anti-Poaching System for real-time monitoring and activity detection in remote forest areas. The system leverages a Raspberry Pi as a low-power, cost-effective device for video streaming and uploading footage to cloud storage for further analysis. For animal detection, a machine learning-based model is employed to recognize species and track their movements. This enables wildlife conservationist

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Zhong, Yujie, Xiao Li, Jiangjian Xie, and Junguo Zhang. "A Lightweight Automatic Wildlife Recognition Model Design Method Mitigating Shortcut Learning." Animals 13, no. 5 (2023): 838. http://dx.doi.org/10.3390/ani13050838.

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Recognizing wildlife based on camera trap images is challenging due to the complexity of the wild environment. Deep learning is an optional approach to solve this problem. However, the backgrounds of images captured from the same infrared camera trap are rather similar, and shortcut learning of recognition models occurs, resulting in reduced generality and poor recognition model performance. Therefore, this paper proposes a data augmentation strategy that integrates image synthesis (IS) and regional background suppression (RBS) to enrich the background scene and suppress the existing backgroun

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Hegde, Nikhil Ramesh, and Dr S. Manju Bargavi. "Smart Conservation: Integrating AI for Enhanced Wildlife Monitoring." International Research Journal of Computer Science 11, no. 01 (2024): 62–67. http://dx.doi.org/10.26562/irjcs.2024.v1101.10.

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Conservation strategies are redefined by smart conservation, which combines wildlife monitoring and artificial intelligence (AI). Artificial intelligence (AI) technologies, such as computer vision and machine learning, automate the identification of species through image and audio analysis, making habitat health assessment and population monitoring more effective. By identifying changes in land cover and habitat decline, AI in conjunction with satellite photography enables ongoing habitat monitoring. AI-driven analytics facilitates the integration of various data sources and offers insightful

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r M, Chandrasheka. "IoT-Integrated Farm Security System with Real-Time Alerts and Intrusion Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41569.

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Agricultural farms face significant threats from unauthorized access and wildlife intrusions, leading to substantial crop damage and financial losses for farmers. To address this issue, we propose the Smart Agriculture Farm Protection System, an automated security solution utilizing a microcontroller, multiple sensors, repellent devices, and an alert system with a GSM module. The system is designed to detect movement, trigger alarms, and send real-time alerts to the farm owner, ensuring prompt action against potential threats. Upon detecting an intrusion, the sensors activate visual and audito

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Sweeney, F. P., O. Courtenay, V. Hibberd, et al. "Environmental Monitoring of Mycobacterium bovis in Badger Feces and Badger Sett Soil by Real-Time PCR, as Confirmed by Immunofluorescence, Immunocapture, and Cultivation." Applied and Environmental Microbiology 73, no. 22 (2007): 7471–73. http://dx.doi.org/10.1128/aem.00978-07.

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ABSTRACT Real-time PCR was used to detect and quantify Mycobacterium bovis cells in naturally infected soil and badger feces. Immunomagnetic capture, immunofluorescence, and selective culture confirmed species identification and cell viability. These techniques will prove useful for monitoring M. bovis in the environment and for elucidating transmission routes between wildlife and cattle.

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Udhayakumar S, Mr. "Wild Guard: Solar Powered Animal Intrusion Detection and Alert System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem46644.

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Abstract—Monitoring animal movement and health is a critical component of veterinary science, livestock management, and wildlife conservation, particularly in regions where human-animal interactions are frequent. This paper presents a solar-powered animal detection and alert system that utilizes real-time image processing and deep learning to enhance animal healthcare monitoring. The system integrates a camera module with the YOLO (You Only Look Once) object detection algorithm to identify animals entering predefined zones such as agricultural fields, roadways, or human habitations. Unlike con

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Shahil A K, Muhammed, Jalwa V P, Afnan M K, Rababa Kareem Kollathodi, and Muneer VK. "Automated Catalogue Using Object Detection." International Journal of Advanced Networking and Applications 16, no. 01 (2024): 6263–69. http://dx.doi.org/10.35444/ijana.2024.16104.

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The areas of security and wildlife monitoring constantly change, and the use of CCTV surveillance cameras surely has improved our ability to protect a range of environments. However, innovative methods are needed to address the persistent challenge of efficiently examining large quantities of video in cases of theft or animal contact. This study presents a new "Automated Catalogue System using Object Detection," which makes use of innovative technologies like Single Shot Multibox Detector (SSD), OpenCV, and MobileNetV3. The system catalogs objects that are identified and operates in real time

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G., Mohanavel, Mariselvam V, Kannan S, Mohanraj K, and Nikilkumar E. "INNOVATIVE SOLUTIONS SAFEGUARDING CROPS AND WILDLIFE WITH INTELLIGENT SURVEILLANCE." International Journal of Technical Research & Science 9, Spl (2024): 27–35. http://dx.doi.org/10.30780/specialissue-iset-2024/007.

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In agriculture, the delicate balance between safeguarding crops and protecting wildlife presents a significant challenge. Traditional methods often involve reactive measures, leading to crop damage and wildlife disturbance. However, emerging technologies offer innovative solutions through intelligent surveillance systems. These systems leverage advanced technologies like predictive analytics and AI algorithms to monitor fields and wells proactively. By deploying such intelligent surveillance systems, it becomes possible to protect crops from wildlife damage while ensuring the safety of animals

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S, Veeramani, Suja Rose R.S., and Suyog Subashrao Patil. "Integrating Geospatial and Real Time Technologies for Risk zone monitoring in Periyar Tiger Reserve, India." Journal of Geomatics 18, no. 2 (2024): 97–105. http://dx.doi.org/10.58825/jog.2024.18.2.156.

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There are numerous monitoring technologies available today, owing to the rapid advancements in technology and the increasing demand for safety and security in forests. Real-time monitoring with AI cameras, which are commonly utilized for creating and updating real-time features through surveillance, stands out as one of the most effective monitoring solutions. The objective of this current research is to monitor various risk zones within the Periyar Tiger Reserve by integrating real-time AI camera with geographic data. AI cameras were strategically placed using spatial analysis techniques. Lev

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Lei, Jialin, Shuhui Gao, Muhammad Awais Rasool, Rong Fan, Yifei Jia, and Guangchun Lei. "Optimized Small Waterbird Detection Method Using Surveillance Videos Based on YOLOv7." Animals 13, no. 12 (2023): 1929. http://dx.doi.org/10.3390/ani13121929.

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Waterbird monitoring is the foundation of conservation and management strategies in almost all types of wetland ecosystems. China’s improved wetland protection infrastructure, which includes remote devices for the collection of larger quantities of acoustic and visual data on wildlife species, increased the need for data filtration and analysis techniques. Object detection based on deep learning has emerged as a basic solution for big data analysis that has been tested in several application fields. However, these deep learning techniques have not yet been tested for small waterbird detection

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Achyutha, Prasad N. "AN EPHEMERAL INVESTIGATION ON FOREST FIRE, WILDLIFE PRESERVATION, AND TRIBAL PREPAREDNESS USING DEEP LEARNING AND IOT." International Journal For Technological Research In Engineering 11, no. 4 (2024): 34–40. https://doi.org/10.5281/zenodo.10450146.

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Forest fire is a serious threat to the environment and wildlife. It can also harm the tribal community as well as the people living in close vicinity to forest areas. Early detection and mitigation are crucial to avoid their destructive impact. By using the cameras used to track movement of wildlife we can capture real- time images and videos of the forest area. The data collected by these cameras is transmitted to a control center via wireless communication, where a deep learning algorithm is used for analysis. These algorithms can accurately identify smoke, flames, and fire within the image.

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N, Subha. "AI-Driven Ultrasound System for Wildlife Detection and Sustainable Crop Protection." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 448–52. https://doi.org/10.22214/ijraset.2025.70176.

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Abstract: The intrusion of wild animals into agricultural areas significantly threatens crop productivity and farmer safety. Conventional deterrent techniques frequently prove inefficient, require excessive manual effort, or risk harming wildlife and ecosystems. This study introduces an intelligent system combining IoT and artificial intelligence (AI) to detect wildlife in real time and protect crops sustainably. The proposed framework employs a camera module for continuous field surveillance, utilizing a Convolutional Neural Network (CNN) powered by the YOLO algorithm to identify animals. Onc

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Bais, Kunalsingh. "Camouflaged Object Detection System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40691.

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The detection of camouflaged objects is crucial for applications in surveillance, wildlife monitoring, and military scenarios, where objects blend seamlessly into their surroundings. This review consolidates 15 influential research studies covering advancements in datasets, models, real-time detection technologies, and multimodal approaches. The focus is on implementing YOLOv8, a state-of-the-art real-time object detection model, using the ACD1K dataset, which is specifically designed for military surveillance. By synthesizing methodologies, evaluation metrics, and applications, this paper hig

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Rahman, Md Auhidur, Stefano Giordano, and Michele Pagano. "Smart Wildlife Monitoring: Real-Time Hybrid Tracking Using Kalman Filter and Local Binary Similarity Matching on Edge Network." Computers 14, no. 8 (2025): 307. https://doi.org/10.3390/computers14080307.

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Real-time wildlife monitoring on edge devices poses significant challenges due to limited power, constrained bandwidth, and unreliable connectivity, especially in remote natural habitats. Conventional object detection systems often transmit redundant data of the same animals detected across multiple consecutive frames as a part of a single event, resulting in increased power consumption and inefficient bandwidth usage. Furthermore, maintaining consistent animal identities in the wild is difficult due to occlusions, variable lighting, and complex environments. In this study, we propose a lightw

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Sannier, C. A. D., J. C. Taylor, W. Du Plessis, and K. Campbell. "Real-time vegetation monitoring with NOAA-AVHRR in Southern Africa for wildlife management and food security assessment." International Journal of Remote Sensing 19, no. 4 (1998): 621–39. http://dx.doi.org/10.1080/014311698215892.

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Sachi Devi. P, Dr, Dr M. Guru Mohan Reddy, and Kum B.Mahima. "Application of AI in wildlife monitoring & Conservation - A comprehensive review." International Journal of Advanced Research in Biological Sciences (IJARBS) 12, no. 2 (2025): 64–74. https://doi.org/10.22192/ijarbs.2025.12.02.007.

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Artificial Intelligence (AI) has emerged as a transformative tool in the field of wildlife monitoring and conservation, revolutionizing the way we understand, protect, and manage biodiversity. AI technologies, such as machine learning algorithms and computer vision, are being increasingly utilized to monitor wildlife populations. These systems can analyze vast amounts of data from camera traps, acoustic recordings, and satellite imagery with remarkable accuracy and speed. By automating the identification and classification of species, AI helps researchers track population trends,detect illegal

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Tonacci, Alessandro, Francesco Sansone, Raffaele Conte, and Claudio Domenici. "Use of Electronic Noses in Seawater Quality Monitoring: A Systematic Review." Biosensors 8, no. 4 (2018): 115. http://dx.doi.org/10.3390/bios8040115.

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Electronic nose (eNose) systems are particularly appreciated for their portability, usability, relative low cost, and real-time or near real-time response. Their application finds space in several domains, including environmental monitoring. Within this field, marine monitoring is of particular scientific relevance due to the fragility of this specific environment, daily threatened by human activities that can potentially bring to catastrophic and irreversible consequences on marine wildlife. Under such considerations, a systematic review, complying with the PRISMA guidelines, was conducted co

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RAJKUMAR, D. V. "Agroguard – Crop Protection Using AI to Detect and Deter Animals." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45379.

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Wildlife monitoring and conservation require accurate and efficient detection of animals in real time. Traditional methods like manual tracking or using motion-triggered cameras often fall short due to low accuracy and lack of immediate response. In this project, we present an intelligent animal detection system that utilizes computer vision and deep learning with the YOLO (You Only Look Once) algorithm to identify various animal species in real time through a camera feed. The system is integrated with an Arduino via serial communication to enable physical alerts or actions based on the detect

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P, Geethanjali. "Advances in Ecological Surveillance: Real-Time Wildlife Detection using MobileNet-SSD V2 Convolutional Neural Network." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 2333–45. http://dx.doi.org/10.22214/ijraset.2023.57847.

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Abstract: Human-wildlife conflicts have escalated due to increased encroachment on natural habitats, necessitating advanced surveillance to mitigate potential threats and preserve biodiversity. Traditional monitoring methods are labor-intensive and inefficient when dealing with the volume and velocity of data generated by camera traps. This research introduces an innovative, automated wildlife detection system utilizing a Convolutional Neural Network (CNN) - the MobileNet-SSD V2 - to process images for real-time animal detection. The paper elaborates on a comprehensive methodology, from datase

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Prakhar Mittal and Rahul Malik. "Optimized Physics-Informed Neural Network Framework for Wild Animal Activity Detection and Classification with Real Time Alert Message Generation." International Journal on Computational Modelling Applications 2, no. 1 (2025): 42–52. https://doi.org/10.63503/j.ijcma.2025.50.

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The growing contact between wild animals and humans has forced the creation of intelligent systems capable of monitoring, detecting, and classifying animal behaviors. This research describes a unique technique for wild animal activity detection and categorization that employs optimized Physics-Informed Neural Networks (PINNs) designed to provide real-time alarm signals. By incorporating domain-specific physical models into neural network training, the proposed method outperforms standard strategies in terms of accuracy and resilience. This article describes the model's design, optimization, an

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Chodavarapu, Sumana. "Wildlife Bird Migration Forecasting and Analysis: A Hybrid AI-Embedded Paradigm for Real-Time Ecological Surveillance." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 7390–91. https://doi.org/10.22214/ijraset.2025.71783.

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Migratory birds are crucial health indicators of the environment, but monitoring their patterns has grown more challenging with fast-paced environmental changes. This thesis suggests an intelligent, low-cost system combining embedded electronics and machine learning to provide real-time bird migration analysis. The design revolves around the LPC2148 ARM7 microcontroller with GPS, temperature, and accelerometer sensors integrated into it. Designed on Proteus simulation, the circuit provides energy-aware and scalable field deployment. The natural migration pattern is simulated using Fibonacci an

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Balaji, J. "Real Time Monitoring of Forest Fire Detection and Object Detection Using Node MCU." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 7174–84. https://doi.org/10.22214/ijraset.2025.70091.

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Forest fires pose a severe threat to the ecosystems, wildlife, and human settlements, causing widespread environmental damage through their system and loss of biodiversity. Rapid detection and accurate prediction of fire spread are crucial for effective wildfire management and mitigation. This project aims to develop the function of a real-time forest fire monitoring and detection and prediction system that leverages the Internet of Things (IoT) and machine learning (ML) to enhance early fire detection, prediction, and response mechanisms. The system is built around a Node MCU microcontroller,

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Kaur, Harkiran, and Sandeep K. Sood. "A Smart Disaster Management Framework For Wildfire Detection and Prediction." Computer Journal 63, no. 11 (2020): 1644–57. http://dx.doi.org/10.1093/comjnl/bxz091.

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Abstract Wildfires are exorbitantly cataclysmic disasters that lead to the destruction of forest cover, wildlife, land resources, human assets, reduced soil fertility and global warming. Every year wildfires wreck havoc across the globe. Therefore, there is a need of an efficient and reliable system for real-time wildfire monitoring to dilute their disastrous effects. Internet of Things (IoT) has demonstrated remarkable evolution and has been successfully adopted in environmental monitoring domain. Therefore, timely detection and prediction of wildfires is the need of the hour. The proliferati

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Shinde, Aishwarya, Bharavi Patil, Hardik Vinde, and Hrithik Mavarkar. "Animal Detector System for Forest Monitoring using OpenCV and Raspberry-pi." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 2934–36. http://dx.doi.org/10.22214/ijraset.2023.50795.

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Abstract: The presented prototype system offers a solution for tracking animals, assets, or people using only Global System for Mobile Communications (GSM) services. The development and application of living terrain by humans have led to harm to wild creatures, making it crucial to explore animal monitoring system technology. This project utilizes a GSM modem to send SMS notifications to the designated person, allowing for real-time tracking of the target. Such a system could have great significance in protecting and preserving wildlife in the face of rapid population and financial growth

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Krishna Priya, Dr C. "Smart Crop Protection System." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04064.

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Abstract—Agriterrorism in the context of animal damage significantly impacts crop production for farmers, leading to economic losses. This paper proposes an AI-driven Scarecrow system using real-time video processing with YOLOv3 and OpenCV to detect and deter wildlife intrusions. Upon detection, the system generates sound alerts to ward off animals and notifies the farmer via email and phone calls if threats persist. The proposed system aims to provide a scalable, cost-effective, and environmentally friendly crop protection method, addressing the limitations of traditional systems. Index Terms

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Povlsen, Peter, Dan Bruhn, Petar Durdevic, Daniel Ortiz Arroyo, and Cino Pertoldi. "Using YOLO Object Detection to Identify Hare and Roe Deer in Thermal Aerial Video Footage—Possible Future Applications in Real-Time Automatic Drone Surveillance and Wildlife Monitoring." Drones 8, no. 1 (2023): 2. http://dx.doi.org/10.3390/drones8010002.

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Wildlife monitoring can be time-consuming and expensive, but the fast-developing technologies of uncrewed aerial vehicles, sensors, and machine learning pave the way for automated monitoring. In this study, we trained YOLOv5 neural networks to detect points of interest, hare (Lepus europaeus), and roe deer (Capreolus capreolus) in thermal aerial footage and proposed a method to manually assess the parameter mean average precision (mAP) compared to the number of actual false positive and false negative detections in a subsample. This showed that a mAP close to 1 for a trained model does not nec

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Castillo Giraudo, Matías, María Marcela Orozco, Marcelo Juan Zabalza, et al. "First Report of Paralytic Rabies in a Lowland Tapir (Tapirus terrestris) in Argentina." Viruses 17, no. 4 (2025): 570. https://doi.org/10.3390/v17040570.

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As a significant zoonotic disease, rabies poses substantial economic challenges for the livestock sector, highlighting the need for effective wildlife monitoring as part of a One Health approach. This study documents the first case of paralytic rabies in a lowland tapir (Tapirus terrestris) at the Guaycolec Wildlife Station in Formosa, Argentina. The 12-year-old male tapir exhibited neurological symptoms, including limb paralysis and dysphagia, leading to its death. The rabies virus was confirmed through direct immunofluorescence, virus isolation in BHK-21 cells, and molecular diagnostics via

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Chalmers, Carl, Paul Fergus, Serge Wich, et al. "AI-Driven Real-Time Monitoring of Ground-Nesting Birds: A Case Study on Curlew Detection Using YOLOv10." Remote Sensing 17, no. 5 (2025): 769. https://doi.org/10.3390/rs17050769.

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Effective monitoring of wildlife is critical for assessing biodiversity and ecosystem health as declines in key species often signal significant environmental changes. Birds, particularly ground-nesting species, serve as important ecological indicators due to their sensitivity to environmental pressures. Camera traps have become indispensable tools for monitoring nesting bird populations, enabling data collection across diverse habitats. However, the manual processing and analysis of such data are resource-intensive, often delaying the delivery of actionable conservation insights. This study p

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