IoT System for Forest Monitoring

Marcu, Alina-Elena; Suciu, George; Olteanu, Elena; Miu, Delia; Drosu, Alexandru; Marcu, Ioana

doi:10.1109/tsp.2019.8768835

Cited by 27 publications

(15 citation statements)

References 12 publications

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“…They demonstrated the advantages of transformers for fire detection in a variety of scenarios, including a variety of input sizes and backbone models that comprised either pure or hybrid transformers, and the experimental results were obtained using Dice loss as the loss function and the CorsicanFire dataset for training and evaluation. In addition, IoT-based fire detection methods are also becoming a more active and attractive research area for scholars [45][46][47].…”

Section: Fire Detection and Segmentation Methods Based On Transformersmentioning

confidence: 99%

Fire Detection Method in Smart City Environments Using a Deep-Learning-Based Approach

et al. 2021

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In the construction of new smart cities, traditional fire-detection systems can be replaced with vision-based systems to establish fire safety in society using emerging technologies, such as digital cameras, computer vision, artificial intelligence, and deep learning. In this study, we developed a fire detector that accurately detects even small sparks and sounds an alarm within 8 s of a fire outbreak. A novel convolutional neural network was developed to detect fire regions using an enhanced You Only Look Once (YOLO) v4network. Based on the improved YOLOv4 algorithm, we adapted the network to operate on the Banana Pi M3 board using only three layers. Initially, we examined the originalYOLOv4 approach to determine the accuracy of predictions of candidate fire regions. However, the anticipated results were not observed after several experiments involving this approach to detect fire accidents. We improved the traditional YOLOv4 network by increasing the size of the training dataset based on data augmentation techniques for the real-time monitoring of fire disasters. By modifying the network structure through automatic color augmentation, reducing parameters, etc., the proposed method successfully detected and notified the incidence of disastrous fires with a high speed and accuracy in different weather environments—sunny or cloudy, day or night. Experimental results revealed that the proposed method can be used successfully for the protection of smart cities and in monitoring fires in urban areas. Finally, we compared the performance of our method with that of recently reported fire-detection approaches employing widely used performance matrices to test the fire classification results achieved.

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Section: Fire Detection and Segmentation Methods Based On Transformersmentioning

confidence: 99%

Fire Detection Method in Smart City Environments Using a Deep-Learning-Based Approach

et al. 2021

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“…For example, the SeaForest solution is a system for detecting fire, pollution sources, or illegal deforestation based on IoT devices. [21,22] revises the available systems for forest fire detection applications and proposes a novel method based on multi-sensors and cameras. On the other hand, such systems can be deployed to monitor the environment but not multiple parameters related to tree ecophysiology.…”

Section: The Treetalkermentioning

confidence: 99%

Toward a Unified TreeTalker Data Curation Process

Tomelleri

Marchesini

Yaroslavtsev

et al. 2022

Forests

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The Internet of Things (IoT) development is revolutionizing environmental monitoring and research in macroecology. This technology allows for the deployment of sizeable diffuse sensing networks capable of continuous monitoring. Because of this property, the data collected from IoT networks can provide a testbed for scientific hypotheses across large spatial and temporal scales. Nevertheless, data curation is a necessary step to make large and heterogeneous datasets exploitable for synthesis analyses. This process includes data retrieval, quality assurance, standardized formatting, storage, and documentation. TreeTalkers are an excellent example of IoT applied to ecology. These are smart devices for synchronously measuring trees’ physiological and environmental parameters. A set of devices can be organized in a mesh and permit data collection from a single tree to plot or transect scale. The deployment of such devices over large-scale networks needs a standardized approach for data curation. For this reason, we developed a unified processing workflow according to the user manual. In this paper, we first introduce the concept of a unified TreeTalker data curation process. The idea was formalized into an R-package, and it is freely available as open software. Secondly, we present the different functions available in “ttalkR”, and, lastly, we illustrate the application with a demonstration dataset. With such a unified processing approach, we propose a necessary data curation step to establish a new environmental cyberinfrastructure and allow for synthesis activities across environmental monitoring networks. Our data curation concept is the first step for supporting the TreeTalker data life cycle by improving accessibility and thus creating unprecedented opportunities for TreeTalker-based macroecological analyses.

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“…A choice tree is a treeshaped outline that is used to choose an activity's route. Each branch of the tree corresponds to a probable option activity or event [6]. Assume there are three organic items in a dish, one each of cherry, orange, and apple.…”

Section: Random Forest Algorithmmentioning

confidence: 99%

Prior Bush Fire Identification Mechanism based on Machine Learning Algorithms

Atheeq¹,

Mohammad²,

Mohammed³

2022

IJAIA

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Besides causing awful fatalities resulting in deaths and significant resources like many acres of timberland and dwelling places, forest fires are a significant threat to sound enormous wilderness biologically and environmentally. Consistently, a considerable number of fires around the globe reason debacles to different habitats and layouts. The stated matter has been the investigation premium for a significant length of time; there is a considerable amount of good concentrated on arrangements available for testing or perhaps ready to be utilized to determine this disadvantage. Woods and actual flames have been severe issues for quite some time. Presently, there is a wide range of answers for distinguishing woods fires. Individuals are utilizing sensors to determine the fire. However, this case isn't workable for vast sections of land woods. This paper discusses another fire-recognition methodology with incremental advancements. Specifically, we put forward a stage-Artificial Intelligence. The PC innovation strategies for acknowledgment and whereabouts of smog and fires, in light of the inert photographs or the graphics captured by the cameras. AI for tracing down the fires. The accuracy relies on the calculations that use dataset values later divided in various test and train sets, respectively.

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IoT System for Forest Monitoring

Cited by 27 publications

References 12 publications

Fire Detection Method in Smart City Environments Using a Deep-Learning-Based Approach

Fire Detection Method in Smart City Environments Using a Deep-Learning-Based Approach

Toward a Unified TreeTalker Data Curation Process

Prior Bush Fire Identification Mechanism based on Machine Learning Algorithms

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