Implementation of Intelligent Plantation System Using Virtual IoT

Sindhwani, Nidhi; Maurya, V. P.; Patel, Amit; Yadav, Roopesh Kumar; Krishna, S.A.; Anand, Rohit

doi:10.1007/978-3-030-77528-5_16

Cited by 35 publications

(2 citation statements)

References 27 publications

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“…Physical distancing, repeated hand sanitizing, and face masking have proven to be quite efficient to control the spreading of the virus, but everyone is not obeying the guidelines. Various technologies like machine learning (ML) algorithms [ 1 ], artificial intelligence (AI) approaches [ 1 ], Internet of things (IoT) [ 2 – 5 ], and unmanned aerial vehicles (UAV) [ 6 ] give a real-time scenario at any given point about (i) the number of people following physical distancing and (ii) whether people are wearing masks or not.…”

Section: Introductionmentioning

confidence: 99%

Mask Detection and Social Distance Identification Using Internet of Things and Faster R-CNN Algorithm

Meivel¹,

Sindhwani

Anand³

et al. 2022

Computational Intelligence and Neuroscience

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The drones can be used to detect a group of people who are unmasked and do not maintain social distance. In this paper, a deep learning-enabled drone is designed for mask detection and social distance monitoring. A drone is one of the unmanned systems that can be automated. This system mainly focuses on Industrial Internet of Things (IIoT) monitoring using Raspberry Pi 4. This drone automation system sends alerts to the people via speaker for maintaining the social distance. This system captures images and detects unmasked persons using faster regions with convolutional neural network (faster R-CNN) model. When the system detects unmasked persons, it sends their details to respective authorities and the nearest police station. The built model covers the majority of face detection using different benchmark datasets. OpenCV camera utilizes 24/7 service reports on a daily basis using Raspberry Pi 4 and a faster R-CNN algorithm.

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Section: Introductionmentioning

confidence: 99%

Mask Detection and Social Distance Identification Using Internet of Things and Faster R-CNN Algorithm

Meivel¹,

Sindhwani

Anand³

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

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“…The data from the sensors is received by the microcontroller, which then processes it. A mobile application generates an alert message based on data from a microcontroller that has been uploaded to a cloud platform and verified by an algorithm running on the cloud platform it-self [2]. Using this architecture, manual work is reduced, which means that a separate person is no longer required to monitor the patient continuously.…”

Section: Introductionmentioning

confidence: 99%

Wearable Smart Jacket for Coal Miners Using IoT

Ananth

Revathi

Poonguzhali³

et al. 2022

2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)

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Smart wearables are redefining the way people move and behave in real-time. Workers will be alerted to the presence of toxic gases as well as be tracked in the event of an accident if this system is implemented. Additionally, the instrument has sensors for methane and carbon monoxide gases included in its design. The prototype can detect gas in the air, the rate of the miner's breathing, the change in temperature and humidity, and the miner's location at all times. Wi-Fi will be used to transmit all of these parameters to a dynamic internet protocol. Every one of them will be able to make it through the shield. This way, all mineworkers can be monitored, and if something goes wrong, the miner can be rescued as quickly as possible. Using a pulse sensor on the miner's body, the base camp can track the miner's GPS location. It may be necessary to dig a coal mine as soon as possible to save the most people in a disaster. With the help of IoT, we can build a database and, if necessary, communicate with a nearby hospital. Our final consideration will look at market trends and challenges for WHDs to keep in mind.

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