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Scientific African ; 19(130), 2023.
Article in English | CAB Abstracts | ID: covidwho-2318053


Due to the world's rapid population expansion, the demand for food is anticipated to increase significantly during the coming decade. Traditional farming practices cannot meet the need for the food crop. Conventional farming methods use resources like land, water, herbicides, and fertilisers rather inefficiently. When it comes to making the most effective and sustainable use of resources to increase production, automation in agriculture is garnering a lot of interest. How people and machines operate on farms has been changed by integrating the Internet of Things (IoT) with numerous sensors, controllers, and communication protocols. A comprehensive literature review of the key technologies involved in smart and sustainable agriculture, viz. various sensors, controllers, communication standards, IoT based intelligent machinery, were compared and presented. These sensors were continuously producing a significant quantity of data on the agricultural field. These data were transmitted to the central control unit for analysis to meet the demands for water, fertiliser, pesticides, etc. The architecture and importance of data analytics in agriculture IoT, case studies of current agricultural automation utilising IoT, key challenges and open issues in agriculture IoT technology were discussed. The findings provide support for the selection of IoT technologies for specific applications.

3rd International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2021 ; 235:333-346, 2022.
Article in English | Scopus | ID: covidwho-1437222


The coronavirus disease 2019 (COVID-19) has appeared in December 2019 at Wuhan city, China. The virus started spreading over the world. Most of the governments have taken different measures to prevent the outbreak. Social distancing (SD) is one of the effective solutions to prevent the spread of COVID-19, in which people should maintain a specific distance between each other. This paper aims to provide a YOLOv4-based model for monitoring social distancing. The model begins by taking a video/picture as input and generating warnings of SD violation. The YOLOv4 we used in this model detects pedestrian’s people in public places such as streets, malls, train stations, and universities based on deep learning techniques. The model uses a predefined SD threshold (SDTH) and a violation index (VI) to determine when the violation occurs and trigger a warning sub-system to make an awareness action immediately. A comprehensive investigation and discussion on the existing literature of SD, object detection methods, and SD monitoring have also been provided in this paper. The model provided is supposed to operate continuously in the targeted places to monitor people, thus reducing the impact of COVID-19 spread. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.