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IoT for Health Ecosystem

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dc.contributor.author Modha, Hiren Jayantilal
dc.contributor.author Dr. Ashish M, Kothari
dc.date.accessioned 2024-01-25T10:36:14Z
dc.date.available 2024-01-25T10:36:14Z
dc.date.issued 2023-01-13
dc.identifier.uri http://10.9.150.37:8080/dspace//handle/atmiyauni/1289
dc.description.abstract The primary goals of this research are to improve agricultural productivity and make the ecosystem newlinesparkle. The remote sensing-based approach is used to improve the quick feedback newlinesystem for decision support. Different Machine Learning and Deep Learning techniques can newlinebe used to provide subsequent executions enough time. Due to the fact that climate variables newlinevary from region to region on earth, care should be made to avoid generalising the established newlinemodel to the entire globe. In order for everyone to use the system, the cost is also considered. newlineWith various prediction approaches, it should be possible to monitor various agricultural newlinediseases at an early stage and treat them as needed to save the crop. The many phases of crop newlinedevelopment have been recognised, and it should be possible to inject more neutrinos during newlinespecific times to boost production. In order to avoid crops from being destroyed by a lack of newlinemoisture in the farm, the damaging effects of drought will be eliminated by ongoing soil newlinemoisture monitoring. Additionally, it is feasible to conserve water and improve the health of newlinethe environment. en_US
dc.language.iso en en_US
dc.subject Agriculture en_US
dc.subject Deep Learning en_US
dc.subject Ecosystem Health en_US
dc.subject Engineering en_US
dc.subject Engineering and Technology en_US
dc.subject Engineering Electrical and Electronic en_US
dc.subject Machine Learning en_US
dc.title IoT for Health Ecosystem en_US
dc.type Thesis en_US


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