PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Agricultural IOT, Cloud Computing and Mobile Based Application Integration for the Total Discharge Predication of Two Farms Smart Agricultural System to Avoid Flooding
Version 1
: Received: 13 October 2023 / Approved: 16 October 2023 / Online: 17 October 2023 (05:50:21 CEST)
How to cite:
Khan, M.; Noor, S. Agricultural IOT, Cloud Computing and Mobile Based Application Integration for the Total Discharge Predication of Two Farms Smart Agricultural System to Avoid Flooding. Preprints2023, 2023101009. https://doi.org/10.20944/preprints202310.1009.v1
Khan, M.; Noor, S. Agricultural IOT, Cloud Computing and Mobile Based Application Integration for the Total Discharge Predication of Two Farms Smart Agricultural System to Avoid Flooding. Preprints 2023, 2023101009. https://doi.org/10.20944/preprints202310.1009.v1
Khan, M.; Noor, S. Agricultural IOT, Cloud Computing and Mobile Based Application Integration for the Total Discharge Predication of Two Farms Smart Agricultural System to Avoid Flooding. Preprints2023, 2023101009. https://doi.org/10.20944/preprints202310.1009.v1
APA Style
Khan, M., & Noor, S. (2023). Agricultural IOT, Cloud Computing and Mobile Based Application Integration for the Total Discharge Predication of Two Farms Smart Agricultural System to Avoid Flooding. Preprints. https://doi.org/10.20944/preprints202310.1009.v1
Chicago/Turabian Style
Khan, M. and Sanam Noor. 2023 "Agricultural IOT, Cloud Computing and Mobile Based Application Integration for the Total Discharge Predication of Two Farms Smart Agricultural System to Avoid Flooding" Preprints. https://doi.org/10.20944/preprints202310.1009.v1
Abstract
The information technology has brought in a revolution in the area of digital agriculture and hydrological modeling . With the advent of IOT and AI such as machine learning is now capable of Predicating flood forecast , drought forecast and farms based water predications. In this article various machine learning algorithms , multiple sensors for environmental and agricultural has been proposed and used . The purpose is to acquire data of soil moisture , temperature , crop stages, irrigation and precipitation on a region constitute of two farms and then performed machine learning predications for total discharge predications at farms outlets so that in case of excessive rainfall or an irrigation event the water is adjusted in the second nearby farm or reroute to a reservoir for future use to avoid flooding. The focus is mostly to work on the concept and building of an andriod -ardiuno based mobile application for the endusers (agricultural system analyst, farmers) to provide an ease. The whole system of smart agricultural based on two farms and reservoir will provide an efficient ,fully automatic, proactive and decision support system to save water waste and reuse. In future the work is also in progress for developing a desktop based application .
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
