USE OF CLOUD COMPUTING IN SMART FARMING
DOI:
https://doi.org/10.29121/shodhkosh.v3.i2.2022.5272Keywords:
Cloud Computing, Smart Farming, Weather, CropAbstract [English]
In case of agriculture our country has a great potential of development. Broadly speaking cloud computing is nothing but a highly ‘utilitarian’ orientation of IT services where users benefited on a pay-as-you go basis. In smart farming all agricultural data such as soil, weather, research, crop, farmers, agriculture marketing, fertilizer, and pesticide information may now be consolidated in the cloud. According to an analysis performed by McKinsey, if agricultural connectivity is successfully implemented, the industry might add $500 billion to the global gross domestic product by 2030. With close to 7.8 billion people inhabiting this earth and enjoying tasty food, it’s inevitable to consider ways to meet the rising demand. In fact, estimates show the demand for food is expected to increase anywhere between 59% to 98% by 2050, but unfortunately, places to farm are scarce. As a result, agriculture need to be smarter about finding innovative ways to get more out of each piece of land. In modern era of cloud computing technology very helpful for centralized the all agricultural related data bank (Soil-related, weather, Research, Crop, Farmers, Agriculture marketing, fertilizers and pesticide information) in the cloud.
References
Philip, A., &Santhosh, L. (2022). The Importance of Cloud Computing in Agriculture. In National Conference on Emerging Computer Applications (Vol. 4, No. 1).
Rekha, M. V., &Shankari, S. U. (2022). The Impact Of Cloud Computing & ICT In Agricultural Productivity. Journal of Pharmaceutical Negative Results, 5598-5608.
https:// The Importance of Cloud Applications in Agriculture - Nutanix Cloud Farm Management Software (nutanix.com)I. S. Jacobs, and C. Magnetism, volume P. Bean, "Fine particles, thin films, and exchange anisotropy." G. T. Rado III, Henry V. Eds. Suhl Pages 271-350 in the 1963 edition published by Academic in New York.
“Smart farming: the transformative potential of data-driven agriculture” (link resides outside ibm.com), ISO.
Darwin, B., Dharmaraj, P., Prince, S., Popescu, D. E., &Hemanth, D. J. (2021). Recognition of bloom/yield in crop images using deep learning models for smart agriculture: A review. Agronomy, 11(4), 646. DOI: https://doi.org/10.3390/agronomy11040646
Mohamed, E. S., Belal, A. A., Abd-Elmabod, S. K., El-Shirbeny, M. A., Gad, A., &Zahran, M. B. (2021). Smart farming for improving agricultural management. The Egyptian Journal of Remote Sensing and Space Science, 24(3), 971-981. DOI: https://doi.org/10.1016/j.ejrs.2021.08.007
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Dr. Harjinder Singh

This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.