DIGITAL TECHNOLOGY: TRANSFORMING THE AGRICULTURAL SECTOR

V. SOLO¹*, A.P. SINGH^2
1Department of Agronomy, School of Agricultural Sciences and Rural Development, Nagaland University, Medziphema, 797106, Nagaland, India
²Department of Agronomy, School of Agricultural Sciences and Rural Development, Nagaland University, Medziphema, 797106, Nagaland, India
* Corresponding Author : virosolo74@gmail.com

Received : 01-07-2020     Accepted : 28-07-2020     Published : 30-07-2020
Volume : 12     Issue : 14       Pages : 10088 - 10091
Int J Agr Sci 12.14 (2020):10088-10091

Keywords : Agriculture, Digital, Farming, Precision
Academic Editor : Dr J. R. Talaviya, Er Prabhat Kumar Dhara
Conflict of Interest : None declared
Acknowledgements/Funding : Author are thankful to Department of Agronomy, School of Agricultural Sciences and Rural Development, Nagaland University, Medziphema, 797106, Nagaland, India
Author Contribution : All authors equally contributed

India is a land of contradictions which produces about 11 percent of total global agriculture and, at the same time, is host to the world’s largest number of malnourished people. Our country perhaps has the world’s largest agricultural community consisting of 60% of the population engaged in some kind of agricultural related activity and basically the use of digital technology to integrate agricultural production from the field to the consumer. These technologies provide the agricultural industry with instruments and data to make more informed decisions and improve productivity and yield. Thus, diversification of agricultural sectors has been enhanced by livelihood opportunities, strengthened resilience and led to considerable increase in infrastructure, knowledge and preparing for the future. In this paper, a review of the technologies is provided with tools that are applicable in real-time decisions from data.

1. Kasturirangan K., Aravamudan R., Deeshatulu B. and Joseph G. (1996) Current science, 70(7).
2. Liu A. and Changyao W. (2003) Geography and Geo-information science, 19 (4),101-104.
3. UN DESA (2019) Population, surface area and density. New York, UN DESA.
4. UN DESA (2017) World Population Prospects: Key findings and advance tables. New York: UN DESA.
5. Vaghjiani K. (2018) Perspectives in the Indian Agriculture Sector. International Conference, Pattaya, Thailand.
6. World Bank (2016) World Development Report 2016: Digital Dividends. Washington, DC: World Bank
7. Ceccato P., Fernandes K., Ruiz D. and Allis E. (2014) Earth Perspectives, 1,16.
8. Hakkim A., Abhilash V, Joseph E. and Gokul A. (2016) Journal of Applied Biology & Biotechnology, 4 (06), 068-072.
9. Wagh R. (2007) International Archive of Applied Sciences and Technology, 8 (4), 04-09.
10. Lang L. (1992) GPS, GIS, remote sensing: An overview. Earth Observation Magazine, 23-26.
11. Davis G., Massey R. and Massey R. (2005) Precision agriculture: An introduction.
12. Batte T. and Buren V. (1999) Precision farming- Factor influencing productivity. In Northern Ohio Crops Day meeting, Wood County, Ohio, 21 Jan.and VGT Data, vegetation 2000, conference, Lake Maggiore- Italy
13. Burrough P.A. and McDonnell R.A. (1998) Principles of geographic information systems. Oxford University Press. Oxford, UK, pp 10-16.
14. Trimble (2005) Precision agriculture. www.trimble.com.
15. Chen F., Kissel D., Clark R, West L., Rickman D, Luval J. and Adkin W. (1997) Determining surface soil clay concentration at a field scale for precision agriculture, University of Georgia, Huntsville.
16. Bingfang W., Chenglin L. (2000) Crop Growth Monitor System with Coupling of AVHRR
17. Bingfang W. (2004) Journal of Remote Sensing, 8 (6), 482-496.
18. Rao T., Ayyangar S. and Rao N. (1982) Machine Processing of Remote Sensed Data. Symposium, 1103-1112.
19. Haiqi L. (1999) Journal of China Agricultural Resources and Regional Planning, 20(3), 55-57.