Agricultural Data Visualization for Prescriptive Crop Planning

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2017 by IJCTT Journal
Volume-49 Number-3
Year of Publication : 2017
Authors : Harshitha B P, Amith R, Abhishek S, Rohit Vibhu C
DOI :  10.14445/22312803/IJCTT-V49P129

MLA

Harshitha B P, Amith R, Abhishek S, Rohit Vibhu C "Agricultural Data Visualization for Prescriptive Crop Planning". International Journal of Computer Trends and Technology (IJCTT) V49(3):183-188, July 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
The importance of carrying out effective and sustainable agriculture is getting more and more obvious. Modern computers and the internet have made it much easier to make graphics out of tabular data and give these graphics the qualities of animation and interactivity through data visualization. Investigate effects of climate change on crop yields over time, climate change effects on crop yield eclipsed by other developments (technology, crop mix change), help make informed decisions for sustainable growth. Agriculture yield data is used to analyze and improve the crop yield and represent in the form of a Graphs through data visualization technique. The visualization methods presented include interactive charts to enable our data users to drill down and focus on more detailed views of these data displays. Each of these methods facilitates the display of large volumes of data and allows data users to extract information from our statistics that is difficult or impossible to obtain from traditional static charts or tabular displays of data.

References
[1] NASS Research, Science and Technology website. (http://www.nass.usda.gov/Research_and_Science/)
[2] Timo Honkela, Samuel Kaski, Krista Lagus, and Teuvo Kohonen. WEBSOM—self- organizing maps of document collections. In Proceedings of WSOM’97, Workshop on Self- Organizing Maps, Espoo, Finland, June 4-6, pages 310–315. Helsinki University of Technol- ogy, Neural Networks Research Centre, Espoo, Finland, 1997. 2.
[3] T. Jensen, A. Apan, F. Young, and L. Zeller. Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform. Comput. Electron. Agric., 59(1-2):66– 77, 2007.
[4] Timo Honkela, Samuel Kaski, Krista Lagus, and Teuvo Kohonen. WEBSOM—selforganizing maps of document collections. In Proceedings of WSOM’97, Workshop on SelfOrganizing Maps, Espoo, Finland, June 4-6, pages 310–315. Helsinki University of Technology, Neural Networks Research Centre, Espoo, Finland, 1997.
[5] M. Schneider and P. Wagner. Prerequisites for the adoption of new technologies - the example of precision agriculture. In Agricultural Engineering for a Better World, Dusseldorf, 2006. ¨ VDI Verlag GmbH.
[6] Lindquist, Evert. ?Grappling with Complex Policy Challenges: Exploring the Potential of
[7] Visualization for Analysis, Advertising and Engagement?, Discussion Paper for HC Coombs Policy Forum Round tables, (2011).
[8] Jacqueline Strecker, Research Award Recipient IDRC; & Tricia Wind, Evaluation Officer IDRC. Report author: Jacqueline Strecker Feature review by: Amanda Cox Winter 2012
[9] Tufte, Edward R., Envisioning Information (Chesire, Conn.: Graphics Press, 1990).
[10] Iliiinsky, Noah, ?On Beauty, Ch. 1 in Steele and Iliinsky (eds.), Beautiful Visualization.
[11] Cambridge: O’Reilly, 2010, pp.1-13.

Keywords
Data Visualisation, Predictive Crop yield.