Comparative Study of Conventional and Satellite Based Agriculture Information System
The purpose of this study is to compare the conventional crop monitoring system with the satellite based crop monitoring system in Pakistan. This study is conducted for SUPARCO (Space and Upper Atmosphere Research Commission). The study focused on the wheat crop, as it is the main cash crop of Pakistan and province of Punjab. This study will answer the following: Which system is better in terms of cost, time and man power? The man power calculated for Punjab CRS is: 1,418 personnel and for SUPARCO: 26 personnel. The total cost calculated for SUPARCO is almost 13.35 million and CRS is 47.705 million. The man hours calculated for CRS (Crop Reporting Service) are 1,543,200 hrs (136 days) and man hours for SUPARCO are 8, 320hrs (40 days). It means that SUPARCO workers finish their work 96 days earlier than CRS workers. The results show that the satellite based crop monitoring system is efficient in terms of manpower, cost and time as compared to the conventional system, and also generates early crop forecasts and estimations. The research instruments used included: Interviews, physical visits, group discussions, questionnaires, study of reports and work flows. A total of 93 employees were selected using Yamane’s formula for data collection, which is done with the help questionnaires and interviews. Comparative graphing is used for the analysis of data to formulate the results of the research. The research findings also demonstrate that although conventional methods have a strong impact still in Pakistan (for crop monitoring) but it is the time to bring a change through technology, so that our agriculture will also be developed along modern lines.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1129113Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 889
 Anup K. Prasad, C. Lim. P. Ramesh. A. Singh. And K. Menas “Crop yield estimation model for Iowa using remote sensing and surface parameters”, 2006.
 Anup K. Prasad a, Lim Chai b, Ramesh P. Singh a, b,*, Menas Kafatos b “Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geo information” 8 (2006) 26–33.
 Dr. Muhammad Hanif “Satellite based crop monitoring system in Pakistan”.
 Punjab CRS: Base Line Survey “Agriculture Information System building Provincial Capacity for Crop Forecasting and Estimation”, 2012.
 Satellite based crop monitoring system in Pakistan, SUPARCO (Volume 1, June 2006).
 Satellite based crop monitoring system in Pakistan, SUPARCO (Volume II, August 2007).
 Satellite based crop monitoring system in Pakistan, SUPARCO (Volume III, August 2008).
 Reynolds, M. Yittayew, D. C and Slack, “Estimation crop yields and production by integrating the FAO Crop Speci. C Water Balance model with real-time satellite data and ground-based ancillary data”. International Journal of remote sensing, vol, 21, no.18, 3487-3508, 2000.
 McNairn, H., Ellis, J., Van Der Sanden, J. J., Hirose, T., and Brown, R. “Providing crop information using RADARSAT-1 and satellite optical imagery”, International Journal of Remote Sensing, 23(5):851-870, 2002.
 Shafian. S. and M. Valadanzouj (2007) “Assessment of Crop Yield Estimation Methods by Using Satellite Images and Ground Observation”, GIS development, Map Asia.
 Wu, B. (2004) China Crop Watch System with Remote Sensing. Journal of Remote Sensing, 8 (6): 481-497.
 Felix Rembold, Jacques Delincé, Hendrik Boogard, Armin Burger (2006) “Spatial Information Systems in Crop Monitoring: Developing New Global Models and Sharing the data”.
 Meng Ji-hua, Wu Bing-fang, Li Qiang-zi, Zhang Lei. An Operational Crop Growth Monitoring System by Remote Sensing. High Technology Letters, 2007. 17 (1) 94-99.
 Wu Bingfng and Liu Chenglin, “An Operational Crop Growth Monitoring System with Coupling of AVHRR and VGT Data by High Technology Letters”, 2007.
 Zhang Feng, Wu Bingfang, “A Method for Extract Regional Crop Growth Information with Time Series of NDVI Data”. Journal of Remote Sensing, 2004.