Affiliation:
1. East West University, Bangladesh
2. NII, Japan
Abstract
Agriculture activity monitoring needs to deal with large amount of data originated from various organizations (weather station, agriculture repositories, field management, farm management, universities, etc.) and mass people. Therefore, a scalable environment with flexible information access, easy communication and real time collaboration from all types of computing devices, including mobile handheld devices as smart phones, PDAs and iPads, Geo-sensor devices, and etc. are essential. It is mandatory that the system must be accessible, scalable, and transparent from location, migration and resources. In addition, the framework should support modern information retrieval and management systems, unstructured information to structured information processing (IBM Info Stream, text analytic, pig & hive, etc.), task prioritization, task distribution (Hadoop), workflow and task scheduling system, processing power and data storage (Amazon S3 and Google BigTable). Thus, High Scalability Computing (HSC) or Cloud based system can be a prominent and convincing solution for this circumstance.
Reference28 articles.
1. GRASS GIS on High Performance Computing with MPI, OpenMP and Ninf-G Programming Framework;S.Akhter;Proceeding of ISPRS 2010,2010
2. Porting a GRASS raster module to distributed computing. Examples for MPI and Ninf-G;S.Akhter;OSGeo Journal,2007
3. Distributed Pixel Method to speed-up the RS data assimilation of SWAP model;S.Akhter;Proceedings of the MapAsia conference,2005
4. Exploring Strategies for Parallel Computing of RS Data Assimilation with SWAP-GA
5. Akhter, S., Jangjaimon, I., Chemin, Y., Uthayopas, P., & Honda, K. (2006). Development of a GRIDRPC tool for Satellite Images Parallel Data Assimilation in Agricultural Monitoring. International Journal of Geoinformatics, 2(3).