2.5. Related Work
The study conducted by 35 proposed architecture of a farm management system using features of internet which concentrated on technique of farming and devices to exchange the information among stakeholders. Further, this study describes the method for better management of only some of the tasks of farmers. Ranya et al. 36 suggest ALSE (Agriculture Land Suitability Evaluator) for describing different types of land, to discover the suitable land for different kinds of crops by examining geo-environmental issues. ALSE used GIS (Global Information System) abilities to estimate land using local environmental conditions through the digital map and depending on this evidence decision can be made. Raimo et al. 37 present FMIS (Farm Management Information System) that used to discovery the precision agriculture necessities for information systems over web-based approach. The author recognized GIS as a key constraint for data management in precision agriculture. Similarly, FMIS is studied by Sorensen et al. 38, that examine flexible necessities of farmers to accomplish decision methods and their equivalent needs. Additional, the study described the identification of development used for original examination of user wants is obligatory for the actual design of FMIS.
The study conducted by 39 present WASS (Web-based Agricultural Support System) which recognized information, collaborative work, decision support and features of WASS. Depend on features, authors divided WASS into production, research-education, and management. Reddy at al. 40 present GIS-based DSS (Decision Support System) framework in which DSS has been designed for watershed management and management of crop productivity regional and farm level. GIS is used to collect and analyze the graphical images for creation of new rules and decisions for effective management of data. Shitala et al. 41 presented a mobile computing-based framework for agriculturists called AgroMobile for farming, marketing, and examination of crop images. Additional, AgroMobile is identified the crop disease depend on image processing and also explain how dynamic requirements of user disturbs the performance of the system. Seokkyun et al. 42 study on cloud-based Disease Forecasting and Livestock Monitoring System (DFLMS) in which information collected and manages by using the sensor. DFLMS makes available an effective interface for the user, but because of temporary storage method used, it is incompetent to store and retrieve data in databases for future use.
Renaud et al. 43 presented cloud-based weather forecasting system which weather-related data collection and examine replication to categorize the farming requirements of different seasons and the system decreases data repetition and guarantees load balancing for management of resources. The study conducted at the majority of farmers in Kenya who are not able to sell their produce at market price because of inappropriate information available. Additionally, the agricultural productivity is being decreased due to the deficiency of information and resistance developed by the agricultural universities. For such farmers to produce and sell their products at market-based competitive prices, information communication technologies (ICT) tools have been availed to them. This is because the development of agriculture is dependent on how fast and relevant information is provided to the end users 44
From these existing systems which have been highlighted and analyzed, it has been found out that all these systems are good but none of them offer the integration and the combination of the services in order to help user get full information about agricultural service. It is necessary to enhance these systems and avoid their limitation in the proposed framework be more useful. In general, our proposed framework differs from other work by cloud deployment model used, service delivery model used, the inclusion domain refuse to use our proposed framework, the prototype developed to show the integrated of the framework.
2.5. Related Work