Big Data Analytics for Agricultural Analytics-Samples for Students

Question: Discuss about the Big Data Anaytics Method for Agricultural Analytics. Answer: Introduction Big Data analytics is the procedure of evaluating varied and huge sets of data, which is known as big data and is utilized to unveil concealed patterns, preferences of various customers, market trends, unknown correlations and other beneficial information. This Big Data Analytics help the organizations and the companies to make informative and important decisions of the business (Nabrzyski 2014). This is useful in many analytics such as Supply Chain Management, Operations Management, Sports Analytics, Agricultural Analytics, Fraud Detection in Banking Sector, Sentiment Analysis. This report outlines the Big Data Analytics for Agricultural Analytics. Business Intelligence or BI technologies provide historical, current and predictive views of business operations based on the analysis, extraction and collection of business data to improve decisions (Srinivasa and Bhatnagar 2012). The report covers a brief introduction on Agricultural Analytics, consumer centric product design and a recommendation system. Discussion Consumer Centric Product Design Consumer centric product is an artifact or commodity that is made by a certain organization or company for its consumers. Specific or distinct product designer of that particular organization designs this product (Woodard 2016). A product is a service or good that almost meticulously fulfills the necessities of a particular market and generates sufficient profit to rationalize its pursuing existence. A consumer centric product is a commercially dispersed good, which is the result or output of a production, fabrication or manufacturing process and passes through a distribution channel before being utilized or consumed. This type products are tangible products and can be perceived. These products are designed or made for the sake of consumers of that organization (Srinivasa and Bhatnagar 2012). Consumer centric product design in agriculture means the products that are designed for the consumers or the customers of the agricultural industry. The farmers are benefitted through this proce ss. Consumer centric product for agriculture is the Electronic Farm Records or EFR, which includes and involves the data and maps regarding the various and several data such as content of moisture in air, air pressure, and precipitation, temperature of soil, electrical conductivity, pH level and nutrient contents. All the information are needed for the agricultural field for farming (Bennett 2015). Along with the above mentioned information, all possible types of information like social media posts, tweets, blogs, articles, news feeds, insurance and yield related information and past cultivation records. Big Data This is the world of Big Data. Big data deals with the analysis, storage and collection of data for understanding the data that not known earlier. Big Data Analytics in agriculture involves the understanding of the precipitation maps, crop records, profits of the farmers, diagnosis reports with the constant analysis of streams of data about the specific agricultural area at every specific point of time (Chen, Chiang and Storey 2012).Big Data in agriculture and farming refers to the Electronic Farm Records or EFR. Big Data can store all these information easily in the Electronic Farm Records. The scientists of Big Data are trying to make it easy for the farmers in the agricultural fields (Kumar et al. 2017). Using the Big Data, the farmers can easily understand trends patterns, find out the associations and various processes to increase the productivity of the crops and their profit, improve the agricultural systems, and utilize proper diagnosis methods to mitigate or reduce the cost involved. Recommendation System Major sources of these data analytics of agriculture are the annual recreation of data in relational data base management system or RDBMS by which the previously processed reports are constructed or produced (Kumar et al. 2017). There should be an on spot analysis of data. This will help the agricultural data to be understood easily. The verification of these agricultural data are to be done in real time. The agricultural and farming systems need to evolve and innovate continuously to provide better services (Kambatla et al. 2015). Multiple types of sensors can be utilized in the association with a GPS (Global Positioning System) to generate various field maps of areas with particular soil properties.Precision agriculture can be performed by the information gained by analysis of big data. This will be a jor help for the farmers. Some of the examples are as follows: Decisive use of irrigation can be achieved by identifying soil moisture using very high resolution geographical mapsVital value that plays an important part in the production decision making can be signaled in real time by evaluation of absorbed data from various systems or sensors (Chen, Chiang and Storey 2012). Using intricate images of pest damages in field, rigorous targeting and control measures can also be taken. These big data applications can be modified, enhanced, tested rapidly and made feasible and will change the face of research and delivery in the sector of agriculture (Bennett 2015). Even though these analytics in big data provide better agricultural services it still has to overcome challenges like incompleteness of scale, privacy, data, timelines, and human collaboration and heterogeneity. The future research is on methods to get through the obstacles and use analytics of Big Data in farming and agriculture to unveil proficiency from data that is raw and unstructured (Shukla, Radadiya and Atkotiya 2015). The recommended systems for implementing Big Data in agricultural analytics are as follows: Hadoop: It is an open source software framework that are used for running applications and storing data on clusters of commodity hardware. It gives huge storage for enormous processing power, any sort of data, and the ability to handle virtually limitless concurrent jobs or tasks. Map Reduce System: It is a programming model and an associated implementation for processing and generating large data sets with a parallel, distributed algorithm on a cluster. Conclusion Therefore, from the above discussion it can be concluded that, Big Data is essential in modern world. Big data is the term used for data sets so huge and complicated that it becomes hard to process using traditional data management tools or processing applications. As with many other sectors the amount of agriculture data are increasing on a daily source. Big data is an increasingly important concern in modern agriculture. The use of electronic and smart technologies, now make it possible to collect vast amount of digital information about agriculture factors. The above report contains the consumer centric product design and two recommended systems of Big Data for agricultural analytics References Srinivasa, S. and Bhatnagar, V., 2012. Big data analytics. InProceedings of the First International Conference on Big Data Analytics BDA(pp. 24-26). Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business intelligence and analytics: From big data to big impact.MIS quarterly,36(4). Kambatla, K., Kollias, G., Kumar, V. and Grama, A., 2014. Trends in big data analytics.Journal of Parallel and Distributed Computing,74(7), pp.2561-2573. Kumar, T.V., JNU, D., Rana, P.S., Sinha, M.S., Tagra, H., Misra, M.B., Goyal, V., Singh, M.P., Kaur, S. and DU, D., 2017. Big Data Analytics. Woodard, J., 2016. Big data and Ag-Analytics: An open source, open data platform for agricultural environmental finance, insurance, and risk.Agricultural Finance Review,76(1), pp.15-26. Nabrzyski, J., Liu, C., Vardeman, C., Gesing, S. and Budhatoki, M., 2014, June. Agriculture data for all-integrated tools for agriculture data integration, analytics, and sharing. InBig Data (BigData Congress), 2014 IEEE International Congress on(pp. 774-775). IEEE. Bennett, J.M., 2015. Agricultural Big Data: utilisation to discover the unknown and instigate practice change.Farm Policy Journal,12(1), pp.43-50. Shukla, P., Radadiya, B. and Atokotiya, K., 2015. An Emerging Trend of Big data for High Volume and Varieties of Data to Search of Agricultural Data.ORIENTAL JOURNAL OF COMPUTER SCIENCE TECHNOLOGY, ISSN, pp.0974-6471.