Big Data Analytics in Modern Agriculture

This assignment delves into the transformative potential of Big Data in agriculture. It discusses how massive datasets from smart technologies are reshaping agricultural practices. The paper examines the advantages and challenges of implementing Big Data analytics in this sector, including data incompleteness, privacy concerns, and the need for human collaboration. Two recommended systems—Hadoop and MapReduce—are presented as tools for processing and analyzing vast agricultural datasets effectively.

Running head: AGRICULTURAL ANALYTICS
Agricultural Analytics
Name of the Student
Name of the University
Author’s Note:

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Executive Summary
The objective of this report is to understand the Big Data Analytics method for
Agricultural Analytics. Big data analytics is the procedure of evaluating varied and huge
data sets known as the big data that is used to display concealed patterns, preferences
of customer, market trends, unknown correlations and other important information,
which can help different organizations to make more notified decisions of business. The
ability of Business Intelligence (BI) technologies to provide historical, current, and
predictive views of business operations based on the collection, extraction, and analysis
of business data to improve decision has been the basis of several studies. More
recently, Big Data and Big Data Analytics have further stirred the interest of researchers
and practitioners alike. This report helps to understand how Big Data technologies are
beneficial to Agricultural Analytics.

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Table of Contents
Introduction............................................................................................................3
Discussion.............................................................................................................3
Consumer Centric Product Design....................................................................3
Big Data.............................................................................................................4
Recommendation System..................................................................................5
Conclusion.............................................................................................................6
References............................................................................................................8

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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

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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 process. 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.

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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:
i) Decisive use of irrigation can be achieved by identifying soil moisture using
very high resolution geographical maps
ii) Vital 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).

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iii) 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:
a) 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.
b) 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

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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.

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References
Srinivasa, S. and Bhatnagar, V., 2012. Big data analytics. In Proceedings 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. In Big 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.

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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.