Classification of Pests for Rice Crop Using Big Data Analytics
DOI:
https://doi.org/10.51983/ajcst-2019.8.3.2737Keywords:
Rice Crop, Pest, Production, Big Data, AgricultureAbstract
Data, in today’s world, is essential. The Big Data technology is rising to examine the data to make fast insight and strategic decisions. Big data refers to the facility to assemble and examine the vast amounts of data that is being generated by different departments working directly or indirectly involved in agriculture. Due to lack of resources the pest analysis of rice crop is in poor condition which effects the production. In Andhra Pradesh rice is cultivated in almost all the districts. The goal is to provide better solutions for finding pest attack conditions in all districts using Big Data Analytics and to make better decisions on high productivity of rice crop in Andhra Pradesh.
References
"Data Science in the Indian Agriculture Industry," [Online] Available: https://www.analyticsvidhya.com/blog/2018/05/data-analytics-in-the-indian-agriculture-industry/, 2018.
N. Yogeshwara Sastry, "Agricultural Statistics at a Glance-2016-2017," Government of Andhra Pradesh, May 2017.
A. Chakraborty and E. V. Murray, "Rice Production and Productivity in Andhra Pradesh," Research Gate, June 2011.
Mukeshkumar and Mayura Nagar, "Big Data Analytics in Agriculture and distribution channel," International Conference on Computing Methodologies and Communication, Accession number: 17575272, July 2017.
"Hadoop and its components," [Online] Available: https://www.tutorialspoint.com/articles/apache-hadoop-and-its-components, 2015.
"Apache Pig advantages and disadvantages," [Online] Available: https://data-flair.training/blogs/pig-advantages-and-disadvantages/, 2018.
Krantibansal and Priyanka Chawla, "A study of Big Data Analysis using Apache Pig," International Journal for IJCTA, pp. 8665-8672.
C. Swarna and Zahid Ansari, "Apache Pig-A Data flow Frame Work based on Hadoop Map Reduce," International Journal of Engineering Trends and Technologies, vol. 50, 2017.
S. Acharya and S. Chellapan, Big Data and Analytics – Wiley Publications, 2015.
Dr. B. Goswami and P. K. Chandra, "The Evolution of Big Data as A Research and Development," International Journal of Scientific Research and Engineering Studies (IJSRES), vol. 2, no. 3, March 2015.
Agarwal, Shafali, and Zeba Khanam, "Map Reduce: A Survey Paper on Recent Expansion," International Journal of Advanced Computer Science and Applications, vol. 6, no. 8, pp. 209-215, 2015.
S. Bhosale, Harshawardhan, and D. P. Gadekar, "A Review Paper on Big Data and Hadoop," International Journal of Scientific and Research Publications, 2014.
V. Chavan, Vibhavari, and R. N. Phursule, "Survey paper on Big Data," International Journal of Computer Science Information Technology, vol. 5, no. 6, 2014.
Samak, Taghrid, Daniel Gunter, and Valerie Hendrix, "Scalable analysis of network measurements with Hadoop and Pig," Network Operations and Management Symposium, IEEE, 2012.
N.G. Yethiraj and Noor Ayesha, "A study to improve crop Yield in Agriculture using IOT and Bigdata," Adarsh Journal of Information Technology, vol. 6, 2017.
D. Laney, "3D data management: Controlling data volume, velocity and variety," Meta Group Inc Application Delivery Strategies, 2012.
X.W. Chen and X. Lin, "Big data deep learning challenges and perspective," IEEE Access, vol. 2, pp. 514–22, 2014.
V. Marx, "Biology: The big challenges of big data," Nature, vol. 498, no. 7453, pp. 255–60, 2013.
"Big Data in Agriculture," [Online] Available at: http://www.citethis-forme.com/topic-ideas/technology/’Big%20Data’-6678234.
H. Zhang, X. Wei, T. Zou, Z. Li, and G. Yang, "Agriculture big data: Research status, challenges and countermeasures," Proceedings of Computer and Computing Technologies in Agriculture, China, 2014.
A. Schumacher, L. Pireddu, M. Niemenmaa, A. Kallio, E. Korpelainen, G. Zanetti, and K. Heljanko, "Simple and scalable scripting for large sequencing data sets in Hadoop Bioinformatics," vol.30, no. 1, 2014.
S. Arjun, A. Joshi, H. P. Das, and R. Amutha, "Big Data Analytics for Agriculture Development in India," International Journal of Engineering Research and Technology, 2016.
Ehizogie Omo-Ojugo, "Relevance of Big Data Analytics in Agriculture: Focus on Nigeria Agricultural Sector," International Journal of Science and Research, 2017.
K. Ravisankar, K. Sidhardha, and B. Prabadevi, "Analysis of Agricultural Data Using Big Data Analytics," Journal of Chemical and Pharmaceutical Sciences, vol. 10, no. 3, 2017.
S. S. De, G. Chattopadhyay, B. Bandyopadhyay, and S. Paul, "A neuro-computing approach to the forecasting of monthly maximum temperature over Kolkata, India using total ozone concentration as predictor," Comptes Rendus Geoscience, vol. 343, no. 10, pp. 664-676, 2011.
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