Classification of Pests for Rice Crop Using Big Data Analytics

Authors

  • R. P. L. Durgabai Department of Computer Science, Sri Padmavati Mahila Visva Vidyalayam, Andhra Pradesh, India
  • P. Bhargavi Department of Computer Science, Sri Padmavati Mahila Visva Vidyalayam, Andhra Pradesh, India
  • S. Jyothi Department of Computer Science, Sri Padmavati Mahila Visva Vidyalayam, Andhra Pradesh, India

Keywords:

Rice Crop, Pest, Production, Big Data, Agriculture

Abstract

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] Retrieved from 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. [3] Ananya Chakraborty, and Emmanuel Vijayanand 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 at: https://www. tutorialspoint.com/articles/apache-hadoop-and-its-components, 2015

Apache Pig advantages and disadvantages, [Online] Available at: 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.

SeemaAcharya, and Subhashini Chellapan, Big Data and Analytics – Wiley Publications, 2015.

Dr. BirendraGoswami, and Pradip Kumar Chandra, “The Evolution of Big Data as A Research and Development”, International Journalof Scientific Research and Engineering Studies (IJSRES), Vol. 2, No. 3, March 2015.

Agarwal,Shafali, and ZebaKhanam, “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 Devender P.Gadekar, “A Review Paper on Big Data and Hadoop”, International Journal of Scientific and Research Publications, 2014.

Chavan, Vibhavari, and Rajesh 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, 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 Heljanko K. Jan, “Simple and scalable scripting for large sequencing data sets in hadoop Bioinformatics”, Vol.30, No. 1, 2014.

S. Arjun, Anish Joshi, H.Pooja 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.

Published

15-11-2019

How to Cite

Durgabai, R. P. L., Bhargavi, P., & Jyothi, . S. (2019). Classification of Pests for Rice Crop Using Big Data Analytics. Asian Journal of Computer Science and Technology, 8(3), 27–31. Retrieved from https://ojs.trp.org.in/index.php/ajcst/article/view/2737