Smart agriculture with big data

  • Authors

    • Kondapally Madhavi
    • Aarthi priya R
    • K Suresh
    • Deepa J
    https://doi.org/10.14419/ijet.v7i3.29.19287
  • Big Data, Smart Agriculture, Hadoop, R, Cloud Computing, Data Acquisition, Classification Data Analytics, Weather Forecast
  • This paper presents the importance of smart agriculture, which is the revolution in traditional farming using new technologies such as big data, Hadoop, R programming and cloud computing. This paper covers the major areas of agriculture from seed to the marketing level. This schema contains mainly three layers. In the First Layer that is data collection layer, collecting data using sensors related to weather and environmental changes, identification of soil types, crop identification ,yield tracking, water availability in the soil . Not only sensor data, Here we will take Historical data i.e. past data, by this prediction will be easy. All this data will be stored in HDFS i.e. Hadoop distributed file system .In the second layer that is Analysis layer, the collected information is analysed using data mining algorithms. The most important part in data analysis is data acquisition; we extract knowledge from sensor data in real time and historical data. In the third layer i.e. data prediction layer predictions obtained from the analysis based on that we will get the answers for the questions what to plant, when to plant and where to plant for this purpose we use data mining algorithms. The prediction discovers relationship between independent variables and relationship between dependent and independent variables. Many classification and regression algorithms are available for data prediction. Some of them are Decision Trees, Artificial neural networks, Support Vector Machine, Bayesian classification and Regression and K-means clustering. Among these we are using Decision Trees and K means clustering for prediction.

     

     
  • References

    1. [1] So yahata, Tetsu onishi ,kanta yamaguchi†A Hybrid Machine Learning Approach to Automatic Plant Phenotyping for Smart Agriculture†978-1-5090-6182-2/17 ©2017 IEEE 1787-1793.

      [2] Purva grover, Rahul Johari†PAID: Predictive Agriculture of data integration in India†2016 InternationalConference on Computing for SustainableGlobal Development (INDIACom) 184-188.

      [3] M.R.Bendre “Big Data in Precision Agriculture : Weather Forecasting for Future Farming†2015 1st International Conference on Next Generation Computing Technologies (NGCT-2015) Dehradun, India, 4-5 September 2015,744-750.

      [4] Qiulan Wuâ€Research on intelligent acquisition of smart agriculture big dataâ€.

      [5] Wikipediahttps://en.wikipedia.org/wiki/Agriculture_in_India.

      [6] jaak wolfert†big data in smart farming-A review†Elsevier Agriculture Systems 153(2017) 69-80.

      [7] Divya chauhan “Data Mining Techniques for Weather Prediction: A Review†International Journal on recent and Innovation Trends in computing and communication volume 2 issue 8 2184-2189.

      [8] D.R. Kothawale and M. Rajeevan “Monthly, Seasonal and Annual Rainfall Time Series for All-India, Homogeneous Regions and Meteorological Subdivisions: 1871-2016 “Research Report No. RR-138.

      [9] K.K singh “Crop Yield forecasting under FASAL (Forecasting Agricultural output using Space Agro meteorology and Land based observations) “.

      [10] Sahitha Roy†IOT, Big Data Science & Analytics, Cloud Computing and Mobile App based Hybrid System for Smart Agriculture “978-1-5386-2215-5/17 ©2017 IEEE 303-304.

      [11] Nitin, Vivek Kumar Sehgal, et al., Image Based Authentication System with Sign-In Seal,Proc. of the World Congress on Engineering and Computer Science, WCECS 2008, SanFrancisco, USA, 2008.

      [12] Renaud K., Just M., Pictures or Questions? Examining User Responses to Association-Based Authentication, ACM Proceedings of the British HCI Conference 2010, Dundee, Scotland, and 6-10 September 2010.

      [13] Confident Technologies Inc., Confident ImageShieldTM, Available at: http:// www .confident technologies.com/ products/ confident-imageshield, 2011.

      [14] Newman R.E. HarshP., and Jayaraman P, Security Analysis of and Proposal for Image Based Authentication, IEEE Carnahan, 2005.

      [15] Just M. and Aspinall D., Personal choice and challenge questions: A security and usability assessment. In L. Cranor, editor, SOUPS, ACM International Conference Proceeding Series. ACM, 2009.

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  • How to Cite

    Madhavi, K., priya R, A., Suresh, K., & J, D. (2018). Smart agriculture with big data. International Journal of Engineering & Technology, 7(3.29), 447-452. https://doi.org/10.14419/ijet.v7i3.29.19287