Realizing Social-Media-Based Analytics for Smart Agriculture

  • M. SaravananEmail author
  • Satheesh K. Perepu


Continuous fluctuation of the daily prices of vegetables and pulses is destabilizing to a country’s economy and hinders the nation’s monetary planning. One factor that affects the fluctuation of the prices is changes in the production of the vegetables and pulses. Early detection and treatment of crop and plant diseases will substantially improve production and constitutes an attempt to introduce smart technology into the system. There is an immediate need for an intelligent solution that advises farmers on how to address crop and plant diseases at the point of demand. In this paper, we have explored social media analytics as a knowledge-intensive process and proposed a new framework for identifying the relevant details of plants from both text and images. This approach will generate knowledge about plant diseases and other areas of development that are related to precision agriculture. The community of farmers within social media is expected to provide immediate suggestions to questions that are posted by members regarding plant diseases and other problems. The newly designed innovative framework identifies the relevant features, finds similarities among the farmers’ heuristics, and ranks their suggestions related to merit and relevance. Our system employs deep learning, natural language processing and other predictive models to compare the combination of text and image extracts and suggest a suitable solution to the farmer’s query. The efficiency of the employed models is evaluated to avoid false positives, and the models are implemented on social media to address plant diseases and other relevant details as a part of evolving smart agriculture.


Social media Plant disease monitoring Deep learning Rank regression Smart agriculture 



  1. 1.
    Farmin, S. W., Ge, L., Verdouw, C., & Bogaard, M. (2017). Big data in smart: Review. Agriculture System, 153, 69–80.CrossRefGoogle Scholar
  2. 2.
    World Bank. (2018) Overview, [online]. Available at Accessed 26 Mar 2018.
  3. 3.
    A. Brian. (1987). Demand forecasting and estimation. 77–85.Google Scholar
  4. 4.
    Zeng, Benxiang, & Gerritsen, Rolf. (2014). What do we know about social media in tourism? A review. Tourism Management Perspectives, 10, 27–36.CrossRefGoogle Scholar
  5. 5.
    Fan, T. (2013). Smart agriculture based on cloud computing and IOT. Journal of Convergence Information Technology., 8, 2.Google Scholar
  6. 6.
    Long, Thomas B., Blok, Vincent, & Coninx, Ingrid. (2016). Barriers to the adoption and diffusion of technological innovations for climate-smart agriculture in Europe: evidence from the Netherlands, France, Switzerland and Italy. Journal of Cleaner Production, 112, 9–21.CrossRefGoogle Scholar
  7. 7.
    Lipper, L., Thornton, P., Campbell, B. M., Baedeker, T., Braimoh, A., Bwalya, M., et al. (2014). Climate-smart agriculture for food security. Nature Climate Change., 4(12), 1068.CrossRefGoogle Scholar
  8. 8.
    Campbell, B. M., Thornton, P., Zougmoré, R., Van Asten, P., & Lipper, L. (2014). Sustainable intensification: What is its role in climate smart agriculture? Current Opinion in Environmental Sustainability., 8, 39–43.CrossRefGoogle Scholar
  9. 9.
    Camargo, A., & Smith, J. S. (2009). An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosystems Engineering, 102(1), 9–21.CrossRefGoogle Scholar
  10. 10.
    Camargo, A., & Smith, J. S. (2009). Image pattern classification for the identification of disease causing agents in plants. Computers and Electronics in Agriculture, 66(2), 121–125.CrossRefGoogle Scholar
  11. 11.
    Rutsaert, Pieter, Regan Pieniak, Zuzanna, McConnon Moss, Adrian, Wall, Patrick, et al. (2013). The use of social media in food risk and benefit communication. Trends in Food Science & Technology, 30(1), 84–91.CrossRefGoogle Scholar
  12. 12.
    Wolfert, Sjaak, Ge, Lan, Verdouw, Cor, & Bogaardt, Marc-Jeroen. (2017). Big data in smart farming—a review. Agricultural Systems, 153, 69–80.CrossRefGoogle Scholar
  13. 13.
    Jeff Gentry (2015). twitteR: R Based Twitter Client. R, package version 1.1.9,
  14. 14.
    Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S.(2015). TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from Scholar
  15. 15.
    Kingma, D. P., & Lei, J. Adam: A method for stochastic optimization. ICLR 2015.Google Scholar
  16. 16.
  17. 17.
    Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. (2011). Natural Language Processing (Almost) from Scratch. Journal of Machine Learning Research., 12, 2493–2537.Google Scholar
  18. 18.
    Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space, CoRR abs/1301.3781.Google Scholar
  19. 19.
    Cuzick, J. (2005). Rank Regression. In: Encyclopedia of Biostatistics (eds P. Armitage and T. Colton).Google Scholar
  20. 20.
    Hicham, Gueddah. “Introduction of the weight edition errors in the Levenshtein distance.” arXiv preprint arXiv:1208.4503(2012).
  21. 21.
    Stephen, V. (1997). Stehman, Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment, 62(1), 77–89.CrossRefGoogle Scholar

Copyright information

© Springer Japan KK, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Ericsson Research Chennai, Ericsson India Global Services Pvt. Ltd.ChennaiIndia

Personalised recommendations