Abstract
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.
Similar content being viewed by others
References
Farmin, S. W., Ge, L., Verdouw, C., & Bogaard, M. (2017). Big data in smart: Review. Agriculture System, 153, 69–80.
World Bank. (2018) Overview, [online]. Available at http://www.worldbank.org/en/topic/agriculture/overview Accessed 26 Mar 2018.
A. Brian. (1987). Demand forecasting and estimation. 77–85.
Zeng, Benxiang, & Gerritsen, Rolf. (2014). What do we know about social media in tourism? A review. Tourism Management Perspectives, 10, 27–36.
Fan, T. (2013). Smart agriculture based on cloud computing and IOT. Journal of Convergence Information Technology., 8, 2.
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.
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.
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.
Camargo, A., & Smith, J. S. (2009). An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosystems Engineering, 102(1), 9–21.
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.
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.
Wolfert, Sjaak, Ge, Lan, Verdouw, Cor, & Bogaardt, Marc-Jeroen. (2017). Big data in smart farming—a review. Agricultural Systems, 153, 69–80.
Jeff Gentry (2015). twitteR: R Based Twitter Client. R, package version 1.1.9, https://CRAN.R-project.org/package=twitteR.
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 tensorflow.org.
Kingma, D. P., & Lei, J. Adam: A method for stochastic optimization. ICLR 2015.
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.
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space, CoRR abs/1301.3781.
Cuzick, J. (2005). Rank Regression. In: Encyclopedia of Biostatistics (eds P. Armitage and T. Colton).
Hicham, Gueddah. “Introduction of the weight edition errors in the Levenshtein distance.” arXiv preprint arXiv:1208.4503(2012).
Stephen, V. (1997). Stehman, Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment, 62(1), 77–89.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Saravanan, M., Perepu, S.K. Realizing Social-Media-Based Analytics for Smart Agriculture. Rev Socionetwork Strat 13, 33–53 (2019). https://doi.org/10.1007/s12626-019-00035-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12626-019-00035-3