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Adopting Big Data Analysis in the Agricultural Sector: Financial and Societal Impacts

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Internet of Things and Analytics for Agriculture, Volume 2

Part of the book series: Studies in Big Data ((SBD,volume 67))

Abstract

Big data analytics (BDA) is constantly formulating decisions in agriculture and transforming the processes by which agriculture operates and controls. The agriculture process can be composed of a sequential flow of stages, starting from planting, spraying, fertilization, collection, and distribution to the end consumer. The variety within agriculture data is distinctly heterogeneous. This big farming data can come in all shapes and sizes from a variety of sources, which composes a hard analytical process for decision-makers. Big data analytical techniques have been applied to each stage in the agricultural operational process. These techniques have been proven to establish both economic gain for farmers and environmental and safety benefits for society at large. This chapter summarizes the state-of-art analytical methods that have been recently used in the agriculture industry, along with their financial and societal impacts.

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References

  1. Bendre, M.R., et al.: Big data in precision agriculture: weather forecasting for future farming. Next Generation Computing Technologies (2015)

    Google Scholar 

  2. Jones, S., Rapp, N.: Where $64.5B in VC funding went last year. Fortune, 2 July 2015. Web. 12 Apr. 2017

    Google Scholar 

  3. Kotsiantis, S.B.: Supervised machine learning: a review of classification techniques (2007)

    Google Scholar 

  4. Dietterich, T.G.: Ensemble methods in machine learning (2000)

    Google Scholar 

  5. Kouru, K., Exarchos, T.P., Exarchos, K.P., Karmaouzis, M.V., Fotiadis, D.I.: Machine learning applications in cancer prognosis and prediction (2015)

    Google Scholar 

  6. Magidson, J., Vermunt, J.: Latent class models for clustering. Can. J. Market. Res. (2002)

    Google Scholar 

  7. Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. J. Intell. Inf. Syst. 17, 107–145 (2001)

    Article  Google Scholar 

  8. Rezaei, M., Fränti, M.P.: Set matching measures for external cluster validity. IEEE Trans. Knowl. Data Eng. 28(8), 2173–2186 (2016)

    Article  Google Scholar 

  9. Rendón, E., Abundez, I., Arizmendi, A., Quiroz, E.M.: Internal versus external cluster validation indexes. Int. J. Comput. Commun. 5(1), 27–34 (2011)

    Google Scholar 

  10. Reichart, R., Rappoport, A.: The NVI clustering evaluation measure. In: Proceedings of the 13 Conference on Computational Natural Language Learning, pp. 165–173) (2009)

    Google Scholar 

  11. Rosenberg, A., Hirschberg, J.: V-measure: a conditional entropy-based external cluster evaluation measure. EMNLP-CoNLL 7, 410–420 (2007)

    Google Scholar 

  12. Draszawka, K., Szymanski, J.: External validation measures for nested clustering of text documents. ISMIS Ind. Sess. 369, 207–225 (2011)

    Google Scholar 

  13. De Souto, M.C., Coelho, A., Faceli, K., Sakata, T., Bonadia, V., Costa I. : A comparison of external clustering evaluation indices in the context of imbalanced data sets. Brazilian Symposium on Neural Networks (2012)

    Google Scholar 

  14. Dalli A.: Adaptation of the F-measure to cluster based lexicon quality evaluation. In: Proceedings of the EACL Workshop on Evaluation Initiatives in Natural Language Processing, pp. 51–56 (2003)

    Google Scholar 

  15. Rezaei, M., Fränti, P.: Set matching measures for external cluster validity. IEEE Trans. Knowl. Data Eng. 28(8), 2173–2186 (2016)

    Article  Google Scholar 

  16. Lei, Y., Bezdek, J., Chan, J., Vinh, N., Romano, S., Bailey, J.: Extending information-theoretic validity indices for fuzzy clustering. IEEE Trans. Fuzzy Syst. 25(4), 1013–1018 (2017)

    Article  Google Scholar 

  17. Cao, J., Lin, Z., Huang, G.: Self-adaptive evolutionary extreme learning machine. Neural Process. Lett. 36, 285–305 (2012)

    Article  Google Scholar 

  18. Mary, S., Sivagami, A., Rani, M.: Cluster validity measures dynamic clustering algorithms. ARPN J. Eng. Appl. Sci. 10(9), 4009–4012 (2015)

    Google Scholar 

  19. Jang, J.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23, 665–685 (1993)

    Article  Google Scholar 

  20. Yeh, C., Yang, M.: Evaluation measures for cluster ensembles based on a fuzzy generalized Rand index. Appl. Soft Comput. 57, 225–234 (2017)

    Article  Google Scholar 

  21. zu Eissen, B., Wißbrock, F.: On cluster validity and the information need of users. ACTA Press, pp. 216–221 (2003)

    Google Scholar 

  22. Wu, K., Yang, M.: A cluster validity index for fuzzy clustering. Pattern Recog. Lett. 26(9), 1275–1291 (2005)

    Article  MathSciNet  Google Scholar 

  23. Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. J. Intell. Inf. Syst. 17, 107–145 (2001)

    Article  Google Scholar 

  24. Petrovic, S.: A comparison between the silhouette index and the davies-bouldin index in labelling ids clusters. In: Proceedings of the eleventh Nordic Workshop of Secure IT Systems, pp. 53–64 (2006)

    Google Scholar 

  25. Brownstein, J., Freifeld, B., Madoff, L.: Digital disease detection: harnessing the web for public health surveillance. New England J. Med. (2009)

    Google Scholar 

  26. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Hung Byers, A.: Big data: the next frontier for innovation, competition, and productivity (2011)

    Google Scholar 

  27. Rindler, A., McClowry, S., Hillard, R.: Big data definition (2012)

    Google Scholar 

  28. Cukier, K., Mayer-Schönberger, V.: Big data: a revolution that will transform how we live, work, and think, p. 22 (2013)

    Google Scholar 

  29. Gandomi, A., Haider, M.: Beyond the hype: big data concepts, methods, and analytics (2015)

    Article  Google Scholar 

  30. White, T.: Hadoop: the definitive guide (2009)

    Google Scholar 

  31. Wolfert, S., et al.: Big data in smart farming. Retrieved from Agricultural Systems (2017)

    Google Scholar 

  32. Greengard, Samuel: Weathering a new era of big data. Commun. ACM 57(9), 12–14 (2014)

    Article  Google Scholar 

  33. Mining Indonesian tweets to understand food price crisis. Feb 2014. Web. Apr 2017

    Google Scholar 

  34. Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386–408 (1958)

    Article  Google Scholar 

  35. Riedmiller, M., Braun, H.: A direct adaptive method for faster back propagation learning: the RPROP algorithm. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 586–591 (1993)

    Google Scholar 

  36. Hecht-Nielsen, R.: Counterpropagation Networks. Appl. Opt. 26, 4979–4983 (1987)

    Article  Google Scholar 

  37. Melssen, W., Wehrens, R., Buyden, L.: Supervised Kohonen networks for classification problems. Chemom. Intell. Lab. Syst. 83, 99–113 (2006)

    Article  Google Scholar 

  38. Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. 79, 2554–2558 (1982)

    Article  MathSciNet  Google Scholar 

  39. Kohonen, T.: The self-organizing map. Proc. IEEE. 78, 1464–1480 (1990)

    Article  Google Scholar 

  40. Huang, G., Zhu, Q., Siew, C.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006)

    Article  Google Scholar 

  41. Hansen, M., Smith, M., Smith, L., Salter, M., Baxter, E., Farish, M., Grieve, B.: Towards on-farm pig face recognition using convolutional neural networks. Comput. Ind. 98, 145–152 (2018)

    Article  Google Scholar 

  42. Ferentinos, K.: Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018)

    Article  Google Scholar 

  43. Ju, Y.: Conversion of remote sense picture into data array (2018)

    Google Scholar 

  44. Goodfellow, I., Bengio, Y., Courville, A.: Deep learning. MIT Press, pp. 216–261 (2016)

    Google Scholar 

  45. Stuart Ward, J., Barker, A.: Undefined by data: a survey of big data definitions (2013)

    Google Scholar 

  46. Lukman, E.: Indonesia is social: 2.4% of world’s twitter posts come from Jakarta. Tech in Asia—Connecting Asia’s Startup Ecosystem, p. 12 (2017)

    Google Scholar 

  47. Greenberg, J., Elliott, C.: A cold cut crisis: listeriosis, maple leaf foods, and the politics of apology. Can. J. Commun. 9, 189–204 (2009)

    Google Scholar 

  48. Noyes, K.: Big data poised to change the face of agriculture. Fortune, 02 June 2014 (2017)

    Google Scholar 

  49. Kamilaris, A., et al.: Estimating the environmental impact of agriculture by means of geospatial and big data analysis: the case of catalonia. Science to Society (2018)

    Google Scholar 

  50. Sonka S.: Big data and the ag sector: more than lots of numbers. Int. Food Agribus. Manag. Rev. (2014)

    Google Scholar 

  51. About GODAN. About GODAN|GODAN. GODAN (2017)

    Google Scholar 

  52. Blue river technology. http://www.bluerivert.com/

  53. Cerqueira, R.F., Mantripragada, K.: Estimating rainfall precipitation amounts by applying computer vision in cameras. U.S. Patent No. 9,436,997 (2016)

    Google Scholar 

  54. Kumar, N., et al.: Leafsnap: a computer vision system for automatic plant species identification. Computer Vision ECCV Lecture Notes in Computer, pp. 502–516 (2017)

    Chapter  Google Scholar 

  55. Treinish, L., Praino, A.P.: Applications and implementation of a mesoscale numerical weather prediction and visualization system. In: Proceedings of the 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction (2004)

    Google Scholar 

  56. Zulkarnain, H.: IBM research: using weather modeling and data analytics to save natural resources (2017)

    Google Scholar 

  57. Nowcasting food prices in Indonesia using social media signals (2017)

    Google Scholar 

  58. Treinish, A., Anthony, P.: The role of meso-γ-scale numerical weather prediction and visualization for weather-sensitive decision making. Proc. Forum Environ. Risk Impacts Soc. Successes Chall. (2006)

    Google Scholar 

  59. Woodie, A.: Farmer’s plant for hyper-local forecasts with IBM’s deep thunder. Datanami. Tabor Commun. 8 Apr 2017

    Google Scholar 

  60. Weston, J.: Support vector machine (and statistical learning theory). Princeton Tutorial. Princeton, USA. Tutorial

    Google Scholar 

  61. Zikopoulos, P., Eaton, C.: Understanding big data: analytics for enterprise class Hadoop and streaming data (2011)

    Google Scholar 

  62. Meilă, M.: Comparing clusterings: an information based distance. J. Multivar. Anal. 98(5), 873–895 (2007)

    Article  MathSciNet  Google Scholar 

  63. Linnainmaa, S.: Taylor expansion of the accumulated rounding error. BIT 16, 146–160 (1976)

    Article  MathSciNet  Google Scholar 

  64. Broomhead, D., Lowe, D.: Multivariable functional interpolation and adaptive networks. Comp. Syst. 2, 321–355 (1988)

    MathSciNet  MATH  Google Scholar 

  65. Specht, D.: A general regression neural network. IEEE Trans. Neural Netw. 2, 568–576 (1991)

    Article  Google Scholar 

  66. Effland, T., et al.: Discovering foodborne illness in online restaurant reviews. J. Am. Med. Inf. Assoc. (2018)

    Google Scholar 

  67. Proudly Farmers First. farmers business network (2017)

    Google Scholar 

  68. Solano, B.: Our story. Farmobile|Press Page. Farmobile, n.d. Web. 13 Apr 2017 http://press.farmobile.com/

  69. Sonka, S.: Big data and the ag sector: more than lots of numbers. Int. Food Agribus. Manag. Rev. 17(1), 1–20 (2014)

    Google Scholar 

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Authors and Affiliations

  1. Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, ON, Canada

    Rasha Kashef

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  1. Rasha Kashef
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Correspondence to Rasha Kashef .

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Editors and Affiliations

  1. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India

    Prasant Kumar Pattnaik

  2. Department of Computer Science and Engineering, LNCT College, Jabalpur, Madhya Pradesh, India

    Raghvendra Kumar

  3. Department of Computer Science and Engineering, Brainware University, Kolkata, West Bengal, India

    Souvik Pal

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Kashef, R. (2020). Adopting Big Data Analysis in the Agricultural Sector: Financial and Societal Impacts. In: Pattnaik, P., Kumar, R., Pal, S. (eds) Internet of Things and Analytics for Agriculture, Volume 2. Studies in Big Data, vol 67. Springer, Singapore. https://doi.org/10.1007/978-981-15-0663-5_7

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