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Applications of Geospatial and Big Data Technologies in Smart Farming

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Smart Agriculture for Developing Nations

Part of the book series: Advanced Technologies and Societal Change ((ATSC))

Abstract

In recent years, smart farming is gaining popularity largely because of the introduction of high-precision technology tools, which are more accurate, cost-effective and user-friendly in adoption. These new innovative technologies include remote sensing, Global Positioning System (GPS), Geographic Information System (GIS), field sensors, artificial intelligence and automated machinery, big data, etc. Geospatial technologies emerged as one of the main sources for the generation of voluminous big data through various platforms of satellites, manned/unmanned aircrafts and ground-based installations. Smart farming uses various technologies like high-resolution satellite data, GPS, GIS, field sensors, artificial intelligence and automated machinery, etc. Further, trending technologies like drones, the Internet of Things (IoT) and cloud computing play a significant role in smart farming. Big data by virtue of its velocity, volume, value, variety and veracity is increasingly developed and used in various fields including agriculture. Big data has immense potential in smart farming like real-time weather monitoring, soil moisture monitoring, automated irrigation systems, inputs monitoring, crop monitoring, pest monitoring, etc. Since both big data and smart farming are emerging fields, the chapter attempts to provide the knowledge and awareness of their applications and implications to the researchers, farmers and other stakeholders to effectively leverage the potential of these emerging technologies to optimize the farm resources and improve overall farm productivity.

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References

  1. Abdmeziem, M.R., Tandjaoui, D., Romdhani, I.: Architecting the internet of things: state of the art. Robots Sens. Clouds 55–75 (2016)

    Google Scholar 

  2. Allen, T.R., Walsh, S.J.: Characterizing multitemporal alpine snowmelt patterns for ecological inferences. Photogramm Eng. Remote Sens. 59(10), 1521–1529 (1993)

    Google Scholar 

  3. Blaschke, T.: Object-based image analysis for remote sensing. ISPRS J. Photogramm Remote Sens. 65, 2–16 (2010)

    Article  Google Scholar 

  4. Brewster, C., Roussaki, I., Kalatzis, N., Doolin, K., Ellis, K.: IoT in agriculture: designing a Europe-wide large-scale pilot. IEEE Commun. Mag. 55(9), 26–33 (2017)

    Article  Google Scholar 

  5. Burrough, P.A., McDonnell, R.A.: Principles of Geographical Information Systems. Oxford University Press, Oxford (1998)

    Google Scholar 

  6. Buyya, R., Dastjerdi, A.V.: Internet of Things: Principles and Paradigms. Elsevier, New York (2016)

    Google Scholar 

  7. Chi, M., Plaza, A., Benediktsson, J.A., Sun, Z., Shen, J., Zhu, Y.: Big data for remote sensing: challenges and opportunities. Proc. IEEE 104, 2207–2219 (2016)

    Article  Google Scholar 

  8. Dobos, E., Carré, F., Hengl, T., Reuter, H.I., Tóth, G.: Digital soil mapping as a support to production of functional maps. Office for Official Publications of the European Communities, Luxemborg. EUR, 22123, 68 (2006)

    Google Scholar 

  9. Fakhruddin, H.: Precision agriculture: top 15 challenges and issues (2020). https://plagiarismdetector.net/teks.co.in/site/blog/precision-agriculture-top-5challenges-and-issues

  10. FAO: E-agriculture in Action. Italy, Rome (2017)

    Google Scholar 

  11. Fortino, G., Savaglio, C., Spezzano, G., Zhou, M.: Internet of things as system of systems: a review of methodologies, frameworks, platforms, and tools. IEEE Trans. Syst. Man Cybern.: Syst. (2020)

    Google Scholar 

  12. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R.: Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017)

    Article  Google Scholar 

  13. Hengl, T., Reuter, H.I. (eds.): Geomorphometry: Concepts, Software, and Applications. Developments in Soil Science. Elsevier, Amsterdam (2009)

    Google Scholar 

  14. Hou, L., Wang, X.D., Gao, Q., et al.: Construction of agricultural big data mining system based on Hadoop. J. Libr. Inf. Sci. Agric. 30(7), 19–21 (2018)

    Google Scholar 

  15. IPCC: Climate Change and Land: an IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems, p 864 (2019)

    Google Scholar 

  16. Jaguey, J.G., Villa-Medina, J.F., Lopez-Guzman, A., Porta-Gandara, M.A.: Smartphone irrigation sensor. IEEE Sens. J. 15, 5122–5127 (2015)

    Google Scholar 

  17. Kamilaris, A., Prenafeta-Boldú, F.X.: Deep learning in agriculture: a survey. Comput. Electron. Agric. 147, 70–90 (2018)

    Article  Google Scholar 

  18. Kanniah, K.D., Hashim, M.: A systematic approach in remote sensing education and training in Malaysia (with Special reference to Universiti Teknology Malaysia). Int. Arch. Photogramm. Remote Sens. 33(B6), 153–163 (2000)

    Google Scholar 

  19. Kingsford, R.T.: Managing the water of the Border Rivers in Australia: irrigation, government and the wetland environment. Wetland. Ecol. Manag. 7(1), 25–35 (1999)

    Article  Google Scholar 

  20. Liu, P.: A survey of remote-sensing big data. Front. Environ. Sci. 3, 1–6 (2015)

    Article  Google Scholar 

  21. Ma, Y., et al.: Remote sensing big data computing: challenges and opportunities. Future Gener. Comput. Syst. 51, 47–60 (2015)

    Article  Google Scholar 

  22. Marjani, M., Nasaruddin, F., Gani, A., Karim, A., Hashem, I.A.T., Siddiqa, A., Yaqoob, I.: Big IOT data analytics: architecture, opportunities, and open research challenges. IEEE Access 5, 5247–5261 (2017)

    Google Scholar 

  23. McKenzie, N.J., Jacquier, D., Ashton, L.J., Cresswell, H.P.: Estimating soil properties using the Atlas of Australian Soils. Technical Report 11/00, CSIRO Land and Water, Canberra (2000)

    Google Scholar 

  24. Moore, I.D., Lewis, A., Gallant, J.C.: Terrain attributes: estimation methods and scale effects. In: Jakeman, A.J., Beck, M.B., McAleer, M.J. (eds.) Modeling Change in Environmental Systems, pp. 189–214. Wiley, New York (1993)

    Google Scholar 

  25. Mulge, M., Sharnappa, M., Sultanpure, A., Sajjan, D., Kamani, M.: An invitation to subscribe. Int. J. Analy. Experiml. Modal. Analy. 10(1), 1112–1117 (2020)

    Google Scholar 

  26. Pike, R.J.: Geomorphometry: diversity in quantitative surface analysis. Prog. Phy. Geogr. 24, 1–20 (2000)

    Google Scholar 

  27. Qi, F., Zhu, A.-X., Harrower, M., Burt, J.E.: Fuzzy soil mapping based on prototype category theory. Geoderma 136, 774–787 (2006)

    Article  Google Scholar 

  28. Reddy, G.P.O.: Global positioning system: principles and applications. In: Reddy, G.P.O., Singh, S.K. (eds.) Geospatial Technologies in Land Resources Mapping, Monitoring and Management. Geotechnologies and the Environment, vol. 21, pp. 63–74. Springer, Cham (2018c)

    Google Scholar 

  29. Reddy, G.P.O., Kumar, K.C.A.: Machine learning algorithms for optical remote sensing data classification and analysis. In: Reddy, G.P.O., et al. (eds.) Data Science in Agriculture and Natural Resource Management, vol. 96, pp. 195–220. Springer (2022)

    Google Scholar 

  30. Reddy, G.P.O., Patil, N.G., Chaturvedi, A.: Sustainable Management of Land Resources—an Indian Perspective, pp. 796. Apple Academic Press Inc., Canada (2017)

    Google Scholar 

  31. Reddy, G.P.O.: Spatial data management, analysis, and modeling in GIS: principles and applications. In: Reddy, G.P.O., Singh, S.K. (eds.) Geospatial Technologies in Land Resources Mapping, Monitoring and Management. Geotechnologies and the Environment, vol. 21, pp. 127–142. Springer, Cham (2018b)

    Google Scholar 

  32. Reddy, G.P.O., Singh, S.K.: Geospatial Technologies in Land Resources Mapping, Monitoring, and Management, Geotechnologies and the Environment, vol. 21, pp. 638. Springer (2018)

    Google Scholar 

  33. Reddy, G.P.O.: Geographic information system: principles and applications. In: Reddy, G.P.O., Singh, S.K. (eds.) Geospatial Technologies in Land Resources Mapping, Monitoring and Management. Geotechnologies and the Environment, vol. 21, pp. 45–62. Springer, Cham (2018a)

    Google Scholar 

  34. Reddy, G.P.O., Dwivedi, B.S., Chary, G.R.: Big data in smart farming: challenges and opportunities. Indian Farming 71(11), 75–78 (2021)

    Google Scholar 

  35. Reddy, G.P.O., Maji, A.K., Nagaraju, M.S.S., Thayalan, S., Ramamurthy, V.: Ecological evaluation of land resources and land-use systems for sustainable development at watershed level in different agro-ecological zones of Vidarbha region. In: Maharashtra using Remote sensing and GIS Techniques, Project Report, NBSS & LUP, Nagpur, 270p (2008)

    Google Scholar 

  36. Schuster, J.: Big data ethics and the digital age of agriculture. Resour. Eng. Technol. Sustain. World 24(1), 20–21 (2017)

    Google Scholar 

  37. Slalmi, A., Chaibi, H., Saadane, R., Chehri, A., Jeon, G., Aroussi, H.K.: Energy-efficient and self-organizing internet of things networks for soil monitoring in smart farming. Comput. Elect. Eng. 92, e107142 (2021)

    Article  Google Scholar 

  38. Smith, M., Pain, C.: Applications of remote sensing in geomorphology. Prog. Phy. Geogr. 33, 568–582 (2009)

    Article  Google Scholar 

  39. Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., Brisco, B.: Google earth engine for geo-big data applications: a meta-analysis and systematic review. ISPRS J. Photogramm Remote Sens. 164, 152–170 (2020)

    Article  Google Scholar 

  40. Tao, Z.L., Guan, X.F., Chen, Y.W.: Construction of information sharing platform based on agricultural big data. Ind. Technol. Forum 17(11), 56–57 (2018)

    Google Scholar 

  41. Tayur, V.M., Suchithra, R.: Review of interoperability approaches in application layer of internet of things. In: International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp. 322–326. IEEE (2017)

    Google Scholar 

  42. Vanegas, F., Bratanov, D., Powell, K., Weiss, J., Gonzalez, F.: A novel methodology for improving plant pest surveillance in vineyards and crops using UAV-based hyperspectral and spatial data. Sensors 18, 260 (2018)

    Article  Google Scholar 

  43. Vijayakanthan, G., Kokul, T., Pakeerathai, S., Pinidiyaarachchi, U.A.J.: Classification of vegetable plant pests using deep transfer learning. In: 10th International Conference on Information and Automation for Sustainability (ICIAfS), pp. 167–172 (2021). https://doi.org/10.1109/ICIAfS52090.2021.9606176

  44. Walter, V.: Object-based classification of remote sensing data for change detection. J. Photogramm Remote Sens. 58, 225–238 (2004)

    Article  Google Scholar 

  45. Xu, S., Zhang, H., Yang, Z.: GPS Measuring Principle and Application, 3rd edn., pp. 1–10. Wuhan University of Technology Press, Wuhan (2008)

    Google Scholar 

  46. Zheng, Q., Huang, W., Cui, X., Shi, Y., Liu, L.: New spectral index for detecting wheat yellow rust using sentinel-2 multispectral imagery. Sensors 18, 868 (2018)

    Article  Google Scholar 

  47. Zhou, X.C., Chen, Y.M., Zhu, X.H.: A kind of agricultural internet of things big data platform architecture. Anhui Agric. Sci. 47(2), 241–245 (2019)

    Google Scholar 

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Author information

Authors and Affiliations

  1. ICAR-National Bureau of Soil Survey and Land Use Planning, Amravati Road, Nagpur, 440033, India

    G. P. Obi Reddy & B. S. Dwivedi

  2. ICAR-Central Research Institute for Dryland Agriculture, Santhosh Nagar, Hyderabad, 500059, India

    G. Ravindra Chary

Authors
  1. G. P. Obi Reddy
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  2. B. S. Dwivedi
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  3. G. Ravindra Chary
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Corresponding author

Correspondence to G. P. Obi Reddy .

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Editors and Affiliations

  1. Department of Agricultural Biology, University of Jaffna, Jaffna, Sri Lanka

    Kandiah Pakeerathan

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Obi Reddy, G.P., Dwivedi, B.S., Ravindra Chary, G. (2023). Applications of Geospatial and Big Data Technologies in Smart Farming. In: Pakeerathan, K. (eds) Smart Agriculture for Developing Nations. Advanced Technologies and Societal Change. Springer, Singapore. https://doi.org/10.1007/978-981-19-8738-0_2

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  • DOI: https://doi.org/10.1007/978-981-19-8738-0_2

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  • Print ISBN: 978-981-19-8737-3

  • Online ISBN: 978-981-19-8738-0

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