Advertisement

SAGRO-Lite: A Light Weight Agent Based Semantic Model for the Internet of Things for Smart Agriculture in Developing Countries

Chapter
  • 95 Downloads
Part of the Studies in Computational Intelligence book series (SCI, volume 941)

Abstract

The recent advancement of the Internet of Things (IoT) has led to the possibilities to process a large number of sensor data streams built upon large-scale IoT platforms. In developed countries IoT is already emerged successfully as a reasonable technique assuring the goal of self-complacency, hybrid and advanced decisions and computerization in the horticulture industry. Instant adoption of IoT in farming is impractical in developing nations because of less literacy, hesitance towards technology, smaller farm sizes and high cost of IoT farming solutions. Through a light weight IOT specifically focused on farming style of developing countries like India, farmers can increase their quality of farming by the use of this technology. The authors have developed a semantically enriched agent based model called Agent Based Semantic Model for Smart Agriculture, ABSMSA which uses SAGRO-Lite, a light weight ontology designed by the authors for specific farming characteristics in developing countries. The system uses two more ontologies the IoT-Lite and Complex Event Service Ontology (CESO) for semantic sensing and event recognition and handling.

References

  1. 1.
    Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)Google Scholar
  2. 2.
    Ray, P.P.: A survey on internet of things architectures. J. King Saud Univ.-Comput. Inf. Sci. 30(3), 291–319 (2018)Google Scholar
  3. 3.
    Elijah, O., et al.: An overview of Internet of Things (IoT) and data analytics in agriculture: benefits and challenges. IEEE Internet Things J. 5(5), 3758–3773 (2018)Google Scholar
  4. 4.
    Khanna, A., Kaur, S.: Evolution of Internet of Things (IoT) and its significant impact in the field of precision agriculture. Comput. Electron. Agric. 157, 218–231 (2019)Google Scholar
  5. 5.
    Luthra, S., et al.: Internet of Things (IoT) in agriculture supply chain management: a developing country perspective. In: Emerging Markets from a Multidisciplinary Perspective, pp. 209–220. Springer, Cham (2018)Google Scholar
  6. 6.
    Chandra, A., McNamara, K.E., Dargusch, P.: Climate-smart agriculture: perspectives and framings. Clim. Policy 18(4), 526–541 (2018)CrossRefGoogle Scholar
  7. 7.
    Lipper, L., et al.: Climate smart agriculture. Nat. Resour. Manag. Policy 52, 2018 (2018)Google Scholar
  8. 8.
    Salam, A., Shah, S.: Internet of things in smart agriculture: enabling technologies. (2019)Google Scholar
  9. 9.
    Vuran, M.C., et al.: Internet of underground things in precision agriculture: architecture and technology aspects. Ad Hoc Netw. 81, 160–173 (2018)Google Scholar
  10. 10.
    Keswani, B., et al.: Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms. Neural Comput. Appl. 31(1), 277–292 (2019)Google Scholar
  11. 11.
    Agoramoorthy, G.: Can India meet the increasing food demand by 2020? Futures 40(5), 503–506 (2008)CrossRefGoogle Scholar
  12. 12.
    Reddy, D.N., Mishra, S. (eds.): Agrarian Crisis in India. Oxford University Press, Oxford (2010)Google Scholar
  13. 13.
    Walter, A., et al.: Opinion: smart farming is key to developing sustainable agriculture. Proc. Natl. Acad. Sci. 114(24), 6148–6150 (2017)Google Scholar
  14. 14.
    Kpadonou, R.A.B., et al.: Advancing climate-smart-agriculture in developing drylands: joint analysis of the adoption of multiple on-farm soil and water conservation technologies in West African Sahel. Land Use Policy 61, 196–207 (2017)Google Scholar
  15. 15.
    Lakhwani, K., et al.: Development of IoT for smart agriculture a review. In: Emerging Trends in Expert Applications and Security, pp. 425–432. Springer, Singapore (2019)Google Scholar
  16. 16.
    Bermudez-Edo, M., et al.: IoT-Lite: a lightweight semantic model for the internet of things. In: 2016 International IEEE Conferences on Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), IEEE (2016)Google Scholar
  17. 17.
    Jara, A.J., et al.: Semantic web of things: an analysis of the application semantics for the iot moving towards the iot convergence. Int. J. Web Grid Serv. 10(2–3), 244–272 (2014)Google Scholar
  18. 18.
    Maliappis, M.T.: Applying an agricultural ontology to web-based applications. Int. J. Metadata Semant. Ontol. 4(1-2), 133–140 (2009)CrossRefGoogle Scholar
  19. 19.
    Beck, H.W., Kim, S., Hagan, D.: A crop-pest ontology for extension publications. Proceedings (2005)Google Scholar
  20. 20.
    Wang, Y., et al.: An ontology-based approach to integration of hilly citrus production knowledge. Comput. Electron. Agric. 113, 24–43 (2015)Google Scholar
  21. 21.
    Xie, N., Wang, W., Yang, Y.: Ontology-based agricultural knowledge acquisition and application. In: International Conference on Computer and Computing Technologies in Agriculture. Springer, Boston, MA (2007)Google Scholar
  22. 22.
    Arjun, K.M.: Indian agriculture-status, importance and role in Indian economy. Int. J. Agric. Food Sci. Technol. 4(4), 343–346 (2013)Google Scholar
  23. 23.
    Reich, D., et al.: Reconstructing Indian population history. Nature 461(7263), 489 (2009)Google Scholar
  24. 24.
    Chaurasia, V.B., Singh, M.: Step towards the improvement of Indian agriculture. In: 14th Annual Conference, pp. 61 (2018)Google Scholar
  25. 25.
    Bhojani, S.H., Patel, A.R.: Information technology: an arising concept in agriculture sector. J. Comput. Technol. Appl. 4(1), 23–27 (2019)Google Scholar
  26. 26.
    Kumar, Y., Singh, P.K.: To study the influence of insurance policy on the agriculture field and Indian economy: concept paper. In: Renewable Energy and its Innovative Technologies, pp. 13–24. Springer, Singapore (2019)Google Scholar
  27. 27.
    Verma, C., Pandey, R.: Big data representation for grade analysis through Hadoop framework. In: 2016 6th International Conference-Cloud System and Big Data Engineering (Confluence), IEEE (2016)Google Scholar
  28. 28.
    Kotwal, A., Ramaswami, B., Wadhwa, W.: Economic liberalization and Indian economic growth: what’s the evidence?. J. Econ. Lit. 49(4), 1152–99 (2011)Google Scholar
  29. 29.
    Pandey, R., Dwivedi, S.: Ontology description using owl to support semantic web applications. Int. J. Comput. Appl. 14(4), 30–33 (2011)Google Scholar
  30. 30.
    Postel, S., et al.: Drip irrigation for small farmers: a new initiative to alleviate hunger and poverty. Water Int. 26(1), 3–13 (2001)Google Scholar
  31. 31.
    Pandey, R., Dwivedi, S.: Interoperability between semantic web layers: a communicating agent approach. Int. J. Comput. Appl. 12(3), 0975–8887 (2010)Google Scholar
  32. 32.
    Pandey, M., Pandey, R.: JSON and its use in semantic web. Int. J. Comput. Appl. 164(11), 10–16 (2017)Google Scholar
  33. 33.
    Kuruvilla, A., Jacob, K.S.: Poverty, social stress and mental health. Indian J. Med. Res. 126(4), 273 (2007)Google Scholar
  34. 34.
    Kumari, Sneha, et al. “Sparql: semantic information retrieval by embedding prepositions. Int. J. Netw. Secur. Appl. 6(1), 49 (2014)Google Scholar
  35. 35.
    Pandey, R., Dwivedi, S.: RDF/RDF-S providing framework support to OWL ontologies. Int. J. Comput. Sci. Inf. Technol. 3(4) (2012)Google Scholar
  36. 36.
    Jagannathan, S., Priyatharshini, R.: Smart farming system using sensors for agricultural task automation. In: 2015 IEEE Technological Innovation in ICT for Agriculture and Rural Development (TIAR), IEEE (2015)Google Scholar
  37. 37.
    Channe, H., Kothari, S., Kadam, D.: Multidisciplinary model for smart agriculture using internet-of-things (IoT), sensors, cloud-computing, mobile-computing and big-data analysis. Int. J. Comput. Technol. Appl. 6(3), 374–382 (2015)Google Scholar
  38. 38.
    Khatri-Chhetri, A., et al.: Farmers’ prioritization of climate-smart agriculture (CSA) technologies. Agric. Syst. 151, 184–191 (2017)Google Scholar
  39. 39.
    Patil, A., et al.: Smart farming using Arduino and data mining. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), IEEE (2016)Google Scholar
  40. 40.
    Auernhammer, H.: Precision farming—the environmental challenge. Comput. Electron. Agric. 30(1-3), 31–43 (2001)CrossRefGoogle Scholar
  41. 41.
    Katyal, N., Pandian, B.J.: A comparative study of conventional and smart farming. In: Emerging Technologies for Agriculture and Environment, pp. 1–8. Springer, Singapore (2020)Google Scholar
  42. 42.
    Bronson, K.: Looking through a responsible innovation lens at uneven engagements with digital farming. NJAS-Wageningen J. Life Sci. (2019)Google Scholar
  43. 43.
    Carolan, M.: Publicising food: big data, precision agriculture, and co‐experimental techniques of addition. Sociologia Ruralis 57(2), 135–154 (2017)Google Scholar
  44. 44.
    Popović, T., et al.: Architecting an IoT-enabled platform for precision agriculture and ecological monitoring: a case study. Comput. Electron. Agric. 140, 255–265 (2017)Google Scholar
  45. 45.
    Atzori, L., Iera, A., Morabito, G.: Understanding the Internet of Things: definition, potentials, and societal role of a fast evolving paradigm. Ad Hoc Netw. 56, 122–140 (2017)Google Scholar
  46. 46.
    Kamilaris, A., et al.: Agri-IoT: a semantic framework for Internet of Things-enabled smart farming applications. In: 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), IEEE (2016)Google Scholar
  47. 47.
    Ilapakurti, A., Vuppalapati, C.: Building an IoT framework for connected dairy. In: 2015 IEEE First International Conference on Big Data Computing Service and Applications, IEEE (2015)Google Scholar
  48. 48.
    Madsen, S.L., et al.: Quantifying behaviour of dairy cows via multi-stage support vector machines: book of proceedings. In: 8th European Conference on Precision Livestock Farming (2017)Google Scholar
  49. 49.
    Sinha, R.S., Wei, Y., Hwang, S.-H.: A survey on LPWA technology: LoRa and NB-IoT. Ict Express 3(1), 14–21 (2017)CrossRefGoogle Scholar
  50. 50.
    Pham, C., Rahim, A., Cousin, P.: Low-cost, long-range open IoT for smarter rural African villages. In: 2016 IEEE International Smart Cities Conference (ISC2), IEEE (2016)Google Scholar
  51. 51.
    Shaikh, F.K., Zeadally, S.: Energy harvesting in wireless sensor networks: a comprehensive review. Renew. Sustain. Energy Rev. 55, 1041–1054 (2016)Google Scholar
  52. 52.
    Wen, Z., et al.: Self-powered textile for wearable electronics by hybridizing fiber-shaped nanogenerators, solar cells, and supercapacitors. Sci. Adv. 2(10), e1600097 (2016)Google Scholar
  53. 53.
    Francesco, A.,et al.: Combined finite–discrete numerical modeling of runout of the Torgiovannetto di Assisi rockslide in central Italy. Int. J. Geomech. 16(6), 04016019 (2016)Google Scholar
  54. 54.
    Wong, B.P., Kerkez, B.: Real-time environmental sensor data: an application to water quality using web services. Environ. Model. Softw. 84, 505–517 (2016)Google Scholar
  55. 55.
    Murphy, E., et al.: Diet of stoats at Okarito Kiwi Sanctuary, South Westland, New Zealand. N. Z. J. Ecol. 41–45 (2008)Google Scholar
  56. 56.
    Singh, H., Sarangi, S.C., Gupta, Y.K.: French Phase I clinical trial disaster: issues, learning points, and potential safety measures. J. Nat. Sci. Biol. Med. 9(2), 106 (2018)Google Scholar
  57. 57.
    Ruan, J., Shi, Y.: Monitoring and assessing fruit freshness in IOT-based e-commerce delivery using scenario analysis and interval number approaches. Inf. Sci. 373, 557–570 (2016)CrossRefGoogle Scholar
  58. 58.
    Liu, Y., et al.: An Internet-of-Things solution for food safety and quality control: a pilot project in China. J. Ind. Inf. Integr. 3, 1–7 (2016)Google Scholar
  59. 59.
    Kant, G.S., Singh, V.K., Darbari, M.: Legal semantic web-a recommendation system. IJAIS 7(3) (2014)Google Scholar
  60. 60.
    Mishra, S.K., Singh, V.K., Shankhdhar, G.K.: Ontology development for wheat information system. IJRET-Int. J. Res. Eng. Technol. 04(05) (2015)Google Scholar
  61. 61.
    Verma, A., Shankhdhar, G.K., Darbari, M.: Verified message exchange in providing security for cloud computing in heterogeneous and dynamic environment. Int. J. Appl. Inf. Syst. 11(10), 15–18 (2017)Google Scholar
  62. 62.
    Garcia-Ojeda, J.C., et al.: O-MaSE: a customizable approach to developing multiagent development processes. In: International Workshop on Agent-Oriented Software Engineering. Springer, Berlin, Heidelberg (2007)Google Scholar
  63. 63.
    Shankhdhar, G.K., Verma, A., Singh, V.K., Darbari, M., Singh, V.: Application of IOT in electrical grid. IOSR J. Eng. ISSN (e): 2250–3021, ISSN (p): 2278-8719 08(4), 01–03 (2018)Google Scholar
  64. 64.
    Shankhdhar, G.K., Darbari, M.: Building custom, adaptive and heterogeneous multi-agent systems for semantic information retrieval using organizational-multi-agent systems engineering, O-MaSE. IEEE Explore, ISBN: 978-1-5090-3480-2 (2016)Google Scholar
  65. 65.
    Gao, F., Ali, M.I., Mileo, A.: Semantic discovery and integration of urban data streams⋆. Challenge 7, 16 (2014)Google Scholar
  66. 66.
    Shankhdhar, G.K., Darbari, M.: Introducing two level verification model for reduction of uncertainty of message exchange in inter agent communication in organizational-multi-agent systems engineering, O-MaSE. Int. Organ. Sci. Res. (2017).  https://doi.org/10.9790/0661-1904020818
  67. 67.
    Shankhdhar, G.K., Darbari, M.: Integrating COCOMO II model in O-MaSE methodology for estimating effort in building heterogeneous and dynamic multi-agent systems. Sci. Eng. Res. Support Soc. Int. J. Softw. Eng. Appl. 29–40Google Scholar
  68. 68.
    Shankhdhar, G.K., Darbari, M.: Implementation of validation of requirements in agent development by means of ontology. Int. J. Comput. Sci. Eng. 6, 1129–1135 (2018).  https://doi.org/10.26438/ijcse/v6i7.11291135
  69. 69.
    DeLoach, S.A., Garcia-Ojeda, J.C.: The o-masemethodology. In: Handbook on Agent-Oriented Design Processes, pp. 253–285. Springer, Berlin, Heidelberg (2014)Google Scholar
  70. 70.
    Garcia-Ojeda, J.C., DeLoach, S.A.: agentTool III: from process definition to code generation. In: Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems-Volume 2. International Foundation for Autonomous Agents and Multiagent Systems (2009)Google Scholar
  71. 71.
    Agarwal, R., et al.: Unified IoT ontology to enable interoperability and federation of testbeds. In: 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), IEEE (2016)Google Scholar
  72. 72.
    Seydoux, N., et al.: IoT-O, a core-domain IoT ontology to represent connected devices networks. In: European Knowledge Acquisition Workshop. Springer, Cham (2016)Google Scholar
  73. 73.
    Compton, M., et al.: The SSN ontology of the W3C semantic sensor network incubator group. Web Semant. Sci. Serv. Agents World Wide Web 17, 25–32 (2012)Google Scholar
  74. 74.
    Caracciolo, C., et al.: The AGROVOC linked dataset. Semant. Web 4(3), 341–348 (2013)Google Scholar
  75. 75.
    Lauser, B., et al.: From AGROVOC to the agricultural ontology service/concept server. An OWL model for creating ontologies in the agricultural domain. In: Dublin Core Conference Proceedings. Dublin Core DCMI (2006)Google Scholar
  76. 76.
    Hu, S., et al.: AgOnt: ontology for agriculture internet of things. In: International Conference on Computer and Computing Technologies in Agriculture. Springer, Berlin, Heidelberg (2010)Google Scholar
  77. 77.
    Barbieri, D.F., et al.: C-SPARQL: SPARQL for continuous querying. In: Te 18th international conference on World wide web-WWW’09 (2009)Google Scholar
  78. 78.
    Dao-Tran, Minh, and Danh Le Phuoc. “Towards Enriching CQELS with Complex Event Processing and Path Navigation.”HiDeSt@ KI. 2015Google Scholar
  79. 79.
    Fulton, M., Giannakas, K.: Organizational commitment in a mixed oligopoly: agricultural cooperatives and investor-owned firms. Am. J. Agric. Econ. 83(5), 1258–1265 (2001)Google Scholar
  80. 80.
    Patnaik, U.: Unbalanced growth, tertiarization of the Indian economy and implications for mass living standards. In: Towards Progressive Fiscal Policy in India. Sage Publications, New Delhi, pp. 299–325 (2011)Google Scholar
  81. 81.
    Pandey, R., Saxena, P., Tripathi, S.: Data interpretation for social network using R API. In: 2018 8th International Conference on Communication Systems and Network Technologies (CSNT), IEEE (2018)Google Scholar
  82. 82.
    Verma, C., Pandey, R.: Mobile cloud computing integrating cloud, mobile computing, and networking services through virtualization. In: Design and Use of Virtualization Technology in Cloud Computing. IGI Global, 140–160 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Department of Basic SciencesBabuBanarasi Das UniversityLucknowIndia
  2. 2.Department of Information TechnologyBabuBanarasi Das National Institute of Technology and ManagementLucknowIndia
  3. 3.Department of Computer ScienceBabuBanarasi Das UniversityLucknowIndia

Personalised recommendations