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Neural Computing and Applications

, Volume 31, Supplement 1, pp 277–292 | Cite as

Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms

  • Bright Keswani
  • Ambarish G. Mohapatra
  • Amarjeet Mohanty
  • Ashish Khanna
  • Joel J. P. C. Rodrigues
  • Deepak Gupta
  • Victor Hugo C. de AlbuquerqueEmail author
S.I. : Machine Learning Applications for Self-Organized Wireless Networks

Abstract

Precision agriculture is the mechanism which controls the land productivity and maximizes the revinue and minimizes the impact on sorroundings by automating the complete agriculture processes. This projected work relies on independent internet of things (IoT) enabled wireless sensor network (WSN) framework consisting of soil moisture (MC) probe, soil temperature measuring device, environmental temperature sensor, environmental humidity sensing device, CO2 sensor, daylight intensity device (light dependent resistor) to acquire real-time farm information through multi-point measurement. The projected observance technique consists of all standalone IoT-enabled WSN nodes used for timely data acquisitions and storage of agriculture information. The farm history is additionally stored for generating necessary action throughout the whole course of farming. The work summarizes the optimum usage of irrigation by the precise management of water valve using neural network-based prediction of soil water requirement in 1 h ahead. Our proposed irrigation control scheme utilizes structural similarity (SSIM)-based water valve management mechanism which is used to locate farm regions having water deficiency. Moreover, a close comparative study of optimization techniques, like variable learning rate gradient descent, gradient descent for feedforward neural network-based pattern classification, is performed and the best practice is employed to forecast soil MC on hourly basis together with interpolation method for generating soil moisture content (MC) distribution map. Finally, SSIM index-based soil MC deficiency is calculated to manipulate the specified valves for maintaining uniform water requirement through the entire farm area. The valve control commands are again processed using fuzzy logic-based weather condition modeling system to manipulate control commands by considering different weather conditions.

Keywords

Soil moisture content Wireless sensor network Internet of things Variable learning rate gradient descent Gradient descent Structural similarity index (SSIM) Interpolation Fuzzy logic 

References

  1. 1.
    Panchard J, Hubaux J-P (2008) Wireless sensor networks for marginal farming in India, Ph.D. thesis, EPFL, Lausanne. http://dx.doi.org/10.5075/epfl-thesis-4172
  2. 2.
    Gupta D, Julka A, Jain S, Aggarwal T, Khanna A, de Albuquerque VHC (2018) Optimized cuttlefish algorithm for diagnosis of parkinson’s disease. Cogn Syst Res 52:36–48.  https://doi.org/10.1016/j.cogsys.2018.06.006 (ISSN: 1389-0417 SCIE (IF 1.18))CrossRefGoogle Scholar
  3. 3.
    Khanna A, Singh AK, Swaroop A (2016) A token based solution to group local mutual exclusion problem in mobile Ad Hoc networks. Arab J for Sci Eng 41(12):5181–5194 (ISSN 2193-567X)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Lakshmanaprabu SK, Shankar K, Gupta D, Khanna A, Rodrigues JJPC, Plácido RP, de Albuquerque VHC (2018) Ranking analysis for online customer reviews of products using opinion mining with clustering. Complexity 2018:1–9.  https://doi.org/10.1155/2018/3569351 (SCIE (IF 4.62)) CrossRefGoogle Scholar
  5. 5.
    Ashish Khanna (2017) Image segmentation and edge detection neuro-fuzzy logic system. Int J Adv Res Trends Eng Technol (IJARTET) 47–54 (ISSN 2394-3785)Google Scholar
  6. 6.
    Rodrigues JJPC, Segundo DPDR, Junqueira HA, Sabino MH, Prince RM, Al-Muhtadi J, De Albuquerque VHC (2018) Enabling technologies for the internet of health things. IEEE Access, vol 6, pp 13129–13141Google Scholar
  7. 7.
    da Cruz MA, Rodrigues JJPC, Al-Muhtadi J, Korotaev VV, de Albuquerque VHC (2018) A reference model for internet of things middleware. IEEE Internet Things J 5:871–883CrossRefGoogle Scholar
  8. 8.
    Lakshmanaprabu SK, Shankar K, Khanna A, Gupta D, Rodrigues JJPC, Pinheiro PR, De Albuquerque VHC (2018) Effective features to classify big data using social internet of things. IEEE Access, vol 6, pp 24196–24204Google Scholar
  9. 9.
    Mahmoud MME, Rodrigues JJPC, Ahmed SH, Shah SC, Al-Muhtadi JF, Korotaev VV, De Albuquerque VHC (2018) Enabling technologies on cloud of things for smart healthcare. IEEE Access, vol 6, pp 31950–31967Google Scholar
  10. 10.
    Hussein AF, Kumar A, Burbano-Fernandez M, Ramirez-Gonzalez G, Abdulhay E, de Albuquerque VHC (2018) An automated remote cloud-based heart rate variability monitoring system. IEEE Accessed 2018Google Scholar
  11. 11.
    Liang R, Ding Y, Zhang X, Zhang W (2008) A real-time prediction system of soil moisture content using genetic neural-network based on annealing algorithm. In: IEEE international conference on automation and logistics (ICAL-2008), Computer and Information Engineering College, Hohai University, Changzhou, 1–3 Sept, pp 2781–2785Google Scholar
  12. 12.
    Khanna A, Singh AK, Swaroop A (2014) A leader-based k-local mutual exclusion algorithm using token for MANETs. J Inf Sci Eng 30(5):1303–1319 (SCI) (IF 0.54) MathSciNetGoogle Scholar
  13. 13.
    Gupta D, Shirish Sundaram, Ashish Khanna, Aboul Ella Hassanien, Victor Hugo C de Albuquerque (2018) Improved diagnosis of parkinson’s disease based on optimized crow search algorithm. Comput Electr Eng, SCIE (IF 1.57)Google Scholar
  14. 14.
    Mohapatra AG, Lenka SK (2016) Hybrid decision model for weather dependent farm irrigation using resilient backpropagation based neural network pattern classification and fuzzy logic. In: Proceedings of the Springer smart innovation, systems and technologies (SIST) Book series, Chapter 30, pp 1–12Google Scholar
  15. 15.
    Mohapatra AG, Keswani B, Lenka SK (2018) Neural network and fuzzy logic based smart DSS model for irrigation notification and control in precision agriculture. In: Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, Springer, Berlin, pp 1–10.  https://doi.org/10.1007/s40010-017-0401-6
  16. 16.
    Mohapatra AG, Keswani B, Lenka SK (2017) Optimizing farm irrigation mechanism using feedforward neural networkand structural similarity index. Int J Comput Appl 4(7):135–141Google Scholar
  17. 17.
    Mohapatra AG, Keswani B, Lenka SK (2017) Soil n-p-k prediction using location and crop specific random forest classification technique in precision agriculture. Int J Adv Res Comput Sci 8:1–6CrossRefGoogle Scholar
  18. 18.
    Lakshmanaprabu SK, Shankar K, Khanna A, Gupta D, Rodrigues JJPC, Plácido RP, de Albuquerque VHC (2018) Effective features to classify big data using social internet of things. IEEE Accessed, SCIE (IF 3.24)Google Scholar
  19. 19.
    Majone B, Viani F, Filippi E, Bellin A, Massa A, Toller G, Robol F, Salucci M (2013) “WSN deployment for monitoring soil moisture dynamics at the field scale, International Conference on Four Decades of Progress in Monitoring and Modeling of Processes in Soil Plant Atmosphere System: Applications and Challenges”, Procedia Environmental Sciences, vol 19, pp 426–435Google Scholar
  20. 20.
    Water Sense Labeled Weather-Based Irrigation Controllers (2012) WTR-SENS. pp 987–7367. http://www3.epa.gov/watersense/docs/irrigation_controller_rpt_minireport_508.pdf
  21. 21.
    Khanna A, Singh AK, Swaroop A (2015) Dynamic request set based mutual exclusion algorithm in MANETs. Int J Wirel Microw Technol (MECS) 5(4):1–14 [ISSN: 2076–1449 (Print), ISSN: 2076–9539 (Online)]CrossRefGoogle Scholar
  22. 22.
    Gómez-Melendez D, Lopez-Lambrantilde A, Herrera-Ruiz G, Fuentes C, Rico-Garcia E, Olvera-Olvera C, Alaniz-Lumbrerasc D, Teobaldis MF, Verlinden S (2011) Fuzzy irrigation greenhouse control system based on a field programmable gate array. Afr J Agric Res 6(11):2544–2557Google Scholar
  23. 23.
    Jia X, Yao L, Zhang Y (2011) Design of field integrative irrigation control system based on fuzzy control and PlC. Commun Comput Inf Sci 237:295–301Google Scholar
  24. 24.
    Schulze K, Spreer W, Keil A, Ongprasert S, Müller J (2013) Mango (Mangifera indica L. cv. Nam Dokmai) production in Northern Thailand—costs and returns under extreme weather conditions and different irrigation treatments. Agric Water Manag 126:46–55CrossRefGoogle Scholar
  25. 25.
    Poch-Massegú R, Jiménez-Martínez J, Wallis KJ, de Cartagena FR, Candela L (2014) Irrigation return flow and nitrate leaching under different crops and irrigation methods in Western Mediterranean weather conditions. Agric Water Manag 134:1–13CrossRefGoogle Scholar
  26. 26.
    Migliaccio KW, Morgan KT, Fraisse C, Vellidis G, Andreis JH (2015) Performance evaluation of urban turf irrigation smartphone app. Comput Electron Agric 118:136–142CrossRefGoogle Scholar
  27. 27.
    Khanna A, Singh AK, Swaroop A (2016) h-group local mutual exclusion algorithm in MANETs, CSI Transaction on the ICT (Springer), pp 227–234, ISSN 2277–9078Google Scholar
  28. 28.
    Lenka SK, Mohapatra AG (2016) Gradient descent with momentum based neural network pattern classification for the prediction of soil moisture content in precision agriculture. In: Proceedings of the IEEE international symposium on nanoelectronic and information systems (iNIS), 17 March 2016, pp 63–66Google Scholar
  29. 29.
    Zhengjun Q, Xiaoxing T, Jiehui S, Yidan B (2007) Irrigation decision-making system based on the fuzzy-control theory and virtual instrument. Nongye Gongcheng Xuebao/Trans Chin Soc Agric Eng 23(8):165–169Google Scholar
  30. 30.
    Mohapatra AG, KeswaniB Lenka SK (2018) ICT specific technological changes in precision agriculture environment. Int J Comput Sci Mob Appl 6:1–16Google Scholar
  31. 31.
    Svozil D, KvasniEka V, Pospichal J (1997) Introduction to multi-layer feed-forward neural networks. Chemom Intell Lab Syst 39:43–62CrossRefGoogle Scholar
  32. 32.
    Phillips AJ, Newlands NK, Liang SHL, Ellert BH (2014) Integrated sensing of soil moisture at the field scale: measuring, modeling and sharing for improved agricultural decision support. Comput Electron Agric 104:73–88CrossRefGoogle Scholar
  33. 33.
    Mohapatra AG, Lenka SK (2016) Hybrid decision support system using PLSR-fuzzy logic for GSM based site specific irrigation notification and control in precision agriculture. Int J Intell Syst Technol Appl Indersci 15(1):4–18Google Scholar
  34. 34.
    Forouzanfar M, Dajani HR, Groza VZ, Bolic M (2010) Comparison of feed-forward neural network training algorithms for oscillometric blood pressure estimation. In: 4th international workshop on soft computing applications (SOFA), Sch. of IT and Eng., Univ. of Ottawa, Ottawa, ON, Canada, 15–17 July 2010, pp 119–123Google Scholar
  35. 35.
    Zhang M, Li M, Wang W, Liu C, Gao H (2013) Temporal and spatial variability of soil moisture based on WSN. Math Comput Model 58(3–4):826–833CrossRefGoogle Scholar
  36. 36.
    Popa CA (2014) Enhanced gradient descent algorithms for complex-valued neural networks. In: 16th international symposium on symbolic and numeric algorithms for scientific computing. SYNASC 2014, Dept. of Comput. and Software Eng., Polytech. Univ. Timisoara, Timisoara, Romania, 22–25 Sept, pp 272–279Google Scholar
  37. 37.
    Zhang G-L, Wang Y, de Silva CW (2008) Multisensor gripper positioning in unstructured urban environments using neural network. In: IEEE international conference on automation and logistics (ICAL), Dept. of Mech. Eng., Univ. of British Columbia, BC Vancouver, 1–3 Sept, pp 1474–1479Google Scholar
  38. 38.
    Gupta A, Reddy BVR, Ghosh U, Khanna A (2012) A permission-based clustering mutual exclusion algorithm for mobile Ad-Hoc networks. Int J Eng Res Appl (IJERA) 2(4):19–26 (ISSN: 2248–9622)Google Scholar
  39. 39.
    Rehman A, Wang Z (2011) SSIM-based non-local means image denoising. In: 18th IEEE international conference on image processing, Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada, pp 217–220Google Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  • Bright Keswani
    • 1
  • Ambarish G. Mohapatra
    • 2
  • Amarjeet Mohanty
    • 3
  • Ashish Khanna
    • 4
  • Joel J. P. C. Rodrigues
    • 5
    • 6
    • 7
  • Deepak Gupta
    • 4
  • Victor Hugo C. de Albuquerque
    • 8
    Email author
  1. 1.Department of Computer ApplicationsSuresh Gyan Vihar UniversityJaipurIndia
  2. 2.Department of Electronics and Instrumentation EngineeringSilicon Institute of TechnologyBhubaneswarIndia
  3. 3.Department of Information TechnologySilicon Institute of TechnologyBhubaneswarIndia
  4. 4.Maharaja Agrasen Institute of TechnologyGGSIP UniversityDwarkaIndia
  5. 5.National Institute of Telecommunications (Inatel), Instituto de TelecomunicaçõesSanta Rita do Sapucaí/MGBrazil
  6. 6.Instituto de TelecomunicaçõesLisbonPortugal
  7. 7.ITMO UniversitySt. PetersburgRussia
  8. 8.Graduate Program in Applied InformaticsUniversity of FortalezaFortalezaBrazil

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