Design of decision support system to identify crop water need | Journal of Ambient Intelligence and Humanized Computing
Skip to main content
Springer Nature Link
Log in

Design of decision support system to identify crop water need

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Crop Water Need (ET crop) is referred to as the amount of water needed by a crop to grow. ET crop has high significance to identify the adequate amount of irrigation need. In this paper, a decision support system is proposed to identify Crop Water Need. The proposed decision support system is implemented through sensors and android based smartphone. Internet of Things (IoT) based temperature sensor (DHT11) is used to acquire the real time environmental factors that affect the ET crop. The sensor will communicate with android based smartphone application using Bluetooth Technology (BT-HC05). This proposed system has been compared with available evapotranspiration and existing manual method of evapotranspiration and it was found that proposed system is more correlated than existing manual method of evapotranspiration. The correlation coefficient obtained between proposed system and available evapotranspiration is 0.9783. The proposed decision support system is beneficial for farmers, agriculture researchers and professionals.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration—guidelines for computing crop water requirements. FAO irrigation and drainage paper 56, p 300 (D05109)

  • Anneketh V et al (2020) IoT and machine learning approaches for automation of farm irrigation system. Proc Comput Sci 167:1250–1257

    Article  Google Scholar 

  • Available Evapotranspiration (2021). http://www.myweather2.com/. Accessed 23 Sept 2018

  • Bartlett AC, Andales AA, Arabi M, Bauder TA (2015) A smartphone app to extend use of a cloud-based irrigation scheduling tool. Comput Electron Agric 111:127–130

    Article  Google Scholar 

  • Bhatnagar V, Poonia RC, Sunda S (2019) State of the art and gap analysis of precision agriculture: a case study of Indian farmers. Int J Agric Environ Inf Syst 10(3):72–92

    Article  Google Scholar 

  • Crop water need (2021). http://www.fao.org/docrep/S2022E/s2022e07.htm. Accessed 23 Sept 2018

  • Declination Angle (2021). https://www.pveducation.org/pvcdrom/properties-of-sunlight/declination-angle/. Accessed 23 Sept 2018

  • Dirk R et al (2009) AquaCrop-the FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agron J 101(3):438–447

    Article  Google Scholar 

  • Feddes RA (1988) Crop factors in relation to Makkink reference-crop evapotranspiration. FAO, Rome

    Google Scholar 

  • Gowda PH, Howell TA, Baumhardt RL, Porter DO, Marek TH, Nangia V (2016) A User-Friendly Interactive Tool for Estimating Reference ET Using ASCE Standardized Penman-Monteith equation. Appl Eng Agric 32:383–390

    Article  Google Scholar 

  • Kongsombut K, Chaisricharoen R (2013) Real-time advisory service for orchid care. In: 2013 13th International symposium on communications and information technologies (ISCIT), IEEE

  • Kumar R, Jat MK, Shankar V (2012) Methods to estimate irrigated reference crop evapotranspiration—a review. Water Sci Technol 66:525–535

    Article  Google Scholar 

  • Li C, Dutta R, Kloppers C, D’Este C, Morshed A, Almeida A, Aryal J (2013) Mobile application based sustainable irrigation water usage decision support system: an intelligent sensor CLOUD approach. In: SENSORS. IEEE, pp 1–4

  • Mbabazi D et al (2017) An irrigation schedule testing model for optimization of the Smartirrigation avocado app. Agric Water Manage 179:390–400

    Article  Google Scholar 

  • Mohanraj I, Ashokumar K, Naren J (2016) Field monitoring and automation using IOT in agriculture domain. Proc Comput Sci 93:931–939

    Article  Google Scholar 

  • Mohapatra AG, Lenka SK, Keswani B (2019) Neural network and fuzzy logic based smart DSS model for irrigation notification and control in precision agriculture. Proc Natl Acad Sci India Sect A Phys Sci 89(1):67–76

    Article  Google Scholar 

  • Smith M, Allen R, Monteith JL, Perrier A, Segeren A (1992) Report. Expert consultation on revision of FAO methodologies for crop water requirements. Rome (Italy), 28–31 May 1990

  • Solar Angle (2021). http://www.itacanet.org/the-sun-as-a-source-of-energy/part-3-calculating-solar-angles/. Accessed 23 Sept 2018

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, CHRIST (Deemed to be University), Bangalore, Karnataka, 560029, India

    Ramesh Chandra Poonia

  2. Department of Computer Applications, Manipal University Jaipur, Jaipur, Rajasthan, India

    Vaibhav Bhatnagar

Authors
  1. Ramesh Chandra Poonia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vaibhav Bhatnagar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vaibhav Bhatnagar.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Poonia, R.C., Bhatnagar, V. Design of decision support system to identify crop water need. J Ambient Intell Human Comput 14, 5171–5178 (2023). https://doi.org/10.1007/s12652-021-03291-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-021-03291-w

Keywords

Search

Navigation