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Analyzing Monitoring and Controlling Techniques for Water Optimization Used in Precision Irrigation

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Advanced Information Networking and Applications (AINA 2024)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 204))

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Abstract

Agriculture is the primary food source, pivotal in meeting the global population’s food and nutritional requirements. With the current surge in population, the challenge of feeding such a vast number becomes increasingly daunting. Smart and precision farming is a crucial solution to address this challenge effectively. Given that farming inherently relies on water, optimizing its usage is imperative. This paper thoroughly reviews various techniques for optimizing water to ensure judicious water use. The document delves into diverse strategies, encompassing crop, soil, and weather-based management and monitoring approaches geared towards efficiently utilizing water resources. Additionally, the paper explores the application of machine learning and deep learning techniques in optimizing water usage, providing a detailed analysis of the results. Serving as a comprehensive guide, this paper illuminates the multitude of tools, techniques, and methodologies that form the foundation of smart and precision farming practices.

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References

  1. Alam, A., Biswas, S., Satpati, L.: Population dynamics and its impact: a historical perspective. In: Population, Sanitation and Health: A Geographical Study Towards Sustainability, pp. 3–15. Springer, Cham (2023)

    Google Scholar 

  2. Abioye, E.A., et al.: A review on monitoring and advanced control strategies for precision irrigation. Comput. Electron. Agricult. 173, 105441 (2020)

    Article  Google Scholar 

  3. Liao, R., Zhang, S., Zhang, X., Wang, M., Huarui, W., Zhangzhong, L.: Development of smart irrigation systems based on real-time soil moisture data in a greenhouse: proof of concept. Agric. Water Manag. 245, 106632 (2021)

    Article  Google Scholar 

  4. Singh, D.K., Sobti, R.: Long-range real-time monitoring strategy for Precision Irrigation in urban and rural farming in society 5.0. Comput. Indust. Eng 167, 107997 (2022)

    Article  Google Scholar 

  5. Bwambale, E., Abagale, F.K., Anornu, G.K.: Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: a review. Agricult. Water Manag. 260, 107324 (2022)

    Article  Google Scholar 

  6. Wheeler, W.D., Chappell, M., van Iersel, M., Thomas, P.: Implementation of soil moisture sensor based automated irrigation in woody ornamental production. J. Environ. Horticult. 38(1), 1–7 (2020)

    Article  Google Scholar 

  7. Millán, S., Casadesús, J., Campillo, C., Moñino, M.J., Prieto, M.H.: Using soil moisture sensors for automated irrigation scheduling in a plum crop. Water 11(10), 2061 (2019)

    Article  Google Scholar 

  8. Sui, R.: Irrigation scheduling using soil moisture sensors. J. Agric. Sci 10, 1 (2017)

    Google Scholar 

  9. Krishnan, R.S., Golden Julie, E., Harold Robinson, Y., Raja, S., Kumar, R., Thong, P.H.: Fuzzy logic based smart irrigation system using internet of things. J. Clean. Prod. 252, 119902 (2020)

    Article  Google Scholar 

  10. Masina, M., Calone, R., Barbanti, L., Mazzotti, C., Lamberti, A., Speranza, M.: Smart water and soil-salinity management in agro-wetlands. Environ. Eng. Manag. J 18, 10 (2019)

    Google Scholar 

  11. Kumawat, S., Bhamare, M., Nagare, A., Kapadnis, A.: Sensor based automatic irrigation system and soil pH detection using image processing. Int. Res. J. Eng. Technol 4, 3673–3675 (2017)

    Google Scholar 

  12. Rowe, R. O. S. I. A.: Soil moisture. Biosyst. Eng. (2018)

    Google Scholar 

  13. Richards, L.A.: Methods of measuring soil moisture tension. Soil Science 68, 1 (1949)

    Article  Google Scholar 

  14. Orouskhani, E., Sahoo, S.R., Agyeman, B.T., Bo, S., Liu, J.: Impact of sensor placement in soil water estimation: a real-case study. arXiv preprint arXiv:2203.06548 (2022)

  15. Zhu, H.-H., Huang, Y.-X., Huang, H., Garg, A., Mei, G.-X., Song, H.-H.: Development and evaluation of Arduino-based automatic irrigation system for regulation of soil moisture. Int. J. Geosynth. Ground Eng. 8(1), 1–9 (2022)

    Article  Google Scholar 

  16. Kuiper, P.J.C.: Water uptake of higher plants as affected by root temperature. No. 64-4. Veenman (1964)

    Google Scholar 

  17. Sawan, Z.M.: Climatic variables: evaporation, sunshine, relative humidity, soil and air temperature and its adverse effects on cotton production. Inf. Process. Agricult. 5(1), 134–148 (2018)

    MathSciNet  Google Scholar 

  18. Schmugge, T.J., Jackson, T.J., McKim, H.L.: Survey of methods for soil moisture determination. Water Resour. Res. 16(6), 961–979 (1980)

    Article  Google Scholar 

  19. Su, S.L., Singh, D.N., Baghini, M.S.: A critical review of soil moisture measurement. Measurement 54, 92–105 (2014)

    Article  Google Scholar 

  20. Sinha, B.B., Dhanalakshmi, R.: Recent advancements and challenges of Internet of Things in smart agriculture: a survey. Future Gen. Comput. Syst. 126, 169–184 (2022)

    Article  Google Scholar 

  21. Goap, A., Sharma, D., Krishna Shukla, A., Rama Krishna, C.: An IoT based smart irrigation management system using machine learning and open source technologies. Comput. Electron. Agricult. 155, 41–49 (2018)

    Article  Google Scholar 

  22. Al-Ali, A.-R., Qasaimeh, M., Al-Mardini, M., Radder, S., Zualkernan, I.A.: ZigBee-based irrigation system for home gardens. In: 2015 International Conference on Communications, Signal Processing, and their Applications (ICCSPA 2015), pp. 1-5. IEEE (2015)

    Google Scholar 

  23. Varatharajalu, K., Ramprabu, J.: Wireless irrigation system via phone call and SMS. Int. J. Eng. Adv. Technol 8, 397–401 (2018)

    Google Scholar 

  24. Sami, M., et al.: A deep learning-based sensor modeling for smart irrigation system. Agronomy 12(1), 212 (2022)

    Article  Google Scholar 

  25. Ali, S., et al.: Solar powered smart irrigation system. Pak. J. Eng. Technol. 5(1), 49–55 (2022)

    Article  MathSciNet  Google Scholar 

  26. White, S.C., Raine, S.R.: A grower guide to plant based sensing for irrigation scheduling (2008)

    Google Scholar 

  27. Davis, S.L., Dukes, M.D.: Irrigation scheduling performance by evapotranspiration-based controllers. Agric. Water Manag. 98(1), 19–28 (2010)

    Article  Google Scholar 

  28. Gutiérrez, J., Villa-Medina, J.F., Nieto-Garibay, A., Ángel Porta-Gándara, M.: Automated irrigation system using a wireless sensor network and GPRS module. IEEE Trans. Instrument. Measur. 63(1), 166–176 (2013)

    Article  Google Scholar 

  29. Jia, X., Huang, Y., Wang, Y., Sun, D.: Research on water and fertilizer irrigation system of tea plantation. Int. J. Distrib. Sens. Netw. 15(3), 1550147719840182 (2019)

    Article  Google Scholar 

  30. Dhillon, R., Francisco, R.O.J.O., Roach, J., Upadhyaya, S., Delwiche, M.: A continuous leaf monitoring system for precision irrigation management in orchard crops. Tarım Makinaları Bilimi Dergisi 10(4), 267–272 (2014)

    Google Scholar 

  31. Viani, F., Bertolli, M., Salucci, M., Polo, A.: Low-cost wireless monitoring and decision support for water saving in agriculture. IEEE Sens. J. 17(13), 4299–4309 (2017)

    Article  Google Scholar 

  32. Ullah, R., et al.: EEWMP: an IoT-based energy-efficient water management platform for smart irrigation. Scientific Program. 2021, 1–9 (2021)

    Article  Google Scholar 

  33. Kanade, P., Prasad, J.P.: Arduino based machine learning and IOT Smart Irrigation System. Int. J. Soft Comput. Eng. 10(4), 1–5 (2021)

    Article  Google Scholar 

  34. Pandey, A.K., Mukherjee, A.: A review on advances in IoT-based technologies for smart agricultural system. Internet of Things Analyt. Agricult. 3, 29–44 (2022)

    Google Scholar 

  35. El Mezouari, A., El Fazziki, A., Sadgal, M.: Hadoop-Spark framework for machine learning-based smart irrigation planning. SN Comput. Sci. 3(1), 1–10 (2022)

    Article  Google Scholar 

  36. Lozoya, C., Eyzaguirre, E., Espinoza, J., Montes-Fonseca, S.L., Rosas-Perez, G.: Spectral vegetation index sensor evaluation for greenhouse precision agriculture. In: 2019 IEEE Sensors, pp. 1–4. IEEE (2019)

    Google Scholar 

  37. Cecilia, B., et al.: On-line monitoring of plant water status: validation of a novel sensor based on photon attenuation of radiation through the leaf. Sci. Total Environ. 817, 152881 (2022)

    Google Scholar 

  38. Kılkış, Ş: Sustainable development of energy, water and environment systems index for Southeast European cities. J. Clean. Prod. 130, 222–23 (2016)

    Article  Google Scholar 

  39. Suzuki, Y., Ibayashi, H., Mineno, H.: An SVM based irrigation control system for home gardening. In: 2013 IEEE 2nd Global Conference on Consumer Electronics (GCCE), pp. 365–366. IEEE (2013)

    Google Scholar 

  40. Kumar, A., Surendra, A., Mohan, H., Muthu Valliappan, K., Kirthika, N.: Internet of things based smart irrigation using regression algorithm. In: 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), pp. 1652–1657. IEEE (2017)

    Google Scholar 

  41. Kumar, G.: Research paper on water irrigation by using wireless sensor network. Int. J. Sci. Res. Eng. Technol. 3–4 (2014)

    Google Scholar 

  42. Shekhar, Y., Dagur, E., Mishra, S., Sankaranarayanan, S.: Intelligent IoT-based automated irrigation system. Int. J. Appl. Eng. Res. 12(18), 7306–7320 (2017)

    Google Scholar 

  43. Poornima, D., Arulselvi, G.: Implementation of precision soil and water conservation agriculture (PSWCA) through machine learning, cloud-enabled IoT integration and wireless sensor network. Eur. J. Molecul. Clin. Med. 7, 3 (2020)

    Google Scholar 

  44. Glória, A., Cardoso, J., Sebastião, P.: Sustainable irrigation system for farming supported by machine learning and real-time sensor data. Sensors 21(9), 3079 (2021)

    Article  Google Scholar 

  45. Subathra, M.S.P., Blessing, C.J., Thomas George, S., Thomas, A., Dhibak Raj, A., Ewards, V.: Automated intelligent wireless drip irrigation using ANN techniques. In: Peter, J.D., Alavi, A.H., Javadi, B. (eds.) Advances in Big Data and Cloud Computing: Proceedings of ICBDCC18, pp. 555–568. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1882-5_49

  46. Monem, M.J., Hashemi, S.M.: Spatial clustering of irrigation networks using K-means method (Case study of Ghazvin irrigation network). Iran-Water Resour. Res. 7(1), 38–46 (2010)

    Google Scholar 

  47. Yashaswini, L.S., Vani, H.U., Sinchana, H.N., Kumar, N.: Smart automated irrigation system with disease prediction. In: 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), pp. 422–427. IEEE (2017)

    Google Scholar 

  48. Albuquerque, C.K.G., Polimante, S., Torre-Neto, A., Prati, R.C.: Water spray detection for smart irrigation systems with mask r-cnn and UAV footage. In: 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pp. 236–240. IEEE (2020)

    Google Scholar 

  49. Anuşlu, T.: Smart precision agriculture with autonomous irrigation system using rnn-based techniques (2017)

    Google Scholar 

  50. Kumar, S., Mishra, S., Khanna, P.: Precision sugarcane monitoring using SVM classifier. Procedia Comput. Sci. 122, 881–887 (2017)

    Article  Google Scholar 

  51. Ramya, S., Swetha, A.M., Doraipandian, M.: IoT framework for smart irrigation using machine learning technique. J. Comput. Sci. 16(3), 355–363 (2020)

    Article  Google Scholar 

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Authors and Affiliations

  1. SLIET, Longowal, Sangrur, Punjab, India

    Rajni Goyal, Amar Nath & Utkarsh Niranjan

  2. IIT Roorkee, Roorkee, 247667, India

    Rajdeep Niyogi

Authors
  1. Rajni Goyal
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  2. Amar Nath
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  3. Utkarsh Niranjan
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  4. Rajdeep Niyogi
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Corresponding author

Correspondence to Rajni Goyal .

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Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

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Goyal, R., Nath, A., Niranjan, U., Niyogi, R. (2024). Analyzing Monitoring and Controlling Techniques for Water Optimization Used in Precision Irrigation. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 204. Springer, Cham. https://doi.org/10.1007/978-3-031-57942-4_17

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