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Machine learning based crop water demand forecasting using minimum climatological data

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Abstract

Rice is one of the world’s most popular food crops. Since its production is dependent on intensive water use, water management is critical to ensure sustainability of water resource. However, very limited data is available on water use in rice irrigation. In the present study, traditional machine learning methods have been used to predict the irrigation schedule of rice daily. The data of year 2013-2015 is used to train the models and to further optimise it. The data of 2016-2017 is used for testing the models. Correlation thresholds are used for feature selection which helps in reducing the number of input parameters from the initial 26 to final 11. The models estimated the crop water demand as a function of weather parameters. Results show that Adaboost performed consistently well with an average accuracy of 71% as compared to other models for predicting the irrigation schedule.

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References

  1. Abdullah SS, Malek MA, Abdullah NS, Kisi O, Yap KS (2015) Extreme learning machines: a new approach for prediction of reference evapotranspiration. J Hydrol 527:184–195

    Google Scholar 

  2. Abrisqueta I, Conejero W, Valdés-Vela M, Vera J, Ortuño M F, Carmen Ruiz-Sánchez M (2015) Stem water potential estimation of drip-irrigated early-maturing peach trees under mediterranean conditions. Comput Electron Agric 114:7–13

    Google Scholar 

  3. Ahlawat IPS, Sharma RP (1993) Agronomic terminology

  4. Allen RG, Pereira LS, Raes D, Smith M, et al. (1998) Crop evapotranspiration-guidelines for computing crop water requirements-fao irrigation and drainage paper 56. Fao, Rome 300(9):D05109

    Google Scholar 

  5. Amer KH, Samak AA, Hatfield JL (2016) Effect of irrigation method and non-uniformity of irrigation on potato performance and quality. J Water Resour Protect 8(03):277

    Google Scholar 

  6. Andriyas S, McKee M (2013) Recursive partitioning techniques for modeling irrigation behavior. Environ Modell Softw 47:207–217

    Google Scholar 

  7. Bausch WC, Neale CMU (1987) Crop coefficients derived from reflected canopy radiation: a concept. Trans ASAE 30(3):703–0709

    Google Scholar 

  8. Belder P, Bouman BAM, Cabangon R, Guoan L u, Quilang EJP, Li Yuanhua, Spiertz JHJ, Tuong TP (2004) Effect of water-saving irrigation on rice yield and water use in typical lowland conditions in asia. Agric Water Manag 65 (3):193–210

    Google Scholar 

  9. Bouman B (2009) How much water does rice use. Management 69:115–133

    Google Scholar 

  10. Campos I, Balbontín C, González-Piqueras J, González-Dugo MP, Neale CMU, Calera A (2016) Combining a water balance model with evapotranspiration measurements to estimate total available soil water in irrigated and rainfed vineyards. Agric Water Manag 165:141–152

    Google Scholar 

  11. Djaman K, Mel VC, Bado BV, Manneh B, Diop L, Mutiibwa D, Rudnick DR, Irmak S, Futakuchi K et al (2017) Evapotranspiration, irrigation water requirement, and water productivity of rice (oryza sativa l.) in the sahelian environment. Paddy Water Environ 15(3):469–482

    Google Scholar 

  12. El-Magd IA, Tanton T (2005) Remote sensing and gis for estimation of irrigation crop water demand. Int J Remote Sens 26(11):2359–2370

    Google Scholar 

  13. Garg KK, Das BS, Safeeq M, Bhadoria PBS (2009) Measurement and modeling of soil water regime in a lowland paddy field showing preferential transport. Agric Water Manag 96(12):1705–1714

    Google Scholar 

  14. Giusti E, Marsili-Libelli S (2015) A fuzzy decision support system for irrigation and water conservation in agriculture. Environ Modell Softw 63:73–86

    Google Scholar 

  15. González Perea R, Camacho Poyato E, Montesinos P, Rodríguez Díaz JA (2019) Prediction of irrigation event occurrence at farm level using optimal decision trees. Comput Electron Agric 157:173–180

    Google Scholar 

  16. Goumopoulos C, O’Flynn B, Kameas A (2014) Automated zone-specific irrigation with wireless sensor/actuator network and adaptable decision support. Comput Electron Agric 105:20–33

    Google Scholar 

  17. Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1(2):96–99

    Google Scholar 

  18. Jensen ME, Wright JL, Pratt BJ (1971) Estimating soil moisture depletion from climate, crop and soil data. Trans ASAE 14(5):954–959

    Google Scholar 

  19. Jones HG (2004) Irrigation scheduling: advantages and pitfalls of plant-based methods. J Exper Botany 55(407):2427–2436

    Google Scholar 

  20. Karandish F, Simnek J (2016) A comparison of numerical and machine-learning modeling of soil water content with limited input data

  21. Khan MA, Islam MZ, Hafeez M (2011) Irrigation water requirement prediction through various data mining techniques applied on a carefully pre-processed dataset. J Res Pract Inf Technol 43(22):1–17

    Google Scholar 

  22. Kukal SS, Hira GS, Sidhu AS (2005) Soil matric potential-based irrigation scheduling to rice (oryza sativa). Irrig Sci 23(4):153–159

    Google Scholar 

  23. Li T, Humphreys E, Gill G, Kukal SS, et al. (2011) Evaluation and application of oryza2000 for irrigation scheduling of puddled transplanted rice in north west india. Field Crop Res 122(2):104–117

    Google Scholar 

  24. Maton L, Leenhardt D, Goulard M, Bergez J-E (2005) Assessing the irrigation strategies over a wide geographical area from structural data about farming systems. Agric Syst 86(3):293–311

    Google Scholar 

  25. Mimi Z, Smith M (2000) Statistical domestic water demand model for the west bank. Water Int 25(3):464–468

    Google Scholar 

  26. Monteith JL (1965) Evaporation and environment. The stage and movement of water in living organisms. In: 19th Symp. soc. exp. biol. Cambridge University Press

  27. Navarro-Hellín H, Martínez-del Rincon J, Domingo-Miguel R, Soto-Valles F, Torres-Sánchez R (2016) A decision support system for managing irrigation in agriculture. Comput Electron Agric 124:121–131

    Google Scholar 

  28. Ojha T, Misra S, Raghuwanshi NS (2015) Wireless sensor networks for agriculture: the state-of-the-art in practice and future challenges. Comput Electron Agric 118:66–84

    Google Scholar 

  29. Penman HL (1948) Natural evaporation from open water, bare soil and grass. Proc R Soc London. Series A Math Phys Sci 193(1032):120–145

    Google Scholar 

  30. Pulido-Calvo I, Gutierrez-Estrada JC (2009) Improved irrigation water demand forecasting using a soft-computing hybrid model. Biosyst Eng 102(2):202–218

    Google Scholar 

  31. Saleem SK, Delgoda DK, Ooi SK, Dassanayake KB, Liu L, Halgamuge MN, Malano H (2013) Model predictive control for real-time irrigation scheduling. IFAC Proc Vol 46(18):299–304

    Google Scholar 

  32. Sreekanth MS, Rajesh R, Satheeshku J (2015) Extreme learning machine for the classification of rainfall and thunderstorm. J Appl Sci 15(1):153–156

    Google Scholar 

  33. Steduto P, Hsiao TC, Raes D, Fereres E (2009) Aquacrop—the fao crop model to simulate yield response to water: I. Concepts and underlying principles. Agron J 101(3):426–437

    Google Scholar 

  34. Steppe K, De Pauw DJW, Lemeur R (2008) A step towards new irrigation scheduling strategies using plant-based measurements and mathematical modelling. Irrig Sci 26(6):505

    Google Scholar 

  35. Timsina J, Connor DJ (2001) Productivity and management of rice–wheat cropping systems: issues and challenges. Field Crops Res 69(2):93–132

    Google Scholar 

  36. Torres AF, Walker WR, McKee M (2011) Forecasting daily potential evapotranspiration using machine learning and limited climatic data. Agric Water Manag 98(4):553–562

    Google Scholar 

  37. Valdés-Vela M, Abrisqueta I, Conejero W, Vera J, Carmen Ruiz-Sánchez M (2015) Soft computing applied to stem water potential estimation: a fuzzy rule based approach. Comput Electron Agric 115:150–160

    Google Scholar 

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Authors and Affiliations

  1. Thapar Institute of Engineering and Technology, Patiala, India

    Ravneet Kaur Sidhu, Ravinder Kumar & Prashant Singh Rana

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  1. Ravneet Kaur Sidhu
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  2. Ravinder Kumar
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  3. Prashant Singh Rana
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Correspondence to Ravneet Kaur Sidhu.

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Sidhu, R.K., Kumar, R. & Rana, P.S. Machine learning based crop water demand forecasting using minimum climatological data. Multimed Tools Appl 79, 13109–13124 (2020). https://doi.org/10.1007/s11042-019-08533-w

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  • DOI: https://doi.org/10.1007/s11042-019-08533-w

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