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Application of fuzzy-genetic and regularization random forest (FG-RRF): Estimation of crop evapotranspiration (ETc) for maize and wheat crops

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  • Saggi, Mandeep Kaur
  • Jain, Sushma

Abstract

Smart farming has played a significant role in decision support system to maximize the yield with minimum consumption of water in the field of agriculture. The main objective of this paper is to design and develop an innovative multilevel model ensembling for accurate estimation of crop coefficient (Kc) and reference evapotranspiration (ETc) using Fuzzy-Genetic (FG) and Regularization Random Forest(RRF) models. This study present the water requirement of three crops namely (maize, wheat1 and wheat2) in which ETc is a function of the product of the crop coefficient Kc and reference evapotranspiration (ETo). The proposed model is used to analyze the data collected by IMD, Pune and PAU, Ludhiana (case study) for decision making in a crop water model. The proposed FG-RRF(ETc) crop prediction model efficiently estimated Kc and ETc and make an efficient decision.

Suggested Citation

  • Saggi, Mandeep Kaur & Jain, Sushma, 2020. "Application of fuzzy-genetic and regularization random forest (FG-RRF): Estimation of crop evapotranspiration (ETc) for maize and wheat crops," Agricultural Water Management, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:agiwat:v:229:y:2020:i:c:s0378377419310054
    DOI: 10.1016/j.agwat.2019.105907
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    Cited by:

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    2. Gu, Nan & Zhang, Jianyun & Wang, Guoqing & Liu, Cuishan & Wang, Zhenlong & Lü, Haishen, 2022. "An atmospheric and soil thermal-based wheat crop coefficient method using additive crop growth models," Agricultural Water Management, Elsevier, vol. 269(C).
    3. Yamaç, Sevim Seda, 2021. "Artificial intelligence methods reliably predict crop evapotranspiration with different combinations of meteorological data for sugar beet in a semiarid area," Agricultural Water Management, Elsevier, vol. 254(C).

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