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

By Mandeep Kaur Saggi, Sushma Jain. 2020

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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.

Subfields: Artificial Intelligence, Plant Science, Soil Science

View Paper: https://www.semanticscholar.org/paper/4214e736ba082c285b3b0bc53424ea817292d32a