A Novel Model Fusion Approach for Greenhouse Crop Yield Prediction
نویسندگان
چکیده
In this work, we have proposed a novel methodology for greenhouse tomato yield prediction, which is based on hybrid of an explanatory biophysical model—the Tomgro model, and machine learning model called CNN-RNN. The CNN-RNN models are calibrated/trained predicting yields while different fusion approaches (linear, Bayesian, neural network, random forest gradient boosting) exploited fusing the prediction result individual obtaining final results. experimental results shown that approach achieves more accurate than or model. Moreover, out approaches, network one produced most results, with means standard deviations root mean square error (RMSE), r2-coefficient, Nash-Sutcliffe efficiency (NSE) percent bias (PBIAS) being 17.69 ± 3.47 g/m2, 0.9995 0.0002, 0.9989 0.0004 0.1791 0.6837, respectively.
منابع مشابه
Quantifying Crop Yield, Bioenergy Production And Greenhouse Gas Emissions From Cropland And Marginal Land Using A Model-Data Fusion Approach
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ژورنال
عنوان ژورنال: Horticulturae
سال: 2022
ISSN: ['2311-7524']
DOI: https://doi.org/10.3390/horticulturae9010005