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• W4293090578 abstract "Crop yield and its prediction are crucial in agricultural production planning. This study investigates and predicts arabica coffee yield in order to match the market demand, using artificial neural networks (ANN) and multiple linear regression (MLR). Data of six variables, including areas, productivity zones, rainfalls, relative humidity, and minimum and maximum temperature, were collected for the recent 180 months between 2004 and 2018. The predicted yield of the cherry coffee crop continuously increases each year. From the dataset, it was found that the prediction accuracy of the R2 and RMSE from ANN was 0.9524 and 0.0784 tons, respectively. The ANN model showed potential in determining the cherry coffee yields." @default.
• W4293090578 created "2022-08-26" @default.
• W4293090578 creator A5043608804 @default.
• W4293090578 creator A5072708939 @default.
• W4293090578 creator A5076119263 @default.
• W4293090578 date "2022-08-25" @default.
• W4293090578 modified "2023-10-14" @default.
• W4293090578 title "Prediction of arabica coffee production using artificial neural network and multiple linear regression techniques" @default.
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• W4293090578 doi "https://doi.org/10.1038/s41598-022-18635-5" @default.
• W4293090578 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36008448" @default.
• W4293090578 hasPublicationYear "2022" @default.
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