AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
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Authors: ALINA DELIA CĂLIN, HOREA-BOGDAN MUREȘAN, ADRIANA MIHAELA COROIU.
DOI: 10.24193/subbi.2022.2.02
Published Online: 2023-02-06
pp. 21-36
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In this research, our objective is to identify the relationship between the date of seeding and the production of corn and sunflower crops. We evaluated the feasibility of using prediction models on a dataset of annual average crop yields and information on plant phenology, from several states of the US. After performing data analysis and preprocessing, we trained a selection of regression models. The best results were obtained for corn using HistGradientRegressor and XGBRegressor with R2 = 0.969 for both algorithms and MAE% = 8.945%, respectively MAE% = 9.423%. These results demonstrate a good potential for the problem of yield prediction based on year, state, average plating day, and crop type. This model will be further used, combined with meteorological data, to build an agricultural crop prediction model.
Received by the editors:
8 December 2022.
2010 Mathematics Subject Classification. 94A15, 94A99.
1998 CR Categories and Descriptors. H.1.1 [Information Systems]: MODELS AND PRINCIPLES – Systems and Information Theory; H.4.2 [INFORMATION SYSTEMS APPLICATIONS]: Types of Systems – Decision support I.2.1 [ARTIFICIAL INTELLIGENCE]: Applications and Expert Systems – Medicine and science.
Key words and phrases. regression, yield prediction, seeding date, agriculture, XGBoostRegressor.
