Maize yield prediction based on artificial intelligence using spatio-temporal data
| dc.contributor.author | Nyéki, Anikó | |
| dc.contributor.author | Kerepesi, Csaba | |
| dc.contributor.author | Daróczy, Bálint | |
| dc.contributor.author | Benczúr, András | |
| dc.contributor.author | Milics, Gábor | |
| dc.contributor.author | Kovács, Attila József | |
| dc.contributor.author | Neményi, Miklós | |
| dc.contributor.status | egyetemi oktató, kutató | hu_HU |
| dc.coverage.temporal | 2018.06.29. | hu_HU |
| dc.date.accessioned | 2019-10-07T13:10:18Z | |
| dc.date.available | 2019-10-07T13:10:18Z | |
| dc.description.abstract | The aim of this study was to predict maize yield by artificial intelligence using spatio-temporal training date. Counter-propagation artificial neural networks (CP-ANNs), xy-fused networks (XY-Fs), supervised Kohonen networks (SKNs), extreme gradient boosting (XGBoost) and support-vector machine (SVM) were used for predicting maize yield in 5 vegetation pariods. Input variables for modelling were: soil parameters (pH, P2O5, K2O, Zn, Clay content, ECa, draught force, Cone index) and micro-relief averages and meteorological parameters for the 63 treatment units. The best performing method (XGBoost) attained 92.1 and 95.3% of accuracy on the training and the test set. | hu_HU |
| dc.format.extent | 1011-1017 | hu_HU |
| dc.identifier.uri | http://hdl.handle.net/2437/273991 | |
| dc.language.iso | en | hu_HU |
| dc.subject | maize yield prediction | hu_HU |
| dc.subject | machine learning | hu_HU |
| dc.subject | XGBoost | hu_HU |
| dc.subject | soil variables | hu_HU |
| dc.subject.discipline | tudományterületek::növénytudományok | hu_HU |
| dc.title | Maize yield prediction based on artificial intelligence using spatio-temporal data | hu_HU |
| dc.type | proceedings | hu_HU |