Modelling the Yield and Estimating the Energy Properties of <i>Miscanthus x Giganteus</i> in Different Harvest Periods
This research aims to use artificial neural networks (ANNs) to estimate the yield and energy characteristics of <i>Miscanthus x giganteus</i> (<i>MxG</i>), considering factors such as year of cultivation, location, and harvest time. In the study, which was conducted over thre...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | AgriEngineering |
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| Online Access: | https://www.mdpi.com/2624-7402/6/1/26 |
| _version_ | 1797242448488955904 |
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| author | Ivan Brandić Neven Voća Josip Leto Nikola Bilandžija |
| author_facet | Ivan Brandić Neven Voća Josip Leto Nikola Bilandžija |
| author_sort | Ivan Brandić |
| collection | DOAJ |
| description | This research aims to use artificial neural networks (ANNs) to estimate the yield and energy characteristics of <i>Miscanthus x giganteus</i> (<i>MxG</i>), considering factors such as year of cultivation, location, and harvest time. In the study, which was conducted over three years in two different geographical areas, ANN regression models were used to estimate the lower heating value (LHV) and yield of <i>MxG</i>. The models showed high predictive accuracy, achieving R<sup>2</sup> values of 0.85 for LHV and 0.95 for yield, with corresponding RMSEs of 0.13 and 2.22. A significant correlation affecting yield was found between plant height and number of shoots. In addition, a sensitivity analysis of the ANN models showed the influence of both categorical and continuous input variables on the predictions. These results highlight the role of <i>MxG</i> as a sustainable biomass energy source and provide insights for optimizing biomass production, influencing energy policy, and contributing to advances in renewable energy and global energy sustainability efforts. |
| first_indexed | 2024-04-24T18:39:23Z |
| format | Article |
| id | doaj.art-e951ef9ced4a459fa25c18584c17ef53 |
| institution | Directory Open Access Journal |
| issn | 2624-7402 |
| language | English |
| last_indexed | 2024-04-24T18:39:23Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj.art-e951ef9ced4a459fa25c18584c17ef532024-03-27T13:16:19ZengMDPI AGAgriEngineering2624-74022024-02-016142343710.3390/agriengineering6010026Modelling the Yield and Estimating the Energy Properties of <i>Miscanthus x Giganteus</i> in Different Harvest PeriodsIvan Brandić0Neven Voća1Josip Leto2Nikola Bilandžija3Department of Sustainable Technologies and Renewable Energy Sources, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10000 Zagreb, CroatiaDepartment of Sustainable Technologies and Renewable Energy Sources, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10000 Zagreb, CroatiaDepartment of Field Crops, Forage and Grassland, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10000 Zagreb, CroatiaDepartment of Mechanization and Autonomous Systems in Agriculture, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10000 Zagreb, CroatiaThis research aims to use artificial neural networks (ANNs) to estimate the yield and energy characteristics of <i>Miscanthus x giganteus</i> (<i>MxG</i>), considering factors such as year of cultivation, location, and harvest time. In the study, which was conducted over three years in two different geographical areas, ANN regression models were used to estimate the lower heating value (LHV) and yield of <i>MxG</i>. The models showed high predictive accuracy, achieving R<sup>2</sup> values of 0.85 for LHV and 0.95 for yield, with corresponding RMSEs of 0.13 and 2.22. A significant correlation affecting yield was found between plant height and number of shoots. In addition, a sensitivity analysis of the ANN models showed the influence of both categorical and continuous input variables on the predictions. These results highlight the role of <i>MxG</i> as a sustainable biomass energy source and provide insights for optimizing biomass production, influencing energy policy, and contributing to advances in renewable energy and global energy sustainability efforts.https://www.mdpi.com/2624-7402/6/1/26artificial neural networksbiomass<i>Miscanthus x giganteus</i>predictive modelling |
| spellingShingle | Ivan Brandić Neven Voća Josip Leto Nikola Bilandžija Modelling the Yield and Estimating the Energy Properties of <i>Miscanthus x Giganteus</i> in Different Harvest Periods AgriEngineering artificial neural networks biomass <i>Miscanthus x giganteus</i> predictive modelling |
| title | Modelling the Yield and Estimating the Energy Properties of <i>Miscanthus x Giganteus</i> in Different Harvest Periods |
| title_full | Modelling the Yield and Estimating the Energy Properties of <i>Miscanthus x Giganteus</i> in Different Harvest Periods |
| title_fullStr | Modelling the Yield and Estimating the Energy Properties of <i>Miscanthus x Giganteus</i> in Different Harvest Periods |
| title_full_unstemmed | Modelling the Yield and Estimating the Energy Properties of <i>Miscanthus x Giganteus</i> in Different Harvest Periods |
| title_short | Modelling the Yield and Estimating the Energy Properties of <i>Miscanthus x Giganteus</i> in Different Harvest Periods |
| title_sort | modelling the yield and estimating the energy properties of i miscanthus x giganteus i in different harvest periods |
| topic | artificial neural networks biomass <i>Miscanthus x giganteus</i> predictive modelling |
| url | https://www.mdpi.com/2624-7402/6/1/26 |
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