Herbicide resistance prediction: a mechanistic model vs. a random forest model
IntroductionHerbicides are an important technology in the Integrated Weed Management (IWM) tool box aiming to control weeds in modern agriculture. Prediction tools to evaluate the risk of resistance evolution will greatly help to choose the best IWM strategy adapted to the local field situation. The...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Agronomy |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fagro.2024.1401716/full |
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| author | Otto Richter Janin Lepke Johannes Herrmann Roland Beffa |
| author_facet | Otto Richter Janin Lepke Johannes Herrmann Roland Beffa |
| author_sort | Otto Richter |
| collection | DOAJ |
| description | IntroductionHerbicides are an important technology in the Integrated Weed Management (IWM) tool box aiming to control weeds in modern agriculture. Prediction tools to evaluate the risk of resistance evolution will greatly help to choose the best IWM strategy adapted to the local field situation. These comprise classical simulation models, mechanistic models (MMs), combining population dynamics and genetics, and recently artificial intelligence (AI) methods such as random forest. In this paper, both approaches are compared.Materials and methodsArtificial data were generated by an MM and used as training dataset for a random forest classifier. Field history information was taken from two previous studies. The data include the field histories and resistance status of Alopecurus myosuroides of 98 fields from the Hohenlohe area in Germany and 131 from the Champagne area in France.Results and discussionWith accuracies of approximately 80%, the results obtained by the random forest method applied to model-generated data and real field data, respectively, are well comparable. This concerns the ranking of prediction variables and the prediction of the resistance status of a real field and a “model field”. Predictions with model outcomes as training sets and, vice versa, predictions of a “model field” with real data as training sets and predictions by splitting of field data could be made with nearly the same accuracies.ConclusionComplementarity is shown between both approaches with the advantages of AI such as random forest to avoid approximations inherent to complex MMs. |
| format | Article |
| id | doaj-art-20b70dd17e934239a4138ae97e9fc837 |
| institution | Kabale University |
| issn | 2673-3218 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Agronomy |
| spelling | doaj-art-20b70dd17e934239a4138ae97e9fc8372025-01-08T06:11:49ZengFrontiers Media S.A.Frontiers in Agronomy2673-32182025-01-01610.3389/fagro.2024.14017161401716Herbicide resistance prediction: a mechanistic model vs. a random forest modelOtto Richter0Janin Lepke1Johannes Herrmann2Roland Beffa3Institute of Geoecology, University of Technology Braunschweig, Braunschweig, GermanyInstitute of Geoecology, University of Technology Braunschweig, Braunschweig, GermanyAgris42 GmbH, Stuttgart, GermanyConsultant, Liederbach, GermanyIntroductionHerbicides are an important technology in the Integrated Weed Management (IWM) tool box aiming to control weeds in modern agriculture. Prediction tools to evaluate the risk of resistance evolution will greatly help to choose the best IWM strategy adapted to the local field situation. These comprise classical simulation models, mechanistic models (MMs), combining population dynamics and genetics, and recently artificial intelligence (AI) methods such as random forest. In this paper, both approaches are compared.Materials and methodsArtificial data were generated by an MM and used as training dataset for a random forest classifier. Field history information was taken from two previous studies. The data include the field histories and resistance status of Alopecurus myosuroides of 98 fields from the Hohenlohe area in Germany and 131 from the Champagne area in France.Results and discussionWith accuracies of approximately 80%, the results obtained by the random forest method applied to model-generated data and real field data, respectively, are well comparable. This concerns the ranking of prediction variables and the prediction of the resistance status of a real field and a “model field”. Predictions with model outcomes as training sets and, vice versa, predictions of a “model field” with real data as training sets and predictions by splitting of field data could be made with nearly the same accuracies.ConclusionComplementarity is shown between both approaches with the advantages of AI such as random forest to avoid approximations inherent to complex MMs.https://www.frontiersin.org/articles/10.3389/fagro.2024.1401716/fullpopulation dynamicspopulation geneticsresistance managementblack-grasscomparison AI and mechanistic models |
| spellingShingle | Otto Richter Janin Lepke Johannes Herrmann Roland Beffa Herbicide resistance prediction: a mechanistic model vs. a random forest model Frontiers in Agronomy population dynamics population genetics resistance management black-grass comparison AI and mechanistic models |
| title | Herbicide resistance prediction: a mechanistic model vs. a random forest model |
| title_full | Herbicide resistance prediction: a mechanistic model vs. a random forest model |
| title_fullStr | Herbicide resistance prediction: a mechanistic model vs. a random forest model |
| title_full_unstemmed | Herbicide resistance prediction: a mechanistic model vs. a random forest model |
| title_short | Herbicide resistance prediction: a mechanistic model vs. a random forest model |
| title_sort | herbicide resistance prediction a mechanistic model vs a random forest model |
| topic | population dynamics population genetics resistance management black-grass comparison AI and mechanistic models |
| url | https://www.frontiersin.org/articles/10.3389/fagro.2024.1401716/full |
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