Advanced Efficient Feature Selection Integrating Augmented Extreme Learning Machine and Particle Swarm Optimization for Predicting Nitrogen Use Efficiency and Yield in Corn
Efficient nitrogen management is crucial for improving corn productivity while minimizing environmental impacts. This study evaluates the response of corn to nitrogen fertilization using three key metrics: yield; nitrogen harvest index (NHI); and agronomic nitrogen use efficiency (ANUE). This experi...
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MDPI AG
2025-01-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/1/244 |
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| author | Josselin Bontemps Isa Ebtehaj Gabriel Deslauriers Alain N. Rousseau Hossein Bonakdari Jacynthe Dessureault-Rompré |
| author_facet | Josselin Bontemps Isa Ebtehaj Gabriel Deslauriers Alain N. Rousseau Hossein Bonakdari Jacynthe Dessureault-Rompré |
| author_sort | Josselin Bontemps |
| collection | DOAJ |
| description | Efficient nitrogen management is crucial for improving corn productivity while minimizing environmental impacts. This study evaluates the response of corn to nitrogen fertilization using three key metrics: yield; nitrogen harvest index (NHI); and agronomic nitrogen use efficiency (ANUE). This experiment was conducted over three years (2021–2023) across 84 sites in Quebec, Canada, with five nitrogen treatments applied post-emergence (0, 50, 100, 150, 200 kg N/ha) and initial nitrogen applied at seeding (30 to 60 kg/ha). In addition, various soil health indicators, including physical, chemical, and biochemical properties, were monitored to understand their interaction with nitrogen use efficiency. Machine learning techniques, such as augmented extreme learning machine (AELM) and particle swarm optimization (PSO), were employed to optimize nitrogen recommendations by identifying the most relevant features for predicting yield and nitrogen use efficiency (NUE). The results highlight that integrating soil health indicators such as enzyme activities (β-glucosidase [BG] and N-acetyl-β-D-glucosaminidase [NAG]) and soil proteins into nitrogen management models improves prediction accuracy, leading to enhanced productivity and environmental sustainability. These findings suggest that advanced data-driven approaches can significantly contribute to more precise and sustainable nitrogen fertilization strategies. |
| format | Article |
| id | doaj-art-3dd926e47d514dd1a92c8b201050710c |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Agronomy |
| spelling | doaj-art-3dd926e47d514dd1a92c8b201050710c2025-01-24T13:17:19ZengMDPI AGAgronomy2073-43952025-01-0115124410.3390/agronomy15010244Advanced Efficient Feature Selection Integrating Augmented Extreme Learning Machine and Particle Swarm Optimization for Predicting Nitrogen Use Efficiency and Yield in CornJosselin Bontemps0Isa Ebtehaj1Gabriel Deslauriers2Alain N. Rousseau3Hossein Bonakdari4Jacynthe Dessureault-Rompré5Department of Soils and Agri-Food Engineering, Université Laval, Quebec, QC G1V 0A6, CanadaDepartment of Soils and Agri-Food Engineering, Université Laval, Quebec, QC G1V 0A6, CanadaPleineTerre Inc., 169 Rue St-Jacques, Napierville, Quebec, QC J0J 1L0, CanadaInstitut National de la Recherche Scientifique–Eau Terre Environnement Research Centre, Quebec, QC G1K 9A9, CanadaDepartment of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, CanadaDepartment of Soils and Agri-Food Engineering, Université Laval, Quebec, QC G1V 0A6, CanadaEfficient nitrogen management is crucial for improving corn productivity while minimizing environmental impacts. This study evaluates the response of corn to nitrogen fertilization using three key metrics: yield; nitrogen harvest index (NHI); and agronomic nitrogen use efficiency (ANUE). This experiment was conducted over three years (2021–2023) across 84 sites in Quebec, Canada, with five nitrogen treatments applied post-emergence (0, 50, 100, 150, 200 kg N/ha) and initial nitrogen applied at seeding (30 to 60 kg/ha). In addition, various soil health indicators, including physical, chemical, and biochemical properties, were monitored to understand their interaction with nitrogen use efficiency. Machine learning techniques, such as augmented extreme learning machine (AELM) and particle swarm optimization (PSO), were employed to optimize nitrogen recommendations by identifying the most relevant features for predicting yield and nitrogen use efficiency (NUE). The results highlight that integrating soil health indicators such as enzyme activities (β-glucosidase [BG] and N-acetyl-β-D-glucosaminidase [NAG]) and soil proteins into nitrogen management models improves prediction accuracy, leading to enhanced productivity and environmental sustainability. These findings suggest that advanced data-driven approaches can significantly contribute to more precise and sustainable nitrogen fertilization strategies.https://www.mdpi.com/2073-4395/15/1/244augmented extreme learning machine (AELM)feature selectionparticle swarm optimization (PSO)nitrogen use efficiency (NUE)soil health indicatorscorn yield |
| spellingShingle | Josselin Bontemps Isa Ebtehaj Gabriel Deslauriers Alain N. Rousseau Hossein Bonakdari Jacynthe Dessureault-Rompré Advanced Efficient Feature Selection Integrating Augmented Extreme Learning Machine and Particle Swarm Optimization for Predicting Nitrogen Use Efficiency and Yield in Corn Agronomy augmented extreme learning machine (AELM) feature selection particle swarm optimization (PSO) nitrogen use efficiency (NUE) soil health indicators corn yield |
| title | Advanced Efficient Feature Selection Integrating Augmented Extreme Learning Machine and Particle Swarm Optimization for Predicting Nitrogen Use Efficiency and Yield in Corn |
| title_full | Advanced Efficient Feature Selection Integrating Augmented Extreme Learning Machine and Particle Swarm Optimization for Predicting Nitrogen Use Efficiency and Yield in Corn |
| title_fullStr | Advanced Efficient Feature Selection Integrating Augmented Extreme Learning Machine and Particle Swarm Optimization for Predicting Nitrogen Use Efficiency and Yield in Corn |
| title_full_unstemmed | Advanced Efficient Feature Selection Integrating Augmented Extreme Learning Machine and Particle Swarm Optimization for Predicting Nitrogen Use Efficiency and Yield in Corn |
| title_short | Advanced Efficient Feature Selection Integrating Augmented Extreme Learning Machine and Particle Swarm Optimization for Predicting Nitrogen Use Efficiency and Yield in Corn |
| title_sort | advanced efficient feature selection integrating augmented extreme learning machine and particle swarm optimization for predicting nitrogen use efficiency and yield in corn |
| topic | augmented extreme learning machine (AELM) feature selection particle swarm optimization (PSO) nitrogen use efficiency (NUE) soil health indicators corn yield |
| url | https://www.mdpi.com/2073-4395/15/1/244 |
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