Genetic algorithm based approach to optimize phenotypical traits of virtual rice
How to select and combine good traits of rice to get high-production individuals is one of the key points in developing crop ideotype cultivation technologies. Existing cultivation methods for producing ideal plants, such as field trials and crop modeling, have some limits. In this paper, we propose...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ding, Weilong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016transfer abstract |
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| Schlagwörter: |
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| Umfang: |
9 |
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| Übergeordnetes Werk: |
Enthalten in: Dissolution versus cementation and its role in determining tight sandstone quality: A case study from the Upper Paleozoic in northeastern Ordos Basin, China - Li, Yong ELSEVIER, 2020, Amsterdam |
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| Übergeordnetes Werk: |
volume:403 ; year:2016 ; day:21 ; month:08 ; pages:59-67 ; extent:9 |
| Links: |
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| DOI / URN: |
10.1016/j.jtbi.2016.05.006 |
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| 520 | |a How to select and combine good traits of rice to get high-production individuals is one of the key points in developing crop ideotype cultivation technologies. Existing cultivation methods for producing ideal plants, such as field trials and crop modeling, have some limits. In this paper, we propose a method based on a genetic algorithm (GA) and a functional-structural plant model (FSPM) to optimize plant types of virtual rice by dynamically adjusting phenotypical traits. In this algorithm, phenotypical traits such as leaf angles, plant heights, the maximum number of tiller, and the angle of tiller are considered as input parameters of our virtual rice model. We evaluate the photosynthetic output as a function of these parameters, and optimized them using a GA. This method has been implemented on GroIMP using the modeling language XL (eXtended L-System) and RGG (Relational Growth Grammar). A double haploid population of rice is adopted as test material in a case study. Our experimental results show that our method can not only optimize the parameters of rice plant type and increase the amount of light absorption, but can also significantly increase crop yield. | ||
| 520 | |a How to select and combine good traits of rice to get high-production individuals is one of the key points in developing crop ideotype cultivation technologies. Existing cultivation methods for producing ideal plants, such as field trials and crop modeling, have some limits. In this paper, we propose a method based on a genetic algorithm (GA) and a functional-structural plant model (FSPM) to optimize plant types of virtual rice by dynamically adjusting phenotypical traits. In this algorithm, phenotypical traits such as leaf angles, plant heights, the maximum number of tiller, and the angle of tiller are considered as input parameters of our virtual rice model. We evaluate the photosynthetic output as a function of these parameters, and optimized them using a GA. This method has been implemented on GroIMP using the modeling language XL (eXtended L-System) and RGG (Relational Growth Grammar). A double haploid population of rice is adopted as test material in a case study. Our experimental results show that our method can not only optimize the parameters of rice plant type and increase the amount of light absorption, but can also significantly increase crop yield. | ||
| 650 | 7 | |a Plant type |2 Elsevier | |
| 650 | 7 | |a Genetic algorithm |2 Elsevier | |
| 650 | 7 | |a Functional-structural model |2 Elsevier | |
| 650 | 7 | |a Optimal design |2 Elsevier | |
| 700 | 1 | |a Xu, Lifeng |4 oth | |
| 700 | 1 | |a Wei, Yang |4 oth | |
| 700 | 1 | |a Wu, Fuli |4 oth | |
| 700 | 1 | |a Zhu, Defeng |4 oth | |
| 700 | 1 | |a Zhang, Yuping |4 oth | |
| 700 | 1 | |a Max, Nelson |4 oth | |
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10.1016/j.jtbi.2016.05.006 doi GBVA2016022000025.pica (DE-627)ELV035612665 (ELSEVIER)S0022-5193(16)30076-5 DE-627 ger DE-627 rakwb eng 570 570 DE-600 660 VZ Ding, Weilong verfasserin aut Genetic algorithm based approach to optimize phenotypical traits of virtual rice 2016transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier How to select and combine good traits of rice to get high-production individuals is one of the key points in developing crop ideotype cultivation technologies. Existing cultivation methods for producing ideal plants, such as field trials and crop modeling, have some limits. In this paper, we propose a method based on a genetic algorithm (GA) and a functional-structural plant model (FSPM) to optimize plant types of virtual rice by dynamically adjusting phenotypical traits. In this algorithm, phenotypical traits such as leaf angles, plant heights, the maximum number of tiller, and the angle of tiller are considered as input parameters of our virtual rice model. We evaluate the photosynthetic output as a function of these parameters, and optimized them using a GA. This method has been implemented on GroIMP using the modeling language XL (eXtended L-System) and RGG (Relational Growth Grammar). A double haploid population of rice is adopted as test material in a case study. Our experimental results show that our method can not only optimize the parameters of rice plant type and increase the amount of light absorption, but can also significantly increase crop yield. How to select and combine good traits of rice to get high-production individuals is one of the key points in developing crop ideotype cultivation technologies. Existing cultivation methods for producing ideal plants, such as field trials and crop modeling, have some limits. In this paper, we propose a method based on a genetic algorithm (GA) and a functional-structural plant model (FSPM) to optimize plant types of virtual rice by dynamically adjusting phenotypical traits. In this algorithm, phenotypical traits such as leaf angles, plant heights, the maximum number of tiller, and the angle of tiller are considered as input parameters of our virtual rice model. We evaluate the photosynthetic output as a function of these parameters, and optimized them using a GA. This method has been implemented on GroIMP using the modeling language XL (eXtended L-System) and RGG (Relational Growth Grammar). A double haploid population of rice is adopted as test material in a case study. Our experimental results show that our method can not only optimize the parameters of rice plant type and increase the amount of light absorption, but can also significantly increase crop yield. Plant type Elsevier Genetic algorithm Elsevier Functional-structural model Elsevier Optimal design Elsevier Xu, Lifeng oth Wei, Yang oth Wu, Fuli oth Zhu, Defeng oth Zhang, Yuping oth Max, Nelson oth Enthalten in Elsevier Ltd Li, Yong ELSEVIER Dissolution versus cementation and its role in determining tight sandstone quality: A case study from the Upper Paleozoic in northeastern Ordos Basin, China 2020 Amsterdam (DE-627)ELV004081676 volume:403 year:2016 day:21 month:08 pages:59-67 extent:9 https://doi.org/10.1016/j.jtbi.2016.05.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 403 2016 21 0821 59-67 9 045F 570 |
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10.1016/j.jtbi.2016.05.006 doi GBVA2016022000025.pica (DE-627)ELV035612665 (ELSEVIER)S0022-5193(16)30076-5 DE-627 ger DE-627 rakwb eng 570 570 DE-600 660 VZ Ding, Weilong verfasserin aut Genetic algorithm based approach to optimize phenotypical traits of virtual rice 2016transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier How to select and combine good traits of rice to get high-production individuals is one of the key points in developing crop ideotype cultivation technologies. Existing cultivation methods for producing ideal plants, such as field trials and crop modeling, have some limits. In this paper, we propose a method based on a genetic algorithm (GA) and a functional-structural plant model (FSPM) to optimize plant types of virtual rice by dynamically adjusting phenotypical traits. In this algorithm, phenotypical traits such as leaf angles, plant heights, the maximum number of tiller, and the angle of tiller are considered as input parameters of our virtual rice model. We evaluate the photosynthetic output as a function of these parameters, and optimized them using a GA. This method has been implemented on GroIMP using the modeling language XL (eXtended L-System) and RGG (Relational Growth Grammar). A double haploid population of rice is adopted as test material in a case study. Our experimental results show that our method can not only optimize the parameters of rice plant type and increase the amount of light absorption, but can also significantly increase crop yield. How to select and combine good traits of rice to get high-production individuals is one of the key points in developing crop ideotype cultivation technologies. Existing cultivation methods for producing ideal plants, such as field trials and crop modeling, have some limits. In this paper, we propose a method based on a genetic algorithm (GA) and a functional-structural plant model (FSPM) to optimize plant types of virtual rice by dynamically adjusting phenotypical traits. In this algorithm, phenotypical traits such as leaf angles, plant heights, the maximum number of tiller, and the angle of tiller are considered as input parameters of our virtual rice model. We evaluate the photosynthetic output as a function of these parameters, and optimized them using a GA. This method has been implemented on GroIMP using the modeling language XL (eXtended L-System) and RGG (Relational Growth Grammar). A double haploid population of rice is adopted as test material in a case study. Our experimental results show that our method can not only optimize the parameters of rice plant type and increase the amount of light absorption, but can also significantly increase crop yield. Plant type Elsevier Genetic algorithm Elsevier Functional-structural model Elsevier Optimal design Elsevier Xu, Lifeng oth Wei, Yang oth Wu, Fuli oth Zhu, Defeng oth Zhang, Yuping oth Max, Nelson oth Enthalten in Elsevier Ltd Li, Yong ELSEVIER Dissolution versus cementation and its role in determining tight sandstone quality: A case study from the Upper Paleozoic in northeastern Ordos Basin, China 2020 Amsterdam (DE-627)ELV004081676 volume:403 year:2016 day:21 month:08 pages:59-67 extent:9 https://doi.org/10.1016/j.jtbi.2016.05.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 403 2016 21 0821 59-67 9 045F 570 |
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10.1016/j.jtbi.2016.05.006 doi GBVA2016022000025.pica (DE-627)ELV035612665 (ELSEVIER)S0022-5193(16)30076-5 DE-627 ger DE-627 rakwb eng 570 570 DE-600 660 VZ Ding, Weilong verfasserin aut Genetic algorithm based approach to optimize phenotypical traits of virtual rice 2016transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier How to select and combine good traits of rice to get high-production individuals is one of the key points in developing crop ideotype cultivation technologies. Existing cultivation methods for producing ideal plants, such as field trials and crop modeling, have some limits. In this paper, we propose a method based on a genetic algorithm (GA) and a functional-structural plant model (FSPM) to optimize plant types of virtual rice by dynamically adjusting phenotypical traits. In this algorithm, phenotypical traits such as leaf angles, plant heights, the maximum number of tiller, and the angle of tiller are considered as input parameters of our virtual rice model. We evaluate the photosynthetic output as a function of these parameters, and optimized them using a GA. This method has been implemented on GroIMP using the modeling language XL (eXtended L-System) and RGG (Relational Growth Grammar). A double haploid population of rice is adopted as test material in a case study. Our experimental results show that our method can not only optimize the parameters of rice plant type and increase the amount of light absorption, but can also significantly increase crop yield. How to select and combine good traits of rice to get high-production individuals is one of the key points in developing crop ideotype cultivation technologies. Existing cultivation methods for producing ideal plants, such as field trials and crop modeling, have some limits. In this paper, we propose a method based on a genetic algorithm (GA) and a functional-structural plant model (FSPM) to optimize plant types of virtual rice by dynamically adjusting phenotypical traits. In this algorithm, phenotypical traits such as leaf angles, plant heights, the maximum number of tiller, and the angle of tiller are considered as input parameters of our virtual rice model. We evaluate the photosynthetic output as a function of these parameters, and optimized them using a GA. This method has been implemented on GroIMP using the modeling language XL (eXtended L-System) and RGG (Relational Growth Grammar). A double haploid population of rice is adopted as test material in a case study. Our experimental results show that our method can not only optimize the parameters of rice plant type and increase the amount of light absorption, but can also significantly increase crop yield. Plant type Elsevier Genetic algorithm Elsevier Functional-structural model Elsevier Optimal design Elsevier Xu, Lifeng oth Wei, Yang oth Wu, Fuli oth Zhu, Defeng oth Zhang, Yuping oth Max, Nelson oth Enthalten in Elsevier Ltd Li, Yong ELSEVIER Dissolution versus cementation and its role in determining tight sandstone quality: A case study from the Upper Paleozoic in northeastern Ordos Basin, China 2020 Amsterdam (DE-627)ELV004081676 volume:403 year:2016 day:21 month:08 pages:59-67 extent:9 https://doi.org/10.1016/j.jtbi.2016.05.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 403 2016 21 0821 59-67 9 045F 570 |
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10.1016/j.jtbi.2016.05.006 doi GBVA2016022000025.pica (DE-627)ELV035612665 (ELSEVIER)S0022-5193(16)30076-5 DE-627 ger DE-627 rakwb eng 570 570 DE-600 660 VZ Ding, Weilong verfasserin aut Genetic algorithm based approach to optimize phenotypical traits of virtual rice 2016transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier How to select and combine good traits of rice to get high-production individuals is one of the key points in developing crop ideotype cultivation technologies. Existing cultivation methods for producing ideal plants, such as field trials and crop modeling, have some limits. In this paper, we propose a method based on a genetic algorithm (GA) and a functional-structural plant model (FSPM) to optimize plant types of virtual rice by dynamically adjusting phenotypical traits. In this algorithm, phenotypical traits such as leaf angles, plant heights, the maximum number of tiller, and the angle of tiller are considered as input parameters of our virtual rice model. We evaluate the photosynthetic output as a function of these parameters, and optimized them using a GA. This method has been implemented on GroIMP using the modeling language XL (eXtended L-System) and RGG (Relational Growth Grammar). A double haploid population of rice is adopted as test material in a case study. Our experimental results show that our method can not only optimize the parameters of rice plant type and increase the amount of light absorption, but can also significantly increase crop yield. How to select and combine good traits of rice to get high-production individuals is one of the key points in developing crop ideotype cultivation technologies. Existing cultivation methods for producing ideal plants, such as field trials and crop modeling, have some limits. In this paper, we propose a method based on a genetic algorithm (GA) and a functional-structural plant model (FSPM) to optimize plant types of virtual rice by dynamically adjusting phenotypical traits. In this algorithm, phenotypical traits such as leaf angles, plant heights, the maximum number of tiller, and the angle of tiller are considered as input parameters of our virtual rice model. We evaluate the photosynthetic output as a function of these parameters, and optimized them using a GA. This method has been implemented on GroIMP using the modeling language XL (eXtended L-System) and RGG (Relational Growth Grammar). A double haploid population of rice is adopted as test material in a case study. Our experimental results show that our method can not only optimize the parameters of rice plant type and increase the amount of light absorption, but can also significantly increase crop yield. Plant type Elsevier Genetic algorithm Elsevier Functional-structural model Elsevier Optimal design Elsevier Xu, Lifeng oth Wei, Yang oth Wu, Fuli oth Zhu, Defeng oth Zhang, Yuping oth Max, Nelson oth Enthalten in Elsevier Ltd Li, Yong ELSEVIER Dissolution versus cementation and its role in determining tight sandstone quality: A case study from the Upper Paleozoic in northeastern Ordos Basin, China 2020 Amsterdam (DE-627)ELV004081676 volume:403 year:2016 day:21 month:08 pages:59-67 extent:9 https://doi.org/10.1016/j.jtbi.2016.05.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 403 2016 21 0821 59-67 9 045F 570 |
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10.1016/j.jtbi.2016.05.006 doi GBVA2016022000025.pica (DE-627)ELV035612665 (ELSEVIER)S0022-5193(16)30076-5 DE-627 ger DE-627 rakwb eng 570 570 DE-600 660 VZ Ding, Weilong verfasserin aut Genetic algorithm based approach to optimize phenotypical traits of virtual rice 2016transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier How to select and combine good traits of rice to get high-production individuals is one of the key points in developing crop ideotype cultivation technologies. Existing cultivation methods for producing ideal plants, such as field trials and crop modeling, have some limits. In this paper, we propose a method based on a genetic algorithm (GA) and a functional-structural plant model (FSPM) to optimize plant types of virtual rice by dynamically adjusting phenotypical traits. In this algorithm, phenotypical traits such as leaf angles, plant heights, the maximum number of tiller, and the angle of tiller are considered as input parameters of our virtual rice model. We evaluate the photosynthetic output as a function of these parameters, and optimized them using a GA. This method has been implemented on GroIMP using the modeling language XL (eXtended L-System) and RGG (Relational Growth Grammar). A double haploid population of rice is adopted as test material in a case study. Our experimental results show that our method can not only optimize the parameters of rice plant type and increase the amount of light absorption, but can also significantly increase crop yield. How to select and combine good traits of rice to get high-production individuals is one of the key points in developing crop ideotype cultivation technologies. Existing cultivation methods for producing ideal plants, such as field trials and crop modeling, have some limits. In this paper, we propose a method based on a genetic algorithm (GA) and a functional-structural plant model (FSPM) to optimize plant types of virtual rice by dynamically adjusting phenotypical traits. In this algorithm, phenotypical traits such as leaf angles, plant heights, the maximum number of tiller, and the angle of tiller are considered as input parameters of our virtual rice model. We evaluate the photosynthetic output as a function of these parameters, and optimized them using a GA. This method has been implemented on GroIMP using the modeling language XL (eXtended L-System) and RGG (Relational Growth Grammar). A double haploid population of rice is adopted as test material in a case study. Our experimental results show that our method can not only optimize the parameters of rice plant type and increase the amount of light absorption, but can also significantly increase crop yield. Plant type Elsevier Genetic algorithm Elsevier Functional-structural model Elsevier Optimal design Elsevier Xu, Lifeng oth Wei, Yang oth Wu, Fuli oth Zhu, Defeng oth Zhang, Yuping oth Max, Nelson oth Enthalten in Elsevier Ltd Li, Yong ELSEVIER Dissolution versus cementation and its role in determining tight sandstone quality: A case study from the Upper Paleozoic in northeastern Ordos Basin, China 2020 Amsterdam (DE-627)ELV004081676 volume:403 year:2016 day:21 month:08 pages:59-67 extent:9 https://doi.org/10.1016/j.jtbi.2016.05.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 403 2016 21 0821 59-67 9 045F 570 |
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Enthalten in Dissolution versus cementation and its role in determining tight sandstone quality: A case study from the Upper Paleozoic in northeastern Ordos Basin, China Amsterdam volume:403 year:2016 day:21 month:08 pages:59-67 extent:9 |
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Dissolution versus cementation and its role in determining tight sandstone quality: A case study from the Upper Paleozoic in northeastern Ordos Basin, China |
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Existing cultivation methods for producing ideal plants, such as field trials and crop modeling, have some limits. In this paper, we propose a method based on a genetic algorithm (GA) and a functional-structural plant model (FSPM) to optimize plant types of virtual rice by dynamically adjusting phenotypical traits. In this algorithm, phenotypical traits such as leaf angles, plant heights, the maximum number of tiller, and the angle of tiller are considered as input parameters of our virtual rice model. We evaluate the photosynthetic output as a function of these parameters, and optimized them using a GA. This method has been implemented on GroIMP using the modeling language XL (eXtended L-System) and RGG (Relational Growth Grammar). A double haploid population of rice is adopted as test material in a case study. 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How to select and combine good traits of rice to get high-production individuals is one of the key points in developing crop ideotype cultivation technologies. Existing cultivation methods for producing ideal plants, such as field trials and crop modeling, have some limits. In this paper, we propose a method based on a genetic algorithm (GA) and a functional-structural plant model (FSPM) to optimize plant types of virtual rice by dynamically adjusting phenotypical traits. In this algorithm, phenotypical traits such as leaf angles, plant heights, the maximum number of tiller, and the angle of tiller are considered as input parameters of our virtual rice model. We evaluate the photosynthetic output as a function of these parameters, and optimized them using a GA. This method has been implemented on GroIMP using the modeling language XL (eXtended L-System) and RGG (Relational Growth Grammar). A double haploid population of rice is adopted as test material in a case study. Our experimental results show that our method can not only optimize the parameters of rice plant type and increase the amount of light absorption, but can also significantly increase crop yield. |
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How to select and combine good traits of rice to get high-production individuals is one of the key points in developing crop ideotype cultivation technologies. Existing cultivation methods for producing ideal plants, such as field trials and crop modeling, have some limits. In this paper, we propose a method based on a genetic algorithm (GA) and a functional-structural plant model (FSPM) to optimize plant types of virtual rice by dynamically adjusting phenotypical traits. In this algorithm, phenotypical traits such as leaf angles, plant heights, the maximum number of tiller, and the angle of tiller are considered as input parameters of our virtual rice model. We evaluate the photosynthetic output as a function of these parameters, and optimized them using a GA. This method has been implemented on GroIMP using the modeling language XL (eXtended L-System) and RGG (Relational Growth Grammar). A double haploid population of rice is adopted as test material in a case study. Our experimental results show that our method can not only optimize the parameters of rice plant type and increase the amount of light absorption, but can also significantly increase crop yield. |
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How to select and combine good traits of rice to get high-production individuals is one of the key points in developing crop ideotype cultivation technologies. Existing cultivation methods for producing ideal plants, such as field trials and crop modeling, have some limits. In this paper, we propose a method based on a genetic algorithm (GA) and a functional-structural plant model (FSPM) to optimize plant types of virtual rice by dynamically adjusting phenotypical traits. In this algorithm, phenotypical traits such as leaf angles, plant heights, the maximum number of tiller, and the angle of tiller are considered as input parameters of our virtual rice model. We evaluate the photosynthetic output as a function of these parameters, and optimized them using a GA. This method has been implemented on GroIMP using the modeling language XL (eXtended L-System) and RGG (Relational Growth Grammar). A double haploid population of rice is adopted as test material in a case study. Our experimental results show that our method can not only optimize the parameters of rice plant type and increase the amount of light absorption, but can also significantly increase crop yield. |
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