A knowledge-and-data-driven modeling approach for simulating plant growth: A case study on tomato growth
文献类型：期刊论文
| 作者 | Fan, Xing-Rong1 ; Kang, Meng-Zhen2; Heuvelink, Ep3; de Reffye, Philippe4; Hu, Bao-Gang1; Kang MZ(康孟珍)
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| 刊名 | ECOLOGICAL MODELLING
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| 出版日期 | 2015-09-24 |
| 卷号 | 312页码:363-373 |
| 关键词 | Data-driven model Knowledge-driven model GreenLab Knowledge-and-data-driven model Model integration Plant growth modeling |
| 英文摘要 | This paper proposes a novel knowledge-and-data-driven modeling (KDDM) approach for simulating plant growth that consists of two submodels. One submodel is derived from all available domain knowledge, including all known relationships from physically based or mechanistic models; the other is constructed solely from data without using any domain knowledge. In this work, a GreenLab model was adopted as the knowledge-driven (KD) submodel and the radial basis function network (RBFN) as the data-driven (DD) submodel. A tomato crop was taken as a case study on plant growth modeling. Tomato growth data sets from twelve greenhouse experiments over five years were used to calibrate and test the model. In comparison with the existing knowledge-driven model (KDM, BIC=1215.67) and data-driven model (DDM, BIC=1150.86), the proposed KDDM approach (BIC=1144.36) presented several benefits in predicting tomato yields. In particular, the KDDM approach is able to provide strong predictions of yields from different types of organs, including leaves, stems, and fruits, even when observational data on the organs are unavailable. The case study confirms that the KDDM approach inherits advantages from both the KDM and DDM approaches. Two cases of superposition and composition coupling operators in the KDDM approach are also discussed. (C) 2015 Elsevier B.V. All rights reserved. |
| WOS标题词 | Science & Technology ; Life Sciences & Biomedicine |
| 类目[WOS] | Ecology |
| 研究领域[WOS] | Environmental Sciences & Ecology |
| 关键词[WOS] | GREENLAB ; CROP ; MACHINES ; DYNAMICS ; SEASONS ; DOMAIN |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000358469200033 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/8885]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队 |
| 通讯作者 | Kang MZ(康孟珍) |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Wageningen Univ, Hort & Prod Physiol Grp, NL-6700 AP Wageningen, Netherlands 4.Cirad Amis, F-34398 Montpellier 5, France |
| 推荐引用方式 GB/T 7714 | Fan, Xing-Rong,Kang, Meng-Zhen,Heuvelink, Ep,et al. A knowledge-and-data-driven modeling approach for simulating plant growth: A case study on tomato growth[J]. ECOLOGICAL MODELLING,2015,312:363-373. |
| APA | Fan, Xing-Rong,Kang, Meng-Zhen,Heuvelink, Ep,de Reffye, Philippe,Hu, Bao-Gang,&康孟珍.(2015).A knowledge-and-data-driven modeling approach for simulating plant growth: A case study on tomato growth.ECOLOGICAL MODELLING,312,363-373. |
| MLA | Fan, Xing-Rong,et al."A knowledge-and-data-driven modeling approach for simulating plant growth: A case study on tomato growth".ECOLOGICAL MODELLING 312(2015):363-373. |
入库方式： OAI收割
来源：自动化研究所
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