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A knowledge-and-data-driven modeling approach for simulating plant growth: A case study on tomato growth
Fan, Xing-Rong1; Kang, Meng-Zhen2; Heuvelink, Ep3; de Reffye, Philippe4; Hu, Bao-Gang1; Kang MZ(康孟珍)
Source PublicationECOLOGICAL MODELLING
2015-09-24
Volume312Pages:363-373
SubtypeArticle
AbstractThis 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.
KeywordData-driven Model Knowledge-driven Model Greenlab Knowledge-and-data-driven Model Model Integration Plant Growth Modeling
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
DOI10.1016/j.ecolmodel.2015.06.006
WOS KeywordGREENLAB ; CROP ; MACHINES ; DYNAMICS ; SEASONS ; DOMAIN
Indexed BySCI
Language英语
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEcology
WOS IDWOS:000358469200033
Citation statistics
Cited Times:10[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8885
Collection模式识别国家重点实验室_多媒体计算与图形学
Corresponding AuthorKang MZ(康孟珍)
Affiliation1.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
Recommended Citation
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.
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