A knowledge-and-data-driven modeling approach for simulating plant growth: A case study on tomato growth

doi:10.1016/j.ecolmodel.2015.06.006

	A knowledge-and-data-driven modeling approach for simulating plant growth: A case study on tomato growth
	Fan, Xing-Rong1 ; Kang, Meng-Zhen2 ; Heuvelink, Ep 3; de Reffye, Philippe 4; Hu, Bao-Gang1 ; Kang MZ(康孟珍)
发表期刊	ECOLOGICAL MODELLING
	2015-09-24
卷号	312 页码:363-373
文章类型	Article
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.
关键词	Data-driven Model Knowledge-driven Model Greenlab Knowledge-and-data-driven Model Model Integration Plant Growth Modeling
WOS标题词	Science & Technology ; Life Sciences & Biomedicine
DOI	10.1016/j.ecolmodel.2015.06.006
关键词[WOS]	GREENLAB ; CROP ; MACHINES ; DYNAMICS ; SEASONS ; DOMAIN
收录类别	SCI
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Ecology
WOS记录号	WOS:000358469200033
引用统计	被引频次：35[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/8885
专题	多模态人工智能系统全国重点实验室_多媒体计算
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
第一作者单位	模式识别国家重点实验室
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.

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