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A protocol of field-based phenotyping procedure for no-till wheat root system architecture based on data-driven model-assist

Authors :
Xinxin Chen
Qishuo Ding
Ruiyin He
Huixin Li
Source :
Artificial Intelligence in Agriculture, Vol 3, Iss , Pp 33-41 (2019)
Publication Year :
2019
Publisher :
KeAi Communications Co., Ltd., 2019.

Abstract

Field-based phenotyping (FBP) of crop root system architecture (RSA) provides a way to quantify the root growth and distribution in field with a smaller scale. Studies on a better understanding of the interrelations between field crop root physiological traits, root developmental phases and environmental changes are hindered due to deficiency of in situ root system architecture testing and quantitative methods for field crop. The present study aimed to propose a protocol for field-based wheat root system architecture with technical details of key operational procedures. Phenotyping of RSA traits from root spatial coordinate data acquisition and visualization software presented scaled illustrations of wheat RSA dynamics and root developmental phases which also revealed the root topological heterogeneities, either within a plant or among individuals. Percentage of horizontal and vertical soil coverage by root showed that root foraging capability along soil depth was better than within the horizontal dimension. In brief, our data indicated that FBP of wheat RSA could be achieved using the protocol of data-driven model-assisted phenotyping procedure. The proposed protocol was demonstrated useful for FBP of RSAs. It was proved effective to illustrate the topological structures of the wheat root system and to quantify RSA-derived parameters, this could be a useful tool for characterizing and analyzing the structural distortion, heterogeneous distribution and the soil space exploration characteristics of wheat root. Keywords: Field-based phenotyping (FPB), Data-driven model-assisted, Paddy-wheat root system architecture, FBP protocol

Subjects

Subjects :
Agriculture

Details

Language :
English
ISSN :
25897217
Volume :
3
Issue :
33-41
Database :
Directory of Open Access Journals
Journal :
Artificial Intelligence in Agriculture
Publication Type :
Academic Journal
Accession number :
edsdoj.46c63c61c67471aab71be22266e09e0
Document Type :
article
Full Text :
https://doi.org/10.1016/j.aiia.2019.10.002