High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field.
High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field.
复制标题
DOI:
10.3389/fbioe.2020.623705
复制
发表时间:
2020
影响因子:
5.7
通讯作者:
Muhammad A
中科院分区:
文献类型:
--
作者:
Li D;Quan C;Song Z;Li X;Yu G;Li C;Muhammad A
Food scarcity, population growth, and global climate change have propelled crop yield growth driven by high-throughput phenotyping into the era of big data. However, access to large-scale phenotypic data has now become a critical barrier that phenomics urgently must overcome. Fortunately, the high-throughput plant phenotyping platform (HT3P), employing advanced sensors and data collection systems, can take full advantage of non-destructive and high-throughput methods to monitor, quantify, and evaluate specific phenotypes for large-scale agricultural experiments, and it can effectively perform phenotypic tasks that traditional phenotyping could not do. In this way, HT3Ps are novel and powerful tools, for which various commercial, customized, and even self-developed ones have been recently introduced in rising numbers. Here, we review these HT3Ps in nearly 7 years from greenhouses and growth chambers to the field, and from ground-based proximal phenotyping to aerial large-scale remote sensing. Platform configurations, novelties, operating modes, current developments, as well the strengths and weaknesses of diverse types of HT3Ps are thoroughly and clearly described. Then, miscellaneous combinations of HT3Ps for comparative validation and comprehensive analysis are systematically present, for the first time. Finally, we consider current phenotypic challenges and provide fresh perspectives on future development trends of HT3Ps. This review aims to provide ideas, thoughts, and insights for the optimal selection, exploitation, and utilization of HT3Ps, and thereby pave the way to break through current phenotyping bottlenecks in botany.
登录
查看更多内容
影响因子:
4.6
作者:
Atieno J;Li Y;Langridge P;Dowling K;Brien C;Berger B;Varshney RK;Sutton T
通讯作者:
Sutton T
DOI:
10.1111/nph.15129
发表时间:
2018-07
期刊:
The New phytologist
影响因子:
--
作者:
Czedik-Eysenberg A;Seitner S;Güldener U;Koemeda S;Jez J;Colombini M;Djamei A
通讯作者:
Djamei A
影响因子:
4.5
作者:
Chang, Sungyul;Lee, Unseok;Kim, Jin-Baek
通讯作者:
Kim, Jin-Baek
影响因子:
5.1
作者:
Das Choudhury S;Bashyam S;Qiu Y;Samal A;Awada T
通讯作者:
Awada T
影响因子:
3
作者:
Acosta-Gamboa, Lucia M.;Liu, Suxing;Lorence, Argelia
通讯作者:
Lorence, Argelia