Literature DB >> 33520974

High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field.

Daoliang Li1,2,3,4,5, Chaoqun Quan1,2,3,4,5, Zhaoyang Song1,2,3,4,5, Xiang Li6, Guanghui Yu1,2,3,4,5, Cheng Li1,2,3,4,5, Akhter Muhammad1,5.   

Abstract

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.
Copyright © 2021 Li, Quan, Song, Li, Yu, Li and Muhammad.

Entities:  

Keywords:  crop improvement; high-throughput; phenomics; phenotyping platform; plant science; remote sensing; sensors

Year:  2021        PMID: 33520974      PMCID: PMC7838587          DOI: 10.3389/fbioe.2020.623705

Source DB:  PubMed          Journal:  Front Bioeng Biotechnol        ISSN: 2296-4185


  13 in total

Review 1.  Omics-Facilitated Crop Improvement for Climate Resilience and Superior Nutritive Value.

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Journal:  Front Plant Sci       Date:  2021-12-01       Impact factor: 5.753

2.  MultipleXLab: A high-throughput portable live-imaging root phenotyping platform using deep learning and computer vision.

Authors:  Vinicius Lube; Mehmet Alican Noyan; Alexander Przybysz; Khaled Salama; Ikram Blilou
Journal:  Plant Methods       Date:  2022-03-27       Impact factor: 4.993

Review 3.  New approaches to improve crop tolerance to biotic and abiotic stresses.

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4.  Development of a plant conveyance system using an AGV and a self-designed plant-handling device: A case study of DIY plant phenotyping.

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Review 5.  Recent Advances for Drought Stress Tolerance in Maize (Zea mays L.): Present Status and Future Prospects.

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Journal:  Front Plant Sci       Date:  2022-05-30       Impact factor: 6.627

Review 6.  Crop breeding for a changing climate: integrating phenomics and genomics with bioinformatics.

Authors:  Jacob I Marsh; Haifei Hu; Mitchell Gill; Jacqueline Batley; David Edwards
Journal:  Theor Appl Genet       Date:  2021-04-14       Impact factor: 5.699

Review 7.  Advances in field-based high-throughput photosynthetic phenotyping.

Authors:  Peng Fu; Christopher M Montes; Matthew H Siebers; Nuria Gomez-Casanovas; Justin M McGrath; Elizabeth A Ainsworth; Carl J Bernacchi
Journal:  J Exp Bot       Date:  2022-05-23       Impact factor: 7.298

Review 8.  A review of remote sensing for potato traits characterization in precision agriculture.

Authors:  Chen Sun; Jing Zhou; Yuchi Ma; Yijia Xu; Bin Pan; Zhou Zhang
Journal:  Front Plant Sci       Date:  2022-07-18       Impact factor: 6.627

9.  A Novel Computational Framework for Precision Diagnosis and Subtype Discovery of Plant With Lesion.

Authors:  Fei Xia; Xiaojun Xie; Zongqin Wang; Shichao Jin; Ke Yan; Zhiwei Ji
Journal:  Front Plant Sci       Date:  2022-01-03       Impact factor: 5.753

10.  Drought Tolerant Near Isogenic Lines of Pusa 44 Pyramided With qDTY2.1 and qDTY3.1, Show Accelerated Recovery Response in a High Throughput Phenomics Based Phenotyping.

Authors:  Priyanka Dwivedi; Naleeni Ramawat; Dhandapani Raju; Gaurav Dhawan; S Gopala Krishnan; Viswanathan Chinnusamy; Prolay Kumar Bhowmick; K K Vinod; Madan Pal; Mariappan Nagarajan; Ranjith Kumar Ellur; Haritha Bollinedi; Ashok K Singh
Journal:  Front Plant Sci       Date:  2022-01-05       Impact factor: 5.753

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