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Gargiulo L, Mele G, Izzo LG, Romano LE, Aronne G. Local mapping of root orientation traits by X-ray micro-CT and 3d image analysis: A study case on carrot seedlings grown in simulated vs real weightlessness. PLANT METHODS 2024; 20:150. [PMID: 39342217 PMCID: PMC11439289 DOI: 10.1186/s13007-024-01276-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2024] [Accepted: 09/23/2024] [Indexed: 10/01/2024]
Abstract
BACKGROUND Root phenotyping is particularly challenging because of complexity and inaccessibility of root apparatus. Orientation is one of the most important architectural traits of roots and its characterization is generally addressed using multiple approaches often based on overall measurements which are difficult to correlate to plant specific physiological aspects and its genetic features. Hence, a 3D image analysis approach, based on the recent method of Straumit, is proposed in this study to obtain a local mapping of root angles. RESULTS Proposed method was applied here on radicles of carrot seedlings grown in real weightlessness on the International Space Station (ISS) and on Earth simulated weightlessness by clinorotation. A reference experiment in 1 g static condition on Earth was also performed. Radicles were imaged by X-ray micro-CT and two novel root orientation traits were defined: the "root angle to sowing plane" (RASP) providing accurate angle distributions for each analysed radicle and the "root orientation changes" (ROC) number. The parameters of the RASP distributions and the ROC values did not exhibit any significant difference in orientation between radicles grown under clinorotation and on the ISS. Only a slight thickening in root corners was found in simulated vs real weightlessness. Such results showed that a simple uniaxial clinostat can be an affordable analog in experimental studies reckoning on weightless radicles growth. CONCLUSIONS The proposed local orientation mapping approach can be extended also to different root systems providing a contribution in the challenging task of phenotyping complex and important plant structures such as roots.
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Affiliation(s)
- L Gargiulo
- Institute for Agricultural and Forest Systems in the Mediterranean, National Research Council, Portici, Italy
| | - G Mele
- Institute for Agricultural and Forest Systems in the Mediterranean, National Research Council, Portici, Italy.
| | - L G Izzo
- Department of Agricultural Sciences, University of Naples Federico II, Portici, Italy
| | - L E Romano
- Department of Agricultural Sciences, University of Naples Federico II, Portici, Italy
| | - G Aronne
- Department of Agricultural Sciences, University of Naples Federico II, Portici, Italy
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Zhang Y, Liu Q, Chen J, Sun C, Lin S, Cao H, Xiao Z, Huang M. Developing non-invasive 3D quantificational imaging for intelligent coconut analysis system with X-ray. PLANT METHODS 2023; 19:24. [PMID: 36894949 PMCID: PMC9996987 DOI: 10.1186/s13007-023-01002-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND As one of the largest drupes in the world, the coconut has a special multilayered structure and a seed development process that is not yet fully understood. On the one hand, the special structure of the coconut pericarp prevents the development of external damage to the coconut fruit, and on the other hand, the thickness of the coconut shell makes it difficult to observe the development of bacteria inside it. In addition, coconut takes about 1 year to progress from pollination to maturity. During the long development process, coconut development is vulnerable to natural disasters, cold waves, typhoons, etc. Therefore, nondestructive observation of the internal development process remains a highly important and challenging task. In this study, We proposed an intelligent system for building a three-dimensional (3D) quantitative imaging model of coconut fruit using Computed Tomography (CT) images. Cross-sectional images of coconut fruit were obtained by spiral CT scanning. Then a point cloud model was built by extracting 3D coordinate data and RGB values. The point cloud model was denoised using the cluster denoising method. Finally, a 3D quantitative model of a coconut fruit was established. RESULTS The innovations of this work are as follows. 1) Using CT scans, we obtained a total of 37,950 non-destructive internal growth change maps of various types of coconuts to establish a coconut data set called "CCID", which provides powerful graphical data support for coconut research. 2) Based on this data set, we built a coconut intelligence system. By inputting a batch of coconut images into a 3D point cloud map, the internal structure information can be ascertained, the entire contour can be drawn and rendered according to need, and the long diameter, short diameter and volume of the required structure can be obtained. We maintained quantitative observation on a batch of local Hainan coconuts for more than 3 months. With 40 coconuts as test cases, the high accuracy of the model generated by the system is proven. The system has a good application value and broad popularization prospects in the cultivation and optimization of coconut fruit. CONCLUSION The evaluation results show that the 3D quantitative imaging model has high accuracy in capturing the internal development process of coconut fruits. The system can effectively assist growers in internal developmental observations and in structural data acquisition from coconut, thus providing decision-making support for improving the cultivation conditions of coconuts.
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Affiliation(s)
- Yu Zhang
- School of Computer Science and Technology, Hainan University, Haikou, China
| | - Qianfan Liu
- School of Computer Science and Technology, Hainan University, Haikou, China
| | - Jing Chen
- Radiology department, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou, China
| | - Chengxu Sun
- Coconut Research Institute, Chinese Academy of Tropical Agricultural Sciences, Wenchang, China
| | - Shenghuang Lin
- Radiology department, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou, China
| | - Hongxing Cao
- Coconut Research Institute, Chinese Academy of Tropical Agricultural Sciences, Wenchang, China
| | - Zhaolin Xiao
- School of Computer Science and Engineering, Xi’an University of Technology, XI’an, China
| | - Mengxing Huang
- School of Computer Science and Technology, Hainan University, Haikou, China
- School of Information and Communication Engineering, Hainan University, Haikou, China
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Wu Q, Wu J, Hu P, Zhang W, Ma Y, Yu K, Guo Y, Cao J, Li H, Li B, Yao Y, Cao H, Zhang W. Quantification of the three-dimensional root system architecture using an automated rotating imaging system. PLANT METHODS 2023; 19:11. [PMID: 36732764 PMCID: PMC9896698 DOI: 10.1186/s13007-023-00988-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Crop breeding based on root system architecture (RSA) optimization is an essential factor for improving crop production in developing countries. Identification, evaluation, and selection of root traits of soil-grown crops require innovations that enable high-throughput and accurate quantification of three-dimensional (3D) RSA of crops over developmental time. RESULTS We proposed an automated imaging system and 3D imaging data processing pipeline to quantify the 3D RSA of soil-grown individual plants across seedlings to the mature stage. A multi-view automated imaging system composed of a rotary table and an imaging arm with 12 cameras mounted with a combination of fan-shaped and vertical distribution was developed to obtain 3D image data of roots grown on a customized root support mesh. A 3D imaging data processing pipeline was developed to quantify the 3D RSA based on the point cloud generated from multi-view images. The global architecture of root systems can be quantified automatically. Detailed analysis of the reconstructed 3D root model also allowed us to investigate the Spatio-temporal distribution of roots. A method combining horizontal slicing and iterative erosion and dilation was developed to automatically segment different root types, and identify local root traits (e.g., length, diameter of the main root, and length, diameter, initial angle, and the number of nodal roots or lateral roots). One maize (Zea mays L.) cultivar and two rapeseed (Brassica napus L.) cultivars at different growth stages were selected to test the performance of the automated imaging system and 3D imaging data processing pipeline. CONCLUSIONS The results demonstrated the capabilities of the proposed imaging and analytical system for high-throughput phenotyping of root traits for both monocotyledons and dicotyledons across growth stages. The proposed system offers a potential tool to further explore the 3D RSA for improving root traits and agronomic qualities of crops.
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Affiliation(s)
- Qian Wu
- IGRB-IAI Joint Laboratory of Germplasm Resources Innovation & Information Utilization, YuanQi-IAI Joint Laboratory for Agricultural Digital Twin, Institute of Agricultural Information, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China
| | - Jie Wu
- Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China
| | - Pengcheng Hu
- School of Agriculture and Food Sciences, The University of Queensland, St. Lucia, QLD, 4072, Australia
| | - Weixin Zhang
- IGRB-IAI Joint Laboratory of Germplasm Resources Innovation & Information Utilization, YuanQi-IAI Joint Laboratory for Agricultural Digital Twin, Institute of Agricultural Information, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
| | - Yuntao Ma
- College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Kun Yu
- IGRB-IAI Joint Laboratory of Germplasm Resources Innovation & Information Utilization, Institute of Germplasm Resources and Biotechnology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China
| | - Yan Guo
- College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jing Cao
- IGRB-IAI Joint Laboratory of Germplasm Resources Innovation & Information Utilization, YuanQi-IAI Joint Laboratory for Agricultural Digital Twin, Institute of Agricultural Information, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China
| | - Huayong Li
- IGRB-IAI Joint Laboratory of Germplasm Resources Innovation & Information Utilization, Institute of Germplasm Resources and Biotechnology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China
| | - Baiming Li
- IGRB-IAI Joint Laboratory of Germplasm Resources Innovation & Information Utilization, YuanQi-IAI Joint Laboratory for Agricultural Digital Twin, Institute of Agricultural Information, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
| | - Yuyang Yao
- College of Electronics & Information Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, Jiangsu, China
| | - Hongxin Cao
- IGRB-IAI Joint Laboratory of Germplasm Resources Innovation & Information Utilization, YuanQi-IAI Joint Laboratory for Agricultural Digital Twin, Institute of Agricultural Information, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China.
| | - Wenyu Zhang
- IGRB-IAI Joint Laboratory of Germplasm Resources Innovation & Information Utilization, YuanQi-IAI Joint Laboratory for Agricultural Digital Twin, Institute of Agricultural Information, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China.
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu, China.
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Hu B, Wang W, Chen J, Liu Y, Chu C. Genetic improvement toward nitrogen-use efficiency in rice: Lessons and perspectives. MOLECULAR PLANT 2023; 16:64-74. [PMID: 36380584 DOI: 10.1016/j.molp.2022.11.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 11/08/2022] [Accepted: 11/11/2022] [Indexed: 06/16/2023]
Abstract
The indispensable role of nitrogen fertilizer in ensuring world food security together with the severe threats it poses to the ecosystem makes the usage of nitrogen fertilizer a major challenge for sustainable agriculture. Genetic improvement of crops with high nitrogen-use efficiency (NUE) is one of the most feasible solutions for tackling this challenge. In the last two decades, extensive efforts toward dissecting the variation of NUE-related traits and the underlying genetic basis in different germplasms have been made, and a series of achievements have been obtained in crops, especially in rice. Here, we summarize the approaches used for genetic dissection of NUE and the functions of the causal genes in modulating NUE as well as their applications in NUE improvement in rice. Strategies for exploring the variants controlling NUE and breeding future crops with "less-input-more-output" for sustainable agriculture are also proposed.
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Affiliation(s)
- Bin Hu
- Guangdong Laboratory for Lingnan Modern Agriculture, State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, South China Agricultural University, Guangzhou 510642, China; Key Laboratory for Enhancing Resource Use Efficiency of Crops in South China, Ministry of Agriculture and Rural Affairs, South China Agricultural University, 510642, Guangzhou, China.
| | - Wei Wang
- Guangdong Laboratory for Lingnan Modern Agriculture, State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, South China Agricultural University, Guangzhou 510642, China; Key Laboratory for Enhancing Resource Use Efficiency of Crops in South China, Ministry of Agriculture and Rural Affairs, South China Agricultural University, 510642, Guangzhou, China
| | - Jiajun Chen
- Guangdong Laboratory for Lingnan Modern Agriculture, State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, South China Agricultural University, Guangzhou 510642, China
| | - Yongqiang Liu
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, The Innovative Academy for Seed Design, Chinese Academy of Sciences, Beijing 100101, China
| | - Chengcai Chu
- Guangdong Laboratory for Lingnan Modern Agriculture, State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, South China Agricultural University, Guangzhou 510642, China; Key Laboratory for Enhancing Resource Use Efficiency of Crops in South China, Ministry of Agriculture and Rural Affairs, South China Agricultural University, 510642, Guangzhou, China.
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Fernandez R, Crabos A, Maillard M, Nacry P, Pradal C. High-throughput and automatic structural and developmental root phenotyping on Arabidopsis seedlings. PLANT METHODS 2022; 18:127. [PMID: 36457133 PMCID: PMC9714072 DOI: 10.1186/s13007-022-00960-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 11/18/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND High-throughput phenotyping is crucial for the genetic and molecular understanding of adaptive root system development. In recent years, imaging automata have been developed to acquire the root system architecture of many genotypes grown in Petri dishes to explore the Genetic x Environment (GxE) interaction. There is now an increasing interest in understanding the dynamics of the adaptive responses, such as the organ apparition or the growth rate. However, due to the increasing complexity of root architectures in development, the accurate description of the topology, geometry, and dynamics of a growing root system remains a challenge. RESULTS We designed a high-throughput phenotyping method, combining an imaging device and an automatic analysis pipeline based on registration and topological tracking, capable of accurately describing the topology and geometry of observed root systems in 2D + t. The method was tested on a challenging Arabidopsis seedling dataset, including numerous root occlusions and crossovers. Static phenes are estimated with high accuracy ([Formula: see text] and [Formula: see text] for primary and second-order roots length, respectively). These performances are similar to state-of-the-art results obtained on root systems of equal or lower complexity. In addition, our pipeline estimates dynamic phenes accurately between two successive observations ([Formula: see text] for lateral root growth). CONCLUSIONS We designed a novel method of root tracking that accurately and automatically measures both static and dynamic parameters of the root system architecture from a novel high-throughput root phenotyping platform. It has been used to characterise developing patterns of root systems grown under various environmental conditions. It provides a solid basis to explore the GxE interaction controlling the dynamics of root system architecture adaptive responses. In future work, our approach will be adapted to a wider range of imaging configurations and species.
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Affiliation(s)
- Romain Fernandez
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Amandine Crabos
- Institute for Plant Sciences of Montpellier (IPSiM), Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France
| | - Morgan Maillard
- Institute for Plant Sciences of Montpellier (IPSiM), Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France
| | - Philippe Nacry
- Institute for Plant Sciences of Montpellier (IPSiM), Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France.
| | - Christophe Pradal
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France.
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France.
- Inria & LIRMM, Univ Montpellier, CNRS, Montpellier, France.
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6
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Abbas M, Abid MA, Meng Z, Abbas M, Wang P, Lu C, Askari M, Akram U, Ye Y, Wei Y, Wang Y, Guo S, Liang C, Zhang R. Integrating advancements in root phenotyping and genome-wide association studies to open the root genetics gateway. PHYSIOLOGIA PLANTARUM 2022; 174:e13787. [PMID: 36169590 DOI: 10.1111/ppl.13787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/12/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
Plant adaptation to challenging environmental conditions around the world has made root growth and development an important research area for plant breeders and scientists. Targeted manipulation of root system architecture (RSA) to increase water and nutrient use efficiency can minimize the adverse effects of climate change on crop production. However, phenotyping of RSA is a major bottleneck since the roots are hidden in the soil. Recently the development of 2- and 3D root imaging techniques combined with the genome-wide association studies (GWASs) have opened up new research tools to identify the genetic basis of RSA. These approaches provide a comprehensive understanding of the RSA, by accelerating the identification and characterization of genes involved in root growth and development. This review summarizes the latest developments in phenotyping techniques and GWAS for RSA, which are used to map important genes regulating various aspects of RSA under varying environmental conditions. Furthermore, we discussed about the state-of-the-art image analysis tools integrated with various phenotyping platforms for investigating and quantifying root traits with the highest phenotypic plasticity in both artificial and natural environments which were used for large scale association mapping studies, leading to the identification of RSA phenotypes and their underlying genetics with the greatest potential for RSA improvement. In addition, challenges in root phenotyping and GWAS are also highlighted, along with future research directions employing machine learning and pan-genomics approaches.
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Affiliation(s)
- Mubashir Abbas
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Muhammad Ali Abid
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Zhigang Meng
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Manzar Abbas
- School of Agriculture, Forestry and Food Engineering, Yibin University, Yibin, China
| | - Peilin Wang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Chao Lu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Muhammad Askari
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Umar Akram
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yulu Ye
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yunxiao Wei
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yuan Wang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Sandui Guo
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Chengzhen Liang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Rui Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
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7
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Herrero-Huerta M, Raumonen P, Gonzalez-Aguilera D. 4DRoot: Root phenotyping software for temporal 3D scans by X-ray computed tomography. FRONTIERS IN PLANT SCIENCE 2022; 13:986856. [PMID: 36212319 PMCID: PMC9539560 DOI: 10.3389/fpls.2022.986856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 08/23/2022] [Indexed: 06/16/2023]
Abstract
Currently, plant phenomics is considered the key to reducing the genotype-to-phenotype knowledge gap in plant breeding. In this context, breakthrough imaging technologies have demonstrated high accuracy and reliability. The X-ray computed tomography (CT) technology can noninvasively scan roots in 3D; however, it is urgently required to implement high-throughput phenotyping procedures and analyses to increase the amount of data to measure more complex root phenotypic traits. We have developed a spatial-temporal root architectural modeling software tool based on 4D data from temporal X-ray CT scans. Through a cylinder fitting, we automatically extract significant root architectural traits, distribution, and hierarchy. The open-source software tool is named 4DRoot and implemented in MATLAB. The source code is freely available at https://github.com/TIDOP-USAL/4DRoot. In this research, 3D root scans from the black walnut tree were analyzed, a punctual scan for the spatial study and a weekly time-slot series for the temporal one. 4DRoot provides breeders and root biologists an objective and useful tool to quantify carbon sequestration throw trait extraction. In addition, 4DRoot could help plant breeders to improve plants to meet the food, fuel, and fiber demands in the future, in order to increase crop yield while reducing farming inputs.
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Affiliation(s)
- Monica Herrero-Huerta
- Department of Cartographic and Land Engineering, Higher Polytechnic School of Ávila, Universidad de Salamanca, Ávila, Spain
| | - Pasi Raumonen
- Department of Computing Sciences, Tampere University, Tampere, Finland
| | - Diego Gonzalez-Aguilera
- Department of Cartographic and Land Engineering, Higher Polytechnic School of Ávila, Universidad de Salamanca, Ávila, Spain
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8
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Li A, Zhu L, Xu W, Liu L, Teng G. Recent advances in methods for in situ root phenotyping. PeerJ 2022; 10:e13638. [PMID: 35795176 PMCID: PMC9252182 DOI: 10.7717/peerj.13638] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/06/2022] [Indexed: 01/17/2023] Open
Abstract
Roots assist plants in absorbing water and nutrients from soil. Thus, they are vital to the survival of nearly all land plants, considering that plants cannot move to seek optimal environmental conditions. Crop species with optimal root system are essential for future food security and key to improving agricultural productivity and sustainability. Root systems can be improved and bred to acquire soil resources efficiently and effectively. This can also reduce adverse environmental impacts by decreasing the need for fertilization and fresh water. Therefore, there is a need to improve and breed crop cultivars with favorable root system. However, the lack of high-throughput root phenotyping tools for characterizing root traits in situ is a barrier to breeding for root system improvement. In recent years, many breakthroughs in the measurement and analysis of roots in a root system have been made. Here, we describe the major advances in root image acquisition and analysis technologies and summarize the advantages and disadvantages of each method. Furthermore, we look forward to the future development direction and trend of root phenotyping methods. This review aims to aid researchers in choosing a more appropriate method for improving the root system.
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Affiliation(s)
- Anchang Li
- School of Information Science and Technology, Hebei Agricultrual University, Baoding, Hebei, China
| | - Lingxiao Zhu
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultrual University, Baoding, Hebei, China
| | - Wenjun Xu
- School of Information Science and Technology, Hebei Agricultrual University, Baoding, Hebei, China
| | - Liantao Liu
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultrual University, Baoding, Hebei, China
| | - Guifa Teng
- School of Information Science and Technology, Hebei Agricultrual University, Baoding, Hebei, China
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