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Ling Y, Zhao Q, Liu W, Wei K, Bao R, Song W, Nie X. Detection and characterization of spike architecture based on deep learning and X-ray computed tomography in barley. PLANT METHODS 2023; 19:115. [PMID: 37891590 PMCID: PMC10604417 DOI: 10.1186/s13007-023-01096-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023]
Abstract
BACKGROUND Spike is the grain-bearing organ in cereal crops, which is a key proxy indicator determining the grain yield and quality. Machine learning methods for image analysis of spike-related phenotypic traits not only hold the promise for high-throughput estimating grain production and quality, but also lay the foundation for better dissection of the genetic basis for spike development. Barley (Hordeum vulgare L.) is one of the most important crops globally, ranking as the fourth largest cereal crop in terms of cultivated area and total yield. However, image analysis of spike-related traits in barley, especially based on CT-scanning, remains elusive at present. RESULTS In this study, we developed a non-invasive, high-throughput approach to quantitatively measuring the multitude of spike architectural traits in barley through combining X-ray computed tomography (CT) and a deep learning model (UNet). Firstly, the spikes of 11 barley accessions, including 2 wild barley, 3 landraces and 6 cultivars were used for X-ray CT scanning to obtain the tomographic images. And then, an optimized 3D image processing method was used to point cloud data to generate the 3D point cloud images of spike, namely 'virtual' spike, which is then used to investigate internal structures and morphological traits of barley spikes. Furthermore, the virtual spike-related traits, such as spike length, grain number per spike, grain volume, grain surface area, grain length and grain width as well as grain thickness were efficiently and non-destructively quantified. The virtual values of these traits were highly consistent with the actual value using manual measurement, demonstrating the accuracy and reliability of the developed model. The reconstruction process took 15 min approximately, 10 min for CT scanning and 5 min for imaging and features extraction, respectively. CONCLUSIONS This study provides an efficient, non-invasive and useful tool for dissecting barley spike architecture, which will contribute to high-throughput phenotyping and breeding for high yield in barley and other crops.
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Affiliation(s)
- Yimin Ling
- State Key Laboratory of Crop Stress Biology in Arid Areas and College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Qinlong Zhao
- State Key Laboratory of Crop Stress Biology in Arid Areas and College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Wenxin Liu
- State Key Laboratory of Crop Stress Biology in Arid Areas and College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Kexu Wei
- State Key Laboratory of Crop Stress Biology in Arid Areas and College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Runfei Bao
- State Key Laboratory of Crop Stress Biology in Arid Areas and College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Weining Song
- State Key Laboratory of Crop Stress Biology in Arid Areas and College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
- ICARDA-NWSUAF Joint Research Centre, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Xiaojun Nie
- State Key Laboratory of Crop Stress Biology in Arid Areas and College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China.
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Feng X, Wang Z, Zeng Z, Zhou Y, Lan Y, Zou W, Gong H, Qi L. Size measurement and filled/unfilled detection of rice grains using backlight image processing. FRONTIERS IN PLANT SCIENCE 2023; 14:1213486. [PMID: 37900751 PMCID: PMC10613065 DOI: 10.3389/fpls.2023.1213486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 09/20/2023] [Indexed: 10/31/2023]
Abstract
Measurements of rice physical traits, such as length, width, and percentage of filled/unfilled grains, are essential steps of rice breeding. A new approach for measuring the physical traits of rice grains for breeding purposes was presented in this study, utilizing image processing techniques. Backlight photography was used to capture a grayscale image of a group of rice grains, which was then analyzed using a clustering algorithm to differentiate between filled and unfilled grains based on their grayscale values. The impact of backlight intensity on the accuracy of the method was also investigated. The results show that the proposed method has excellent accuracy and high efficiency. The mean absolute percentage error of the method was 0.24% and 1.36% in calculating the total number of grain particles and distinguishing the number of filled grains, respectively. The grain size was also measured with a little margin of error. The mean absolute percentage error of grain length measurement was 1.11%, while the measurement error of grain width was 4.03%. The method was found to be highly accurate, non-destructive, and cost-effective when compared to conventional methods, making it a promising approach for characterizing physical traits for crop breeding.
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Affiliation(s)
- Xiao Feng
- College of Engineering, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
| | - Zhiqi Wang
- College of Engineering, South China Agricultural University, Guangzhou, Guangdong, China
| | - Zhiwei Zeng
- Department of Agricultural Engineering Technology, University of Wisconsin-River Falls, River Falls, WI, United States
| | - Yuhao Zhou
- College of Engineering, South China Agricultural University, Guangzhou, Guangdong, China
| | - Yunting Lan
- College of Engineering, South China Agricultural University, Guangzhou, Guangdong, China
| | - Wei Zou
- R&D Center, Top-Leading Intelligent Technology Co. ltd., Guangzhou, Guangdong, China
| | - Hao Gong
- College of Engineering, South China Agricultural University, Guangzhou, Guangdong, China
| | - Long Qi
- College of Engineering, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
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Haghshenas A, Emam Y, Jafarizadeh S. Wheat grain width: a clue for re-exploring visual indicators of grain weight. PLANT METHODS 2022; 18:58. [PMID: 35505376 PMCID: PMC9063171 DOI: 10.1186/s13007-022-00891-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 04/13/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Mean grain weight (MGW) is among the most frequently measured parameters in wheat breeding and physiology. Although in the recent decades, various wheat grain analyses (e.g. counting, and determining the size, color, or shape features) have been facilitated, thanks to the automated image processing systems, MGW estimations have been limited to using few number of image-derived indices; i.e. mainly the linear or power models developed based on the projected area (Area). Following a preliminary observation which indicated the potential of grain width in improving the predictions, the present study was conducted to explore more efficient indices for increasing the precision of image-based MGW estimations. For this purpose, an image archive of the grains was processed, which were harvested from a 2-year field experiment carried out with 3 replicates under two irrigation conditions and included 15 cultivar mixture treatments (so the archive was consisted of 180 images including more than 72,000 grains). RESULTS It was observed that among the more than 30 evaluated indices of grain size and shape, indicators of grain width (i.e. Minor & MinFeret) along with 8 other empirical indices had a higher correlation with MGW, compared with Area. The most precise MGW predictions were obtained using the Area × Circularity, Perimeter × Circularity, and Area/Perimeter indices. Furthermore, it was found that (i) grain width and the Area/Perimeter ratio were the common factors in the structure of the superior predictive indices; and (ii) the superior indices had the highest correlation with grain width, rather than with their mathematical components. Moreover, comparative efficiency of the superior indices almost remained stable across the 4 environmental conditions. Eventually, using the selected indices, ten simple linear models were developed and validated for MGW prediction, which indicated a relatively higher precision than the current Area-based models. The considerable effect of enhancing image resolution on the precision of the models has been also evidenced. CONCLUSIONS It is expected that the findings of the present study, along with the simple predictive linear models developed and validated using new image-derived indices, could improve the precision of the image-based MGW estimations, and consequently facilitate wheat breeding and physiological assessments.
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Affiliation(s)
- Abbas Haghshenas
- Department of Plant Production and Genetics, Shiraz University, Shiraz, Iran
| | - Yahya Emam
- Department of Plant Production and Genetics, Shiraz University, Shiraz, Iran.
| | - Saeid Jafarizadeh
- Vision Lab, Electrical and Computer Engineering School, Shiraz University, Shiraz, Iran
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Huang C, Qin Z, Hua X, Zhang Z, Xiao W, Liang X, Song P, Yang W. An Intelligent Analysis Method for 3D Wheat Grain and Ventral Sulcus Traits Based on Structured Light Imaging. FRONTIERS IN PLANT SCIENCE 2022; 13:840908. [PMID: 35498671 PMCID: PMC9044079 DOI: 10.3389/fpls.2022.840908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 03/14/2022] [Indexed: 06/14/2023]
Abstract
The wheat grain three-dimensional (3D) phenotypic characters are of great significance for final yield and variety breeding, and the ventral sulcus traits are the important factors to the wheat flour yield. The wheat grain trait measurements are necessary; however, the traditional measurement method is still manual, which is inefficient, subjective, and labor intensive; moreover, the ventral sulcus traits can only be obtained by destructive measurement. In this paper, an intelligent analysis method based on the structured light imaging has been proposed to extract the 3D wheat grain phenotypes and ventral sulcus traits. First, the 3D point cloud data of wheat grain were obtained by the structured light scanner, and then, the specified point cloud processing algorithms including single grain segmentation and ventral sulcus location have been designed; finally, 28 wheat grain 3D phenotypic characters and 4 ventral sulcus traits have been extracted. To evaluate the best experimental conditions, three-level orthogonal experiments, which include rotation angle, scanning angle, and stage color factors, were carried out on 125 grains of 5 wheat varieties, and the results demonstrated that optimum conditions of rotation angle, scanning angle, and stage color were 30°, 37°, black color individually. Additionally, the results also proved that the mean absolute percentage errors (MAPEs) of wheat grain length, width, thickness, and ventral sulcus depth were 1.83, 1.86, 2.19, and 4.81%. Moreover, the 500 wheat grains of five varieties were used to construct and validate the wheat grain weight model by 32 phenotypic traits, and the cross-validation results showed that the R 2 of the models ranged from 0.77 to 0.83. Finally, the wheat grain phenotype extraction and grain weight prediction were integrated into the specialized software. Therefore, this method was demonstrated to be an efficient and effective way for wheat breeding research.
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Affiliation(s)
- Chenglong Huang
- College of Engineering, Huazhong Agricultural University, Wuhan, China
| | - Zhijie Qin
- College of Engineering, Huazhong Agricultural University, Wuhan, China
| | - Xiangdong Hua
- College of Engineering, Huazhong Agricultural University, Wuhan, China
| | - Zhongfu Zhang
- College of Engineering, Huazhong Agricultural University, Wuhan, China
| | - Wenli Xiao
- College of Engineering, Huazhong Agricultural University, Wuhan, China
| | - Xiuying Liang
- College of Engineering, Huazhong Agricultural University, Wuhan, China
| | - Peng Song
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, China
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, China
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5
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Synchrotron Based X-ray Microtomography Reveals Cellular Morphological Features of Developing Wheat Grain. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Wheat is one of the most important crops in the world, mainly used for human consumption and animal feed. To overcome the increasing demand in wheat production, it is necessary to better understand the mechanisms involved in the growth of the wheat grain. X-ray computed tomography is an efficient method for the non-destructive investigation of the 3D architecture of biological specimens, which does not require staining, sectioning, or inclusion. In particular, phase-contrast tomography results in images with better contrast and an increased resolution compared to that obtained with laboratory tomography devices. The aim of this study was to investigate the potential of phase-contrast tomography for the study of the anatomy of the wheat grain at early stages of development. We provided 3D images of entire grains at various development stages. The image analysis allowed identifying a large number of tissues, and to visualize individual cells. Using a high-resolution setup, finer details were obtained, making it possible to identify additional tissues. Three-dimensional rendering of the grain also revealed the pattern resulting from the epidermis cells. X-ray phase-contrast tomography appears as a promising imaging method for the study of the 3D anatomy of plant organs and tissues.
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Zhou H, Riche AB, Hawkesford MJ, Whalley WR, Atkinson BS, Sturrock CJ, Mooney SJ. Determination of wheat spike and spikelet architecture and grain traits using X-ray Computed Tomography imaging. PLANT METHODS 2021; 17:26. [PMID: 33750418 PMCID: PMC7945051 DOI: 10.1186/s13007-021-00726-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 02/26/2021] [Indexed: 05/28/2023]
Abstract
BACKGROUND Wheat spike architecture is a key determinant of multiple grain yield components and detailed examination of spike morphometric traits is beneficial to explain wheat grain yield and the effects of differing agronomy and genetics. However, quantification of spike morphometric traits has been very limited because it relies on time-consuming manual measurements. RESULTS In this study, using X-ray Computed Tomography imaging, we proposed a method to efficiently detect the 3D architecture of wheat spikes and component spikelets by clustering grains based on their Euclidean distance and relative positions. Morphometric characteristics of wheat spikelets and grains, e.g., number, size and spatial distribution along the spike can be determined. Two commercial wheat cultivars, one old, Maris Widgeon, and one modern, Siskin, were studied as examples. The average grain volume of Maris Widgeon and Siskin did not differ, but Siskin had more grains per spike and therefore greater total grain volume per spike. The spike length and spikelet number were not statistically different between the two cultivars. However, Siskin had a higher spikelet density (number of spikelets per unit spike length), with more grains and greater grain volume per spikelet than Maris Widgeon. Spatial distribution analysis revealed the number of grains, the average grain volume and the total grain volume of individual spikelets varied along the spike. Siskin had more grains and greater grain volumes per spikelet from spikelet 6, but not spikelet 1-5, compared with Maris Widgeon. The distribution of average grain volume along the spike was similar for the two wheat cultivars. CONCLUSION The proposed method can efficiently extract spike, spikelet and grain morphometric traits of different wheat cultivars, which can contribute to a more detailed understanding of the sink of wheat grain yield.
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Affiliation(s)
- Hu Zhou
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD Leicestershire UK
| | | | | | | | - Brian S. Atkinson
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD Leicestershire UK
| | - Craig J. Sturrock
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD Leicestershire UK
| | - Sacha J. Mooney
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD Leicestershire UK
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7
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Wu D, Wu D, Feng H, Duan L, Dai G, Liu X, Wang K, Yang P, Chen G, Gay AP, Doonan JH, Niu Z, Xiong L, Yang W. A deep learning-integrated micro-CT image analysis pipeline for quantifying rice lodging resistance-related traits. PLANT COMMUNICATIONS 2021; 2:100165. [PMID: 33898978 PMCID: PMC8060729 DOI: 10.1016/j.xplc.2021.100165] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 11/07/2020] [Accepted: 01/26/2021] [Indexed: 05/20/2023]
Abstract
Lodging is a common problem in rice, reducing its yield and mechanical harvesting efficiency. Rice architecture is a key aspect of its domestication and a major factor that limits its high productivity. The ideal rice culm structure, including major_axis_culm, minor axis_culm, and wall thickness_culm, is critical for improving lodging resistance. However, the traditional method of measuring rice culms is destructive, time consuming, and labor intensive. In this study, we used a high-throughput micro-CT-RGB imaging system and deep learning (SegNet) to develop a high-throughput micro-CT image analysis pipeline that can extract 24 rice culm morphological traits and lodging resistance-related traits. When manual and automatic measurements were compared at the mature stage, the mean absolute percentage errors for major_axis_culm, minor_axis_culm, and wall_thickness_culm in 104 indica rice accessions were 6.03%, 5.60%, and 9.85%, respectively, and the R2 values were 0.799, 0.818, and 0.623. We also built models of bending stress using culm traits at the mature and tillering stages, and the R2 values were 0.722 and 0.544, respectively. The modeling results indicated that this method can quantify lodging resistance nondestructively, even at an early growth stage. In addition, we also evaluated the relationships of bending stress to shoot dry weight, culm density, and drought-related traits and found that plants with greater resistance to bending stress had slightly higher biomass, culm density, and culm area but poorer drought resistance. In conclusion, we developed a deep learning-integrated micro-CT image analysis pipeline to accurately quantify the phenotypic traits of rice culms in ∼4.6 min per plant; this pipeline will assist in future high-throughput screening of large rice populations for lodging resistance.
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Affiliation(s)
- Di Wu
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Key Laboratory of Agricultural Bioinformatics and College of Engineering, Huazhong Agricultural University, Wuhan 430070, PR China
- School of Information Engineering, Wuhan Technology and Business University, Wuhan 430065, PR China
| | - Dan Wu
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Key Laboratory of Agricultural Bioinformatics and College of Engineering, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Hui Feng
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Key Laboratory of Agricultural Bioinformatics and College of Engineering, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Lingfeng Duan
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Key Laboratory of Agricultural Bioinformatics and College of Engineering, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Guoxing Dai
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Key Laboratory of Agricultural Bioinformatics and College of Engineering, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Xiao Liu
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Key Laboratory of Agricultural Bioinformatics and College of Engineering, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Kang Wang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Key Laboratory of Agricultural Bioinformatics and College of Engineering, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Peng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Key Laboratory of Agricultural Bioinformatics and College of Engineering, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Guoxing Chen
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Key Laboratory of Agricultural Bioinformatics and College of Engineering, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Alan P. Gay
- The National Plant Phenomics Centre, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - John H. Doonan
- The National Plant Phenomics Centre, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - Zhiyou Niu
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Key Laboratory of Agricultural Bioinformatics and College of Engineering, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Lizhong Xiong
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Key Laboratory of Agricultural Bioinformatics and College of Engineering, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Key Laboratory of Agricultural Bioinformatics and College of Engineering, Huazhong Agricultural University, Wuhan 430070, PR China
- Corresponding author
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8
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Besançon L, Rondet E, Grabulos J, Lullien-Pellerin V, Lhomond L, Cuq B. Study of the microstructure of durum wheat endosperm using X-ray micro-computed tomography. J Cereal Sci 2020. [DOI: 10.1016/j.jcs.2020.103115] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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9
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Booth S, Kurtz B, de Heer MI, Mooney SJ, Sturrock CJ. Tracking wireworm burrowing behaviour in soil over time using 3D X-ray computed tomography. PEST MANAGEMENT SCIENCE 2020; 76:2653-2662. [PMID: 32112498 DOI: 10.1002/ps.5808] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 02/14/2020] [Accepted: 02/28/2020] [Indexed: 06/10/2023]
Abstract
BACKGROUND Wireworms (larvae of the click beetle, Elateridae) are a significant agricultural pest, causing crop damage and reducing yields globally. Owing to the complex nature and opacity of the soil environment, research to investigate wireworm behaviour in situ has been scarce. X-ray computed tomography (CT) has previously been demonstrated as a powerful tool to independently visualise the 3D root system architecture, macroinvertebrate movement and distribution of burrow systems in soil, but not simultaneously within the same sample. In this study, we apply X-ray CT to visualise and quantify wireworms, their burrow systems and the root architecture of two contrasting crop species (Hordeum vulgare and Zea mays) in a soil pot experiment scanned at different time intervals. RESULTS The majority of wireworm burrows were produced within the first 20 h post inoculation, suggesting that burrow systems are established quickly and persist at a similar volume. There was a significant difference in the volume of burrow systems produced by wireworms between the two crop species suggesting differences in wireworm behaviour elicited by crop species. There was no significant correlation between burrow volume and either root volume or surface area, indicating this behavioural difference is caused by factor(s) other than the mass of root systems. CONCLUSION X-ray CT shows potential as a non-destructive technique to quantify the interaction of wireworms in the natural soil environment with crop roots, and aid the development of effective pest management strategies to minimise their negative impact on crop production. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Samuel Booth
- School of Biosciences, University of Nottingham, Loughborough, UK
| | - Benedikt Kurtz
- Syngenta, Syngenta Crop Protection, Stein Research Center, Stein, Switzerland
| | | | - Sacha J Mooney
- School of Biosciences, University of Nottingham, Loughborough, UK
| | - Craig J Sturrock
- School of Biosciences, University of Nottingham, Loughborough, UK
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10
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Schmidt J, Claussen J, Wörlein N, Eggert A, Fleury D, Garnett T, Gerth S. Drought and heat stress tolerance screening in wheat using computed tomography. PLANT METHODS 2020; 16:15. [PMID: 32082405 PMCID: PMC7017466 DOI: 10.1186/s13007-020-00565-w] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 02/06/2020] [Indexed: 05/19/2023]
Abstract
BACKGROUND Improving abiotic stress tolerance in wheat requires large scale screening of yield components such as seed weight, seed number and single seed weight, all of which is very laborious, and a detailed analysis of seed morphology is time-consuming and visually often impossible. Computed tomography offers the opportunity for much faster and more accurate assessment of yield components. RESULTS An X-ray computed tomographic analysis was carried out on 203 very diverse wheat accessions which have been exposed to either drought or combined drought and heat stress. Results demonstrated that our computed tomography pipeline was capable of evaluating grain set with an accuracy of 95-99%. Most accessions exposed to combined drought and heat stress developed smaller, shrivelled seeds with an increased seed surface. As expected, seed weight and seed number per ear as well as single seed size were significantly reduced under combined drought and heat compared to drought alone. Seed weight along the ear was significantly reduced at the top and bottom of the wheat spike. CONCLUSIONS We were able to establish a pipeline with a higher throughput with scanning times of 7 min per ear and accuracy than previous pipelines predicting a set of agronomical important seed traits and to visualize even more complex traits such as seed deformations. The pipeline presented here could be scaled up to use for high throughput, high resolution phenotyping of tens of thousands of heads, greatly accelerating breeding efforts to improve abiotic stress tolerance.
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Affiliation(s)
- Jessica Schmidt
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA Australia
| | - Joelle Claussen
- Fraunhofer Development Center X-Ray Technology, Fürth, Germany
| | - Norbert Wörlein
- Fraunhofer Development Center X-Ray Technology, Fürth, Germany
| | - Anja Eggert
- Fraunhofer Development Center X-Ray Technology, Fürth, Germany
| | - Delphine Fleury
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA Australia
- Innolea, 6 chemin de Panedautes, 31700 Mondonville, France
| | - Trevor Garnett
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA Australia
| | - Stefan Gerth
- Fraunhofer Development Center X-Ray Technology, Fürth, Germany
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11
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Hu W, Zhang C, Jiang Y, Huang C, Liu Q, Xiong L, Yang W, Chen F. Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed Tomography. PLANT PHENOMICS (WASHINGTON, D.C.) 2020; 2020:3414926. [PMID: 33313550 PMCID: PMC7706343 DOI: 10.34133/2020/3414926] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 03/27/2020] [Indexed: 05/11/2023]
Abstract
The traits of rice panicles play important roles in yield assessment, variety classification, rice breeding, and cultivation management. Most traditional grain phenotyping methods require threshing and thus are time-consuming and labor-intensive; moreover, these methods cannot obtain 3D grain traits. In this work, based on X-ray computed tomography, we proposed an image analysis method to extract twenty-two 3D grain traits. After 104 samples were tested, the R 2 values between the extracted and manual measurements of the grain number and grain length were 0.980 and 0.960, respectively. We also found a high correlation between the total grain volume and weight. In addition, the extracted 3D grain traits were used to classify the rice varieties, and the support vector machine classifier had a higher recognition accuracy than the stepwise discriminant analysis and random forest classifiers. In conclusion, we developed a 3D image analysis pipeline to extract rice grain traits using X-ray computed tomography that can provide more 3D grain information and could benefit future research on rice functional genomics and rice breeding.
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Affiliation(s)
- Weijuan Hu
- Crop Phenomics Joint Research Center, Wuhan 430070, China
- Institute of Genetics and Developmental Biology Chinese Academy of Sciences, Beijing 100101, China
| | - Can Zhang
- Crop Phenomics Joint Research Center, Wuhan 430070, China
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, and Key Laboratory of Ministry of Education for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yuqiang Jiang
- Crop Phenomics Joint Research Center, Wuhan 430070, China
- Institute of Genetics and Developmental Biology Chinese Academy of Sciences, Beijing 100101, China
| | - Chenglong Huang
- Crop Phenomics Joint Research Center, Wuhan 430070, China
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Agricultural Bioinformatics Key Laboratory of Hubei Province, and College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
| | - Qian Liu
- Crop Phenomics Joint Research Center, Wuhan 430070, China
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, and Key Laboratory of Ministry of Education for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Lizhong Xiong
- Crop Phenomics Joint Research Center, Wuhan 430070, China
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Agricultural Bioinformatics Key Laboratory of Hubei Province, and College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
| | - Wanneng Yang
- Crop Phenomics Joint Research Center, Wuhan 430070, China
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Agricultural Bioinformatics Key Laboratory of Hubei Province, and College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
| | - Fan Chen
- Crop Phenomics Joint Research Center, Wuhan 430070, China
- Institute of Genetics and Developmental Biology Chinese Academy of Sciences, Beijing 100101, China
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12
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Le TDQ, Alvarado C, Girousse C, Legland D, Chateigner-Boutin AL. Use of X-ray micro computed tomography imaging to analyze the morphology of wheat grain through its development. PLANT METHODS 2019; 15:84. [PMID: 31384289 PMCID: PMC6668075 DOI: 10.1186/s13007-019-0468-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 07/23/2019] [Indexed: 05/23/2023]
Abstract
BACKGROUND Wheat is one of the most important staple source in the world for human consumption, animal feed and industrial raw materials. To deal with the global and increasing population demand, enhancing crop yield by increasing the final weight of individual grain is considered as a feasible solution. Morphometric analysis of wheat grain plays an important role in tracking and understanding developmental processes by assessing potential impacts on grains properties, size and shape that are major determinants of final grain weight. X-ray micro computed tomography (μCT) is a very powerful non-invasive imaging tool that is able to acquire 3D images of an individual grain, enabling to assess the morphology of wheat grain and of its different compartments. Our objective is to quantify changes of morphology during growth stages of wheat grain from 3D μCT images. METHODS 3D μCT images of wheat grains were acquired at various development stages ranging from 60 to 310 degree days after anthesis. We developed robust methods for the identification of outer and inner tissues within the grains, and the extraction of morphometric features using 3D μCT images. We also developed a specific workflow for the quantification of the shape of the grain crease. RESULTS The different compartments of the grain could be semi-automatically segmented. Variations of volumes of the compartments adequately describe the different stages of grain developments. The evolution of voids within wheat grain reflects lysis of outer tissues and growth of inner tissues. The crease shape could be quantified for each grain and averaged for each stage of development, helping us understand the genesis of the grain shape. CONCLUSION This work shows that μCT acquisitions and image processing methodologies are powerful tools to extract morphometric parameters of developing wheat grain. The results of quantitative analysis revealed remarkable features of wheat grain growth. Further work will focus on building a computational model of wheat grain growth based on real 3D imaging data.
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Affiliation(s)
| | | | - Christine Girousse
- UMR GDEC, INRA, Université Clermont-Auvergne, 63000 Clermont-Ferrand, France
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13
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Hughes A, Oliveira HR, Fradgley N, Corke FMK, Cockram J, Doonan JH, Nibau C. μCT trait analysis reveals morphometric differences between domesticated temperate small grain cereals and their wild relatives. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 99:98-111. [PMID: 30868647 PMCID: PMC6618119 DOI: 10.1111/tpj.14312] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 02/22/2019] [Accepted: 03/05/2019] [Indexed: 05/29/2023]
Abstract
Wheat and barley are two of the founder crops domesticated in the Fertile Crescent, and currently represent crops of major economic importance in temperate regions. Due to impacts on yield, quality and end-use, grain morphometric traits remain an important goal for modern breeding programmes and are believed to have been selected for by human populations. To directly and accurately assess the three-dimensional (3D) characteristics of grains, we combine X-ray microcomputed tomography (μCT) imaging techniques with bespoke image analysis tools and mathematical modelling to investigate how grain size and shape vary across wild and domesticated wheat and barley. We find that grain depth and, to a lesser extent, width are major drivers of shape change and that these traits are still relatively plastic in modern bread wheat varieties. Significant changes in grain depth are also observed to be associated with differences in ploidy. Finally, we present a model that can accurately predict the wild or domesticated status of a grain from a given taxa based on the relationship between three morphometric parameters (length, width and depth) and suggest its general applicability to both archaeological identification studies and breeding programmes.
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Affiliation(s)
- Aoife Hughes
- The National Plant Phenomics CentreInstitute of Biological, Rural and Environmental Sciences (IBERS)Aberystwyth UniversityGogerddan, AberystwythSY23 3EEUK
- Present address:
Computational and Systems Biology and Crop GeneticsJohn Innes CentreNorwichNR4 7 UHUK
| | - Hugo R. Oliveira
- School of Earth and Environmental SciencesManchester Institute of BiotechnologyUniversity of ManchesterManchesterM1 7DNUK
- Present address:
Interdisciplinary Center for Archaeology and Evolution of Human Behaviour (ICArEHB)Faculdade das Ciências Humanas e SociaisUniversidade do AlgarveCampus de GambelasFaro8005‐139Portugal
| | - Nick Fradgley
- John Bingham LaboratoryNIABHuntingdon RoadCambridgeCB3 0LEUK
| | - Fiona M. K. Corke
- The National Plant Phenomics CentreInstitute of Biological, Rural and Environmental Sciences (IBERS)Aberystwyth UniversityGogerddan, AberystwythSY23 3EEUK
| | - James Cockram
- John Bingham LaboratoryNIABHuntingdon RoadCambridgeCB3 0LEUK
| | - John H. Doonan
- The National Plant Phenomics CentreInstitute of Biological, Rural and Environmental Sciences (IBERS)Aberystwyth UniversityGogerddan, AberystwythSY23 3EEUK
| | - Candida Nibau
- The National Plant Phenomics CentreInstitute of Biological, Rural and Environmental Sciences (IBERS)Aberystwyth UniversityGogerddan, AberystwythSY23 3EEUK
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14
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Komyshev E, Genaev M, Afonnikov D. Evaluation of the SeedCounter, A Mobile Application for Grain Phenotyping. FRONTIERS IN PLANT SCIENCE 2017; 7:1990. [PMID: 28101093 PMCID: PMC5209368 DOI: 10.3389/fpls.2016.01990] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Accepted: 12/15/2016] [Indexed: 05/18/2023]
Abstract
Grain morphometry in cereals is an important step in selecting new high-yielding plants. Manual assessment of parameters such as the number of grains per ear and grain size is laborious. One solution to this problem is image-based analysis that can be performed using a desktop PC. Furthermore, the effectiveness of analysis performed in the field can be improved through the use of mobile devices. In this paper, we propose a method for the automated evaluation of phenotypic parameters of grains using mobile devices running the Android operational system. The experimental results show that this approach is efficient and sufficiently accurate for the large-scale analysis of phenotypic characteristics in wheat grains. Evaluation of our application under six different lighting conditions and three mobile devices demonstrated that the lighting of the paper has significant influence on the accuracy of our method, unlike the smartphone type.
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Affiliation(s)
- Evgenii Komyshev
- Laboratory of Evolutionary Bioinformatics and Theoretical Genetics, Department of Systems Biology, Institute of Cytology and Genetics Siberian Branch of Russian Academy of Sciences (SB RAS)Novosibirsk, Russia
| | - Mikhail Genaev
- Chair of Informational Biology, Novosibirsk State UniversityNovosibirsk, Russia
| | - Dmitry Afonnikov
- Laboratory of Evolutionary Bioinformatics and Theoretical Genetics, Department of Systems Biology, Institute of Cytology and Genetics Siberian Branch of Russian Academy of Sciences (SB RAS)Novosibirsk, Russia
- Chair of Informational Biology, Novosibirsk State UniversityNovosibirsk, Russia
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15
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Hughes A, Askew K, Scotson CP, Williams K, Sauze C, Corke F, Doonan JH, Nibau C. Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography. PLANT METHODS 2017; 13:76. [PMID: 29118820 PMCID: PMC5664813 DOI: 10.1186/s13007-017-0229-8] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Accepted: 09/21/2017] [Indexed: 05/09/2023]
Abstract
BACKGROUND Wheat is one of the most widely grown crop in temperate climates for food and animal feed. In order to meet the demands of the predicted population increase in an ever-changing climate, wheat production needs to dramatically increase. Spike and grain traits are critical determinants of final yield and grain uniformity a commercially desired trait, but their analysis is laborious and often requires destructive harvest. One of the current challenges is to develop an accurate, non-destructive method for spike and grain trait analysis capable of handling large populations. RESULTS In this study we describe the development of a robust method for the accurate extraction and measurement of spike and grain morphometric parameters from images acquired by X-ray micro-computed tomography (μCT). The image analysis pipeline developed automatically identifies plant material of interest in μCT images, performs image analysis, and extracts morphometric data. As a proof of principle, this integrated methodology was used to analyse the spikes from a population of wheat plants subjected to high temperatures under two different water regimes. Temperature has a negative effect on spike height and grain number with the middle of the spike being the most affected region. The data also confirmed that increased grain volume was correlated with the decrease in grain number under mild stress. CONCLUSIONS Being able to quickly measure plant phenotypes in a non-destructive manner is crucial to advance our understanding of gene function and the effects of the environment. We report on the development of an image analysis pipeline capable of accurately and reliably extracting spike and grain traits from crops without the loss of positional information. This methodology was applied to the analysis of wheat spikes can be readily applied to other economically important crop species.
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Affiliation(s)
- Aoife Hughes
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Gogerddan, Aberystwyth, SY23 3EE UK
| | - Karen Askew
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Gogerddan, Aberystwyth, SY23 3EE UK
| | - Callum P. Scotson
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Gogerddan, Aberystwyth, SY23 3EE UK
- Present Address: Faculty of Engineering and Environment, University of Southampton, University Road, Southampton, SO17 1BJ UK
| | - Kevin Williams
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Gogerddan, Aberystwyth, SY23 3EE UK
| | - Colin Sauze
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Gogerddan, Aberystwyth, SY23 3EE UK
| | - Fiona Corke
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Gogerddan, Aberystwyth, SY23 3EE UK
| | - John H. Doonan
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Gogerddan, Aberystwyth, SY23 3EE UK
| | - Candida Nibau
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Gogerddan, Aberystwyth, SY23 3EE UK
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Dee H, French A. From image processing to computer vision: plant imaging grows up. FUNCTIONAL PLANT BIOLOGY : FPB 2015; 42:iii-v. [PMID: 32480688 DOI: 10.1071/fpv42n5_fo] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Image analysis is a field of research which, combined with novel methods of capturing images, can help to bridge the genotype-phenotype gap, where our understanding of the genotype has until now been leaps and bounds ahead of our ability to work with the phenotype. Methods of automating image capture in plant science research have increased in usage recently, as has the need to provide objective and highly accurate measures on large image datasets, thereby bringing the phenotype back to the centre of interest. In this special issue of Functional Plant Biology, we present some recent advances in the field of image analysis, and look at examples of different kinds of image processing and computer vision, which is occurring with increasing frequency in the plant sciences.
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Affiliation(s)
- Hannah Dee
- Computer Science, Aberystwyth University, Penglais, Aberystwyth SY23 3DB, UK
| | - Andrew French
- Schools of Biosciences and Computer Science, Centre for Plant Integrative Biology, University of Nottingham, Nottingham, UK
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