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Dainelli R, Bruno A, Martinelli M, Moroni D, Rocchi L, Morelli S, Ferrari E, Silvestri M, Agostinelli S, La Cava P, Toscano P. GranoScan: an AI-powered mobile app for in-field identification of biotic threats of wheat. FRONTIERS IN PLANT SCIENCE 2024; 15:1298791. [PMID: 38911980 PMCID: PMC11190326 DOI: 10.3389/fpls.2024.1298791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 05/07/2024] [Indexed: 06/25/2024]
Abstract
Capitalizing on the widespread adoption of smartphones among farmers and the application of artificial intelligence in computer vision, a variety of mobile applications have recently emerged in the agricultural domain. This paper introduces GranoScan, a freely available mobile app accessible on major online platforms, specifically designed for the real-time detection and identification of over 80 threats affecting wheat in the Mediterranean region. Developed through a co-design methodology involving direct collaboration with Italian farmers, this participatory approach resulted in an app featuring: (i) a graphical interface optimized for diverse in-field lighting conditions, (ii) a user-friendly interface allowing swift selection from a predefined menu, (iii) operability even in low or no connectivity, (iv) a straightforward operational guide, and (v) the ability to specify an area of interest in the photo for targeted threat identification. Underpinning GranoScan is a deep learning architecture named efficient minimal adaptive ensembling that was used to obtain accurate and robust artificial intelligence models. The method is based on an ensembling strategy that uses as core models two instances of the EfficientNet-b0 architecture, selected through the weighted F1-score. In this phase a very good precision is reached with peaks of 100% for pests, as well as in leaf damage and root disease tasks, and in some classes of spike and stem disease tasks. For weeds in the post-germination phase, the precision values range between 80% and 100%, while 100% is reached in all the classes for pre-flowering weeds, except one. Regarding recognition accuracy towards end-users in-field photos, GranoScan achieved good performances, with a mean accuracy of 77% and 95% for leaf diseases and for spike, stem and root diseases, respectively. Pests gained an accuracy of up to 94%, while for weeds the app shows a great ability (100% accuracy) in recognizing whether the target weed is a dicot or monocot and 60% accuracy for distinguishing species in both the post-germination and pre-flowering stage. Our precision and accuracy results conform to or outperform those of other studies deploying artificial intelligence models on mobile devices, confirming that GranoScan is a valuable tool also in challenging outdoor conditions.
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Affiliation(s)
- Riccardo Dainelli
- Institute of BioEconomy (IBE), National Research Council (CNR), Firenze, Italy
| | - Antonio Bruno
- Institute of Information Science and Technologies (ISTI), National Research Council (CNR), Pisa, Italy
| | - Massimo Martinelli
- Institute of Information Science and Technologies (ISTI), National Research Council (CNR), Pisa, Italy
| | - Davide Moroni
- Institute of Information Science and Technologies (ISTI), National Research Council (CNR), Pisa, Italy
| | - Leandro Rocchi
- Institute of BioEconomy (IBE), National Research Council (CNR), Firenze, Italy
| | | | | | | | | | | | - Piero Toscano
- Institute of BioEconomy (IBE), National Research Council (CNR), Firenze, Italy
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Jiang N, Zhu XG. Modern phenomics to empower holistic crop science, agronomy, and breeding research. J Genet Genomics 2024:S1673-8527(24)00102-4. [PMID: 38734136 DOI: 10.1016/j.jgg.2024.04.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 04/25/2024] [Accepted: 04/30/2024] [Indexed: 05/13/2024]
Abstract
Crop phenomics enables the collection of diverse plant traits for a large number of samples along different time scales, representing a greater data collection throughput compared with traditional measurements. Most modern crop phenomics use different sensors to collect reflective, emitted, and fluorescence signals, etc., from plant organs at different spatial and temporal resolutions. Such multi-modal, high-dimensional data not only accelerates basic research on crop physiology, genetics, and whole plant systems modeling, but also supports the optimization of field agronomic practices, internal environments of plant factories, and ultimately crop breeding. Major challenges and opportunities facing the current crop phenomics research community include developing community consensus or standards for data collection, management, sharing, and processing, developing capabilities to measure physiological parameters, and enabling farmers and breeders to effectively use phenomics in the field to directly support agricultural production.
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Affiliation(s)
- Ni Jiang
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
| | - Xin-Guang Zhu
- Center of Excellence for Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200032, China.
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3
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Uemura Y, Tsukagoshi H. Quantitative analysis of lateral root development with time-lapse imaging and deep neural network. QUANTITATIVE PLANT BIOLOGY 2024; 5:e1. [PMID: 38385121 PMCID: PMC10877138 DOI: 10.1017/qpb.2024.2] [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: 07/28/2023] [Revised: 01/15/2024] [Accepted: 01/21/2024] [Indexed: 02/23/2024]
Abstract
During lateral root (LR) development, morphological alteration of the developing single LR primordium occurs continuously. Precise observation of this continuous alteration is important for understanding the mechanism involved in single LR development. Recently, we reported that very long-chain fatty acids are important signalling molecules that regulate LR development. In the study, we developed an efficient method to quantify the transition of single LR developmental stages using time-lapse imaging followed by a deep neural network (DNN) analysis. In this 'insight' paper, we discuss our DNN method and the importance of time-lapse imaging in studies on plant development. Integrating DNN analysis and imaging is a powerful technique for the quantification of the timing of the transition of organ morphology; it can become an important method to elucidate spatiotemporal molecular mechanisms in plant development.
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Affiliation(s)
- Yuta Uemura
- Faculty of Agriculture, Meijo University, Nagoya, Japan
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4
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Gao Y, Zhou Q, Luo J, Xia C, Zhang Y, Yue Z. Crop-GPA: an integrated platform of crop gene-phenotype associations. NPJ Syst Biol Appl 2024; 10:15. [PMID: 38346982 PMCID: PMC10861494 DOI: 10.1038/s41540-024-00343-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 01/22/2024] [Indexed: 02/15/2024] Open
Abstract
With the increasing availability of large-scale biology data in crop plants, there is an urgent demand for a versatile platform that fully mines and utilizes the data for modern molecular breeding. We present Crop-GPA ( https://crop-gpa.aielab.net ), a comprehensive and functional open-source platform for crop gene-phenotype association data. The current Crop-GPA provides well-curated information on genes, phenotypes, and their associations (GPAs) to researchers through an intuitive interface, dynamic graphical visualizations, and efficient online tools. Two computational tools, GPA-BERT and GPA-GCN, are specifically developed and integrated into Crop-GPA, facilitating the automatic extraction of gene-phenotype associations from bio-crop literature and predicting unknown relations based on known associations. Through usage examples, we demonstrate how our platform enables the exploration of complex correlations between genes and phenotypes in crop plants. In summary, Crop-GPA serves as a valuable multi-functional resource, empowering the crop research community to gain deeper insights into the biological mechanisms of interest.
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Affiliation(s)
- Yujia Gao
- School of Information and Artificial Intelligence, Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - Qian Zhou
- School of Information and Artificial Intelligence, Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - Jiaxin Luo
- School of Information and Artificial Intelligence, Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - Chuan Xia
- School of Information and Artificial Intelligence, Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - Youhua Zhang
- School of Information and Artificial Intelligence, Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei, Anhui, 230036, China.
| | - Zhenyu Yue
- School of Information and Artificial Intelligence, Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei, Anhui, 230036, China.
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5
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Cao Y, Tian D, Tang Z, Liu X, Hu W, Zhang Z, Song S. OPIA: an open archive of plant images and related phenotypic traits. Nucleic Acids Res 2024; 52:D1530-D1537. [PMID: 37930849 PMCID: PMC10767956 DOI: 10.1093/nar/gkad975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 10/11/2023] [Accepted: 10/16/2023] [Indexed: 11/08/2023] Open
Abstract
High-throughput plant phenotype acquisition technologies have been extensively utilized in plant phenomics studies, leading to vast quantities of images and image-based phenotypic traits (i-traits) that are critically essential for accelerating germplasm screening, plant diseases identification and biotic & abiotic stress classification. Here, we present the Open Plant Image Archive (OPIA, https://ngdc.cncb.ac.cn/opia/), an open archive of plant images and i-traits derived from high-throughput phenotyping platforms. Currently, OPIA houses 56 datasets across 11 plants, comprising a total of 566 225 images with 2 417 186 labeled instances. Notably, it incorporates 56 i-traits of 93 rice and 105 wheat cultivars based on 18 644 individual RGB images, and these i-traits are further annotated based on the Plant Phenotype and Trait Ontology (PPTO) and cross-linked with GWAS Atlas. Additionally, each dataset in OPIA is assigned an evaluation score that takes account of image data volume, image resolution, and the number of labeled instances. More importantly, OPIA is equipped with useful tools for online image pre-processing and intelligent prediction. Collectively, OPIA provides open access to valuable datasets, pre-trained models, and phenotypic traits across diverse plants and thus bears great potential to play a crucial role in facilitating artificial intelligence-assisted breeding research.
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Affiliation(s)
- Yongrong Cao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dongmei Tian
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Zhixin Tang
- University of Chinese Academy of Sciences, Beijing 100049, China
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Xiaonan Liu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weijuan Hu
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhang Zhang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shuhui Song
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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Ghimire A, Kim SH, Cho A, Jang N, Ahn S, Islam MS, Mansoor S, Chung YS, Kim Y. Automatic Evaluation of Soybean Seed Traits Using RGB Image Data and a Python Algorithm. PLANTS (BASEL, SWITZERLAND) 2023; 12:3078. [PMID: 37687325 PMCID: PMC10490075 DOI: 10.3390/plants12173078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 08/11/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023]
Abstract
Soybean (Glycine max) is a crucial legume crop known for its nutritional value, as its seeds provide large amounts of plant protein and oil. To ensure maximum productivity in soybean farming, it is essential to carefully choose high-quality seeds that possess desirable characteristics, such as the appropriate size, shape, color, and absence of any damage. By studying the relationship between seed shape and other traits, we can effectively identify different genotypes and improve breeding strategies to develop high-yielding soybean seeds. This study focused on the analysis of seed traits using a Python algorithm. The seed length, width, projected area, and aspect ratio were measured, and the total number of seeds was calculated. The OpenCV library along with the contour detection function were used to measure the seed traits. The seed traits obtained through the algorithm were compared with the values obtained manually and from two software applications (SmartGrain and WinDIAS). The algorithm-derived measurements for the seed length, width, and projected area showed a strong correlation with the measurements obtained using various methods, with R-square values greater than 0.95 (p < 0.0001). Similarly, the error metrics, including the residual standard error, root mean square error, and mean absolute error, were all below 0.5% when comparing the seed length, width, and aspect ratio across different measurement methods. For the projected area, the error was less than 4% when compared with different measurement methods. Furthermore, the algorithm used to count the number of seeds present in the acquired images was highly accurate, and only a few errors were observed. This was a preliminary study that investigated only some morphological traits, and further research is needed to explore more seed attributes.
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Affiliation(s)
- Amit Ghimire
- Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea; (A.G.); (M.S.I.)
| | - Seong-Hoon Kim
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA, Jeonju 5487, Republic of Korea;
| | - Areum Cho
- School of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea; (A.C.); (N.J.); (S.A.)
| | - Naeun Jang
- School of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea; (A.C.); (N.J.); (S.A.)
| | - Seonhwa Ahn
- School of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea; (A.C.); (N.J.); (S.A.)
| | - Mohammad Shafiqul Islam
- Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea; (A.G.); (M.S.I.)
| | - Sheikh Mansoor
- Department of Plant Resources and Environment, Jeju National University, Jeju 63243, Republic of Korea;
| | - Yong Suk Chung
- Department of Plant Resources and Environment, Jeju National University, Jeju 63243, Republic of Korea;
| | - Yoonha Kim
- Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea; (A.G.); (M.S.I.)
- Upland Field Machinery Research Center, Kyungpook National University, Daegu 41566, Republic of Korea
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7
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Zhang B, Zhao D. An Ensemble Learning Model for Detecting Soybean Seedling Emergence in UAV Imagery. SENSORS (BASEL, SWITZERLAND) 2023; 23:6662. [PMID: 37571446 PMCID: PMC10422598 DOI: 10.3390/s23156662] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/19/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023]
Abstract
Efficient detection and evaluation of soybean seedling emergence is an important measure for making field management decisions. However, there are many indicators related to emergence, and using multiple models to detect them separately makes data processing too slow to aid timely field management. In this study, we aimed to integrate several deep learning and image processing methods to build a model to evaluate multiple soybean seedling emergence information. An unmanned aerial vehicle (UAV) was used to acquire soybean seedling RGB images at emergence (VE), cotyledon (VC), and first node (V1) stages. The number of soybean seedlings that emerged was obtained by the seedling emergence detection module, and image datasets were constructed using the seedling automatic cutting module. The improved AlexNet was used as the backbone network of the growth stage discrimination module. The above modules were combined to calculate the emergence proportion in each stage and determine soybean seedlings emergence uniformity. The results show that the seedling emergence detection module was able to identify the number of soybean seedlings with an average accuracy of 99.92%, a R2 of 0.9784, a RMSE of 6.07, and a MAE of 5.60. The improved AlexNet was more lightweight, training time was reduced, the average accuracy was 99.07%, and the average loss was 0.0355. The model was validated in the field, and the error between predicted and real emergence proportions was up to 0.0775 and down to 0.0060. It provides an effective ensemble learning model for the detection and evaluation of soybean seedling emergence, which can provide a theoretical basis for making decisions on soybean field management and precision operations and has the potential to evaluate other crops emergence information.
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Affiliation(s)
- Bo Zhang
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
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8
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Krosney AE, Sotoodeh P, Henry CJ, Beck MA, Bidinosti CP. Inside out: transforming images of lab-grown plants for machine learning applications in agriculture. Front Artif Intell 2023; 6:1200977. [PMID: 37483870 PMCID: PMC10358354 DOI: 10.3389/frai.2023.1200977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 06/05/2023] [Indexed: 07/25/2023] Open
Abstract
Introduction Machine learning tasks often require a significant amount of training data for the resultant network to perform suitably for a given problem in any domain. In agriculture, dataset sizes are further limited by phenotypical differences between two plants of the same genotype, often as a result of different growing conditions. Synthetically-augmented datasets have shown promise in improving existing models when real data is not available. Methods In this paper, we employ a contrastive unpaired translation (CUT) generative adversarial network (GAN) and simple image processing techniques to translate indoor plant images to appear as field images. While we train our network to translate an image containing only a single plant, we show that our method is easily extendable to produce multiple-plant field images. Results Furthermore, we use our synthetic multi-plant images to train several YoloV5 nano object detection models to perform the task of plant detection and measure the accuracy of the model on real field data images. Discussion The inclusion of training data generated by the CUT-GAN leads to better plant detection performance compared to a network trained solely on real data.
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Affiliation(s)
- Alexander E. Krosney
- Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada
- Department of Physics, University of Winnipeg, Winnipeg, MB, Canada
| | - Parsa Sotoodeh
- Department of Applied Computer Science, University of Winnipeg, Winnipeg, MB, Canada
| | - Christopher J. Henry
- Department of Applied Computer Science, University of Winnipeg, Winnipeg, MB, Canada
| | - Michael A. Beck
- Department of Applied Computer Science, University of Winnipeg, Winnipeg, MB, Canada
| | - Christopher P. Bidinosti
- Department of Physics, University of Winnipeg, Winnipeg, MB, Canada
- Department of Applied Computer Science, University of Winnipeg, Winnipeg, MB, Canada
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9
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Mimault M, Ptashnyk M, Dupuy LX. Particle-based model shows complex rearrangement of tissue mechanical properties are needed for roots to grow in hard soil. PLoS Comput Biol 2023; 19:e1010916. [PMID: 36881572 PMCID: PMC10072375 DOI: 10.1371/journal.pcbi.1010916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 04/04/2023] [Accepted: 02/02/2023] [Indexed: 03/08/2023] Open
Abstract
When exposed to increased mechanical resistance from the soil, plant roots display non-linear growth responses that cannot be solely explained by mechanical principles. Here, we aim to investigate how changes in tissue mechanical properties are biologically regulated in response to soil strength. A particle-based model was developed to solve root-soil mechanical interactions at the cellular scale, and a detailed numerical study explored factors that affect root responses to soil resistance. Results showed how softening of root tissues at the tip may contribute to root responses to soil impedance, a mechanism likely linked to soil cavity expansion. The model also predicted the shortening and decreased anisotropy of the zone where growth occurs, which may improve the mechanical stability of the root against axial forces. The study demonstrates the potential of advanced modeling tools to help identify traits that confer plant resistance to abiotic stress.
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Affiliation(s)
- Matthias Mimault
- Information and Computational Science, The James Hutton Institute, Invergowrie, United Kingdom
- * E-mail: (MM); (MP); (LXD)
| | - Mariya Ptashnyk
- School of Mathematical and Computer Sciences, Maxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh, United Kingdom
- * E-mail: (MM); (MP); (LXD)
| | - Lionel X. Dupuy
- Neiker, Basque Institute for Agricultural Research and Development, Derio, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
- * E-mail: (MM); (MP); (LXD)
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Van Antro M, Prelovsek S, Ivanovic S, Gawehns F, Wagemaker NCAM, Mysara M, Horemans N, Vergeer P, Verhoeven KJF. DNA methylation in clonal duckweed (Lemna minor L.) lineages reflects current and historical environmental exposures. Mol Ecol 2023; 32:428-443. [PMID: 36324253 PMCID: PMC10100429 DOI: 10.1111/mec.16757] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 09/16/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022]
Abstract
Environmentally induced DNA methylation variants may mediate gene expression responses to environmental changes. If such induced variants are transgenerationally stable, there is potential for expression responses to persist over multiple generations. Our current knowledge in plants, however, is almost exclusively based on studies conducted in sexually reproducing species where the majority of DNA methylation changes are subject to resetting in germlines, limiting the potential for transgenerational epigenetics stress memory. Asexual reproduction circumvents germlines, and may therefore be more conducive to long-term inheritance of epigenetic marks. Taking advantage of the rapid clonal reproduction of the common duckweed Lemna minor, we hypothesize that long-term, transgenerational stress memory from exposure to high temperature can be detected in DNA methylation profiles. Using a reduced representation bisulphite sequencing approach (epiGBS), we show that temperature stress induces DNA hypermethylation at many CG and CHG cytosine contexts but not CHH. Additionally, differential methylation in CHG context that was observed was still detected in a subset of cytosines, even after 3-12 generations of culturing in a common environment. This demonstrates a memory effect of stress reflected in the methylome and that persists over multiple clonal generations. Structural annotation revealed that this memory effect in CHG methylation was enriched in transposable elements. The observed epigenetic stress memory is probably caused by stable transgenerational persistence of temperature-induced DNA methylation variants across clonal generations. To the extent that such epigenetic memory has functional consequences for gene expression and phenotypes, this result suggests potential for long-term modulation of stress responses in asexual plants.
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Affiliation(s)
- Morgane Van Antro
- Department of Terrestrial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
| | - Stella Prelovsek
- Department of Terrestrial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
| | - Slavica Ivanovic
- Department of Terrestrial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
| | - Fleur Gawehns
- Department of Terrestrial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
| | | | - Mohamed Mysara
- Biosphere Impact Studies, Belgian Nuclear Research Centre (SCK CEN), Mol, Belgium
| | - Nele Horemans
- Biosphere Impact Studies, Belgian Nuclear Research Centre (SCK CEN), Mol, Belgium
| | - Philippine Vergeer
- Plant Ecology and Physiology, Radboud University, Nijmegen, The Netherlands.,Wageningen University and Research (WUR), Plant Ecology and Nature Conservation Group, Wageningen, The Netherlands
| | - Koen J F Verhoeven
- Department of Terrestrial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
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11
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Leiva F, Zakieh M, Alamrani M, Dhakal R, Henriksson T, Singh PK, Chawade A. Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging. FRONTIERS IN PLANT SCIENCE 2022; 13:1010249. [PMID: 36330238 PMCID: PMC9623152 DOI: 10.3389/fpls.2022.1010249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 09/22/2022] [Indexed: 06/16/2023]
Abstract
Fusarium head blight (FHB) is an economically important disease affecting wheat and thus poses a major threat to wheat production. Several studies have evaluated the effectiveness of image analysis methods to predict FHB using disease-infected grains; however, few have looked at the final application, considering the relationship between cost and benefit, resolution, and accuracy. The conventional screening of FHB resistance of large-scale samples is still dependent on low-throughput visual inspections. This study aims to compare the performance of two cost-benefit seed image analysis methods, the free software "SmartGrain" and the fully automated commercially available instrument "Cgrain Value™" by assessing 16 seed morphological traits of winter wheat to predict FHB. The analysis was carried out on a seed set of FHB which was visually assessed as to the severity. The dataset is composed of 432 winter wheat genotypes that were greenhouse-inoculated. The predictions from each method, in addition to the predictions combined from the results of both methods, were compared with the disease visual scores. The results showed that Cgrain Value™ had a higher prediction accuracy of R 2 = 0.52 compared with SmartGrain for which R 2 = 0.30 for all morphological traits. However, the results combined from both methods showed the greatest prediction performance of R 2 = 0.58. Additionally, a subpart of the morphological traits, namely, width, length, thickness, and color features, showed a higher correlation with the visual scores compared with the other traits. Overall, both methods were related to the visual scores. This study shows that these affordable imaging methods could be effective to predict FHB in seeds and enable us to distinguish minor differences in seed morphology, which could lead to a precise performance selection of disease-free seeds/grains.
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Affiliation(s)
- Fernanda Leiva
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Lomma, Sweden
| | - Mustafa Zakieh
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Lomma, Sweden
| | - Marwan Alamrani
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Lomma, Sweden
| | - Rishap Dhakal
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Lomma, Sweden
| | | | - Pawan Kumar Singh
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Lomma, Sweden
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12
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Carley CN, Chen G, Das KK, Delory BM, Dimitrova A, Ding Y, George AP, Greeley LA, Han Q, Hendriks PW, Hernandez-Soriano MC, Li M, Ng JLP, Mau L, Mesa-Marín J, Miller AJ, Rae AE, Schmidt J, Thies A, Topp CN, Wacker TS, Wang P, Wang X, Xie L, Zheng C. Root biology never sleeps: 11 th Symposium of the International Society of Root Research (ISRR11) and the 9 th International Symposium on Root Development (Rooting2021), 24-28 May 2021. THE NEW PHYTOLOGIST 2022; 235:2149-2154. [PMID: 35979688 DOI: 10.1111/nph.18338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Affiliation(s)
- Clayton N Carley
- Department of Agronomy, Iowa State University, Ames, IA, 50011, USA
| | - Guanying Chen
- Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg C, 1871, Denmark
| | - Krishna K Das
- Division of Biology, Indian Institute of Science Education and Research Tirupati, Tirupati, 517507, India
| | - Benjamin M Delory
- Institute of Ecology, Leuphana University of Lüneburg, Lüneburg, 21335, Germany
| | - Anastazija Dimitrova
- Department of Biosciences and Territory, University of Molise, Pesche, 86090, Italy
| | - Yiyang Ding
- Department of Forest Sciences, University of Helsinki, Helsinki, FI-00014, Finland
| | - Abin P George
- Division of Biology, Indian Institute of Science Education and Research Tirupati, Tirupati, 517507, India
| | - Laura A Greeley
- Department of Biochemistry & Interdisciplinary Plant Group, University of Missouri-Columbia, Columbia, MO, 65201, USA
| | - Qingqing Han
- State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, China
| | - Pieter-Willem Hendriks
- CSIRO, Agriculture and Food, PO Box 1700, Canberra, 2601, ACT, Australia
- School of Agriculture and Wine Sciences, Charles Sturt University, Boorooma Street, 14, Wagga Wagga, NSW, 2650, Australia
- Graham Centre for Agricultural Innovation, Locked bag 588, Wagga Wagga, NSW, 2678, Australia
| | | | - Meng Li
- Department of Plant Science, The Pennsylvania State University, State College, PA, 16801, USA
| | - Jason Liang Pin Ng
- Research School of Biology, Australian National University, Canberra, 2601, ACT, Australia
| | - Lisa Mau
- Institute of Bio- and Geosciences - Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, 52425, Germany
- Faculty of Agriculture, University of Bonn, Bonn, 53115, Germany
- School of BioSciences, The University of Melbourne, Melbourne, 3010, VIC, Australia
| | - Jennifer Mesa-Marín
- Department of Plant Biology and Ecology, Universidad de Sevilla, Seville, 41012, Spain
| | - Allison J Miller
- Department of Biology, Saint Louis University, St Louis, MO, 63103, USA
- Donald Danforth Plant Science Center, St Louis, MO, 63132, USA
| | - Angus E Rae
- Research School of Biology, Australian National University, Canberra, 2601, ACT, Australia
| | | | - August Thies
- Donald Danforth Plant Science Center, St Louis, MO, 63132, USA
- Division of Plant Sciences, University of Missouri-Columbia, Columbia, MO, 65201, USA
| | | | - Tomke S Wacker
- Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg C, 1871, Denmark
| | - Pinhui Wang
- Research School of Biology, Australian National University, Canberra, 2601, ACT, Australia
| | - Xinyu Wang
- Institute of Grassland Science, Northeast Normal University, Key Laboratory of Vegetation Ecology, Ministry of Education, Jilin Songnen Grassland Ecosystem National Observation and Research Station, Changchun, 130024, China
| | - Limeng Xie
- Department of Plant Biology, University of Georgia, Athens, GA, 30605, USA
| | - Congcong Zheng
- Institute of Bio- and Geosciences - Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, 52425, Germany
- Faculty of Agriculture, University of Bonn, Bonn, 53115, Germany
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13
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Bello-Bello E, López-Arredondo D, Rico-Chambrón TY, Herrera-Estrella L. Conquering compacted soils: uncovering the molecular components of root soil penetration. TRENDS IN PLANT SCIENCE 2022; 27:814-827. [PMID: 35525799 DOI: 10.1016/j.tplants.2022.04.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 03/28/2022] [Accepted: 04/04/2022] [Indexed: 06/14/2023]
Abstract
Global agriculture and food security face paramount challenges due to climate change and land degradation. Human-induced soil compaction severely affects soil fertility, impairing root system development and crop yield. There is a need to design compaction-resilient crops that can thrive in degraded soils and maintain high yields. To address plausible solutions to this challenging scenario, we discuss current knowledge on plant root penetration ability and delineate potential approaches based on root-targeted genetic engineering (RGE) and genomics-assisted breeding (GAB) for developing crops with enhanced root system penetrability (RSP) into compacted soils. Such approaches could lead to crops with improved resilience to climate change and marginal soils, which can help to boost CO2 sequestration and storage in deeper soil strata.
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Affiliation(s)
- Elohim Bello-Bello
- Unidad de Genómica Avanzada/LANGEBIO, Centro de Investigación y de Estudios Avanzados, Irapuato, México
| | - Damar López-Arredondo
- Institute of Genomics for Crop Abiotic Stress Tolerance, Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 79409, USA
| | - Thelma Y Rico-Chambrón
- Unidad de Genómica Avanzada/LANGEBIO, Centro de Investigación y de Estudios Avanzados, Irapuato, México
| | - Luis Herrera-Estrella
- Unidad de Genómica Avanzada/LANGEBIO, Centro de Investigación y de Estudios Avanzados, Irapuato, México; Institute of Genomics for Crop Abiotic Stress Tolerance, Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 79409, USA.
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14
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A Machine-Learning Method to Assess Growth Patterns in Plants of the Family Lemnaceae. PLANTS 2022; 11:plants11151910. [PMID: 35893614 PMCID: PMC9332063 DOI: 10.3390/plants11151910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/19/2022] [Accepted: 07/20/2022] [Indexed: 11/17/2022]
Abstract
Numerous new technologies have been implemented in image analysis methods that help researchers draw scientific conclusions from biological phenomena. Plants of the family Lemnaceae (duckweeds) are the smallest flowering plants in the world, and biometric measurements of single plants and their growth rate are highly challenging. Although the use of software for digital image analysis has changed the way scientists extract phenomenological data (also for studies on duckweeds), the procedure is often not wholly automated and sometimes relies on the intervention of a human operator. Such a constraint can limit the objectivity of the measurements and generally slows down the time required to produce scientific data. Herein lies the need to implement image analysis software with artificial intelligence that can substitute the human operator. In this paper, we present a new method to study the growth rates of the plants of the Lemnaceae family based on the application of machine-learning procedures to digital image analysis. The method is compared to existing analogical and computer-operated procedures. The results showed that our method drastically reduces the time consumption of the human operator while retaining a high correlation in the growth rates measured with other procedures. As expected, machine-learning methods applied to digital image analysis can overcome the constraints of measuring growth rates of very small plants and might help duckweeds gain worldwide attention thanks to their strong nutritional qualities and biological plasticity.
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15
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Yoshida S, Weijers D. Quantitative analysis of 3D cellular geometry and modeling of the Arabidopsis embryo. J Microsc 2022; 287:107-113. [PMID: 35759505 DOI: 10.1111/jmi.13130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/17/2022] [Accepted: 06/22/2022] [Indexed: 11/30/2022]
Abstract
As many multicellular organisms, land plants start their life as a single cell, which forms an embryo. Embryo morphology is relatively simple, yet comprises basic tissues and organs, as well as stem cells that sustain post-embryonic development. Being condensed in both time and space, early plant embryogenesis offers an excellent window to study general principles of plant development. However, it has been technically challenging to obtain high spatial microscopic resolution, or to perform live imaging, that would enable an in-depth investigation. Recent advances in sample preparation and microscopy now allow studying the detailed cellular morphology of plant embryos in 3D. When coupled to quantitative image analysis and computational modeling, this allows resolving the temporal and spatial interactions between cellular patterning and genetic networks. In this review, we discuss examples of interdisciplinary studies that showcase the potential of the early plant embryo for revealing principles underlying plant development. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Saiko Yoshida
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, Carl-von-Linné-Weg 10, Köln, D-50829, Germany
| | - Dolf Weijers
- Laboratory of Biochemistry, Wageningen University
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16
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Elliott K, Berry JC, Kim H, Bart RS. A comparison of ImageJ and machine learning based image analysis methods to measure cassava bacterial blight disease severity. PLANT METHODS 2022; 18:86. [PMID: 35729628 PMCID: PMC9210806 DOI: 10.1186/s13007-022-00906-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 05/16/2022] [Indexed: 05/13/2023]
Abstract
BACKGROUND Methods to accurately quantify disease severity are fundamental to plant pathogen interaction studies. Commonly used methods include visual scoring of disease symptoms, tracking pathogen growth in planta over time, and various assays that detect plant defense responses. Several image-based methods for phenotyping of plant disease symptoms have also been developed. Each of these methods has different advantages and limitations which should be carefully considered when choosing an approach and interpreting the results. RESULTS In this paper, we developed two image analysis methods and tested their ability to quantify different aspects of disease lesions in the cassava-Xanthomonas pathosystem. The first method uses ImageJ, an open-source platform widely used in the biological sciences. The second method is a few-shot support vector machine learning tool that uses a classifier file trained with five representative infected leaf images for lesion recognition. Cassava leaves were syringe infiltrated with wildtype Xanthomonas, a Xanthomonas mutant with decreased virulence, and mock treatments. Digital images of infected leaves were captured overtime using a Raspberry Pi camera. The image analysis methods were analyzed and compared for the ability to segment the lesion from the background and accurately capture and measure differences between the treatment types. CONCLUSIONS Both image analysis methods presented in this paper allow for accurate segmentation of disease lesions from the non-infected plant. Specifically, at 4-, 6-, and 9-days post inoculation (DPI), both methods provided quantitative differences in disease symptoms between different treatment types. Thus, either method could be applied to extract information about disease severity. Strengths and weaknesses of each approach are discussed.
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Affiliation(s)
- Kiona Elliott
- Donald Danforth Plant Science Center, Saint Louis, MO, 63132, USA
- Division of Biological and Biomedical Sciences, Washington University in Saint Louis, St. Louis, MO, 63110, USA
| | - Jeffrey C Berry
- Donald Danforth Plant Science Center, Saint Louis, MO, 63132, USA
| | - Hobin Kim
- Army and Navy Academy, Carlsbad, CA, 92008, USA
| | - Rebecca S Bart
- Donald Danforth Plant Science Center, Saint Louis, MO, 63132, USA.
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17
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Danilevicz MF, Gill M, Anderson R, Batley J, Bennamoun M, Bayer PE, Edwards D. Plant Genotype to Phenotype Prediction Using Machine Learning. Front Genet 2022; 13:822173. [PMID: 35664329 PMCID: PMC9159391 DOI: 10.3389/fgene.2022.822173] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 03/07/2022] [Indexed: 12/13/2022] Open
Abstract
Genomic prediction tools support crop breeding based on statistical methods, such as the genomic best linear unbiased prediction (GBLUP). However, these tools are not designed to capture non-linear relationships within multi-dimensional datasets, or deal with high dimension datasets such as imagery collected by unmanned aerial vehicles. Machine learning (ML) algorithms have the potential to surpass the prediction accuracy of current tools used for genotype to phenotype prediction, due to their capacity to autonomously extract data features and represent their relationships at multiple levels of abstraction. This review addresses the challenges of applying statistical and machine learning methods for predicting phenotypic traits based on genetic markers, environment data, and imagery for crop breeding. We present the advantages and disadvantages of explainable model structures, discuss the potential of machine learning models for genotype to phenotype prediction in crop breeding, and the challenges, including the scarcity of high-quality datasets, inconsistent metadata annotation and the requirements of ML models.
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Affiliation(s)
- Monica F. Danilevicz
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Mitchell Gill
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Robyn Anderson
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Jacqueline Batley
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Mohammed Bennamoun
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Philipp E. Bayer
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
- *Correspondence: David Edwards,
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18
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Sarić R, Nguyen VD, Burge T, Berkowitz O, Trtílek M, Whelan J, Lewsey MG, Čustović E. Applications of hyperspectral imaging in plant phenotyping. TRENDS IN PLANT SCIENCE 2022; 27:301-315. [PMID: 34998690 DOI: 10.1016/j.tplants.2021.12.003] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 11/30/2021] [Accepted: 12/06/2021] [Indexed: 06/14/2023]
Abstract
Our ability to interrogate and manipulate the genome far exceeds our capacity to measure the effects of genetic changes on plant traits. Much effort has been made recently by the plant science research community to address this imbalance. The responses of plants to environmental conditions can now be defined using a variety of imaging approaches. Hyperspectral imaging (HSI) has emerged as a promising approach to measure traits using a wide range of wavebands simultaneously in 3D to capture information in lab, glasshouse, or field settings. HSI has been applied to define abiotic, biotic, and quality traits for optimisation of crop management.
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Affiliation(s)
- Rijad Sarić
- Department of Animal, Plant and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Department of Engineering, School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, Australia
| | - Viet D Nguyen
- Department of Animal, Plant and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Department of Engineering, School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, Australia
| | - Timothy Burge
- Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Oliver Berkowitz
- Department of Animal, Plant and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Martin Trtílek
- Photon Systems Instruments plant phenotyping research centre, Photon System Instruments, 664 24 Drasov, Brno, Czech Republic
| | - James Whelan
- Department of Animal, Plant and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia.
| | - Mathew G Lewsey
- Department of Animal, Plant and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Edhem Čustović
- Department of Engineering, School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, Australia
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19
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Zhu Y, Sun G, Ding G, Zhou J, Wen M, Jin S, Zhao Q, Colmer J, Ding Y, Ober ES, Zhou J. Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat. PLANT PHYSIOLOGY 2021; 187:716-738. [PMID: 34608970 PMCID: PMC8491082 DOI: 10.1093/plphys/kiab324] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 06/22/2021] [Indexed: 05/12/2023]
Abstract
Plant phenomics bridges the gap between traits of agricultural importance and genomic information. Limitations of current field-based phenotyping solutions include mobility, affordability, throughput, accuracy, scalability, and the ability to analyze big data collected. Here, we present a large-scale phenotyping solution that combines a commercial backpack Light Detection and Ranging (LiDAR) device and our analytic software, CropQuant-3D, which have been applied jointly to phenotype wheat (Triticum aestivum) and associated 3D trait analysis. The use of LiDAR can acquire millions of 3D points to represent spatial features of crops, and CropQuant-3D can extract meaningful traits from large, complex point clouds. In a case study examining the response of wheat varieties to three different levels of nitrogen fertilization in field experiments, the combined solution differentiated significant genotype and treatment effects on crop growth and structural variation in the canopy, with strong correlations with manual measurements. Hence, we demonstrate that this system could consistently perform 3D trait analysis at a larger scale and more quickly than heretofore possible and addresses challenges in mobility, throughput, and scalability. To ensure our work could reach non-expert users, we developed an open-source graphical user interface for CropQuant-3D. We, therefore, believe that the combined system is easy-to-use and could be used as a reliable research tool in multi-location phenotyping for both crop research and breeding. Furthermore, together with the fast maturity of LiDAR technologies, the system has the potential for further development in accuracy and affordability, contributing to the resolution of the phenotyping bottleneck and exploiting available genomic resources more effectively.
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Affiliation(s)
- Yulei Zhu
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Gang Sun
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Guohui Ding
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Jie Zhou
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Mingxing Wen
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
- Zhenjiang Institute of Agricultural Science in Hill Area of Jiangsu Province, Jurong 212400, China
| | - Shichao Jin
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Qiang Zhao
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200233, China
| | - Joshua Colmer
- Earlham Institute, Norwich Research Park, Norwich NR4 7UH, UK
| | - Yanfeng Ding
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Eric S. Ober
- Cambridge Crop Research, National Institute of Agricultural Botany (NIAB), Cambridge CB3 0LE, UK
| | - Ji Zhou
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
- Cambridge Crop Research, National Institute of Agricultural Botany (NIAB), Cambridge CB3 0LE, UK
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20
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Danilevicz MF, Bayer PE, Nestor BJ, Bennamoun M, Edwards D. Resources for image-based high-throughput phenotyping in crops and data sharing challenges. PLANT PHYSIOLOGY 2021; 187:699-715. [PMID: 34608963 PMCID: PMC8561249 DOI: 10.1093/plphys/kiab301] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 05/26/2021] [Indexed: 05/06/2023]
Abstract
High-throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors, such as red green and blue (RGB) cameras, hyperspectral sensors, and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterize crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalizing between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation, and quantitative trait measurement. We emphasize the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.
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Affiliation(s)
- Monica F. Danilevicz
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, Western Australia 6009, Australia
| | - Philipp E. Bayer
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, Western Australia 6009, Australia
| | - Benjamin J. Nestor
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, Western Australia 6009, Australia
| | - Mohammed Bennamoun
- Department of Computer Science and Software Engineering, University of Western Australia, Perth, Western Australia 6009, Australia
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, Western Australia 6009, Australia
- Author for communication:
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21
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Fernández-Campos M, Huang YT, Jahanshahi MR, Wang T, Jin J, Telenko DEP, Góngora-Canul C, Cruz CD. Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks. FRONTIERS IN PLANT SCIENCE 2021; 12:673505. [PMID: 34220894 PMCID: PMC8248543 DOI: 10.3389/fpls.2021.673505] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 05/10/2021] [Indexed: 05/21/2023]
Abstract
Wheat blast is a threat to global wheat production, and limited blast-resistant cultivars are available. The current estimations of wheat spike blast severity rely on human assessments, but this technique could have limitations. Reliable visual disease estimations paired with Red Green Blue (RGB) images of wheat spike blast can be used to train deep convolutional neural networks (CNN) for disease severity (DS) classification. Inter-rater agreement analysis was used to measure the reliability of who collected and classified data obtained under controlled conditions. We then trained CNN models to classify wheat spike blast severity. Inter-rater agreement analysis showed high accuracy and low bias before model training. Results showed that the CNN models trained provide a promising approach to classify images in the three wheat blast severity categories. However, the models trained on non-matured and matured spikes images showing the highest precision, recall, and F1 score when classifying the images. The high classification accuracy could serve as a basis to facilitate wheat spike blast phenotyping in the future.
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Affiliation(s)
| | - Yu-Ting Huang
- Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, United States
| | - Mohammad R. Jahanshahi
- Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, United States
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States
| | - Tao Wang
- Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, United States
| | - Jian Jin
- Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, United States
| | - Darcy E. P. Telenko
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
| | - Carlos Góngora-Canul
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
- Tecnológico Nacional de México/IT Conkal, Conkal, Yucatán, Mexico
| | - C. D. Cruz
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
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22
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Beck MA, Liu CY, Bidinosti CP, Henry CJ, Godee CM, Ajmani M. An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture. PLoS One 2020; 15:e0243923. [PMID: 33332382 PMCID: PMC7745972 DOI: 10.1371/journal.pone.0243923] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 12/01/2020] [Indexed: 11/18/2022] Open
Abstract
A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models designed to perform tasks such as autonomous plant classification will typically be coupled to just one or perhaps a few plant species. As a consequence, each crop-specific task is very likely to require its own specialized training data, and the question of how to serve this need for data now often overshadows the more routine exercise of actually training such models. To tackle this problem, we have developed an embedded robotic system to automatically generate and label large datasets of plant images for ML applications in agriculture. The system can image plants from virtually any angle, thereby ensuring a wide variety of data; and with an imaging rate of up to one image per second, it can produce lableled datasets on the scale of thousands to tens of thousands of images per day. As such, this system offers an important alternative to time- and cost-intensive methods of manual generation and labeling. Furthermore, the use of a uniform background made of blue keying fabric enables additional image processing techniques such as background replacement and image segementation. It also helps in the training process, essentially forcing the model to focus on the plant features and eliminating random correlations. To demonstrate the capabilities of our system, we generated a dataset of over 34,000 labeled images, with which we trained an ML-model to distinguish grasses from non-grasses in test data from a variety of sources. We now plan to generate much larger datasets of Canadian crop plants and weeds that will be made publicly available in the hope of further enabling ML applications in the agriculture sector.
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Affiliation(s)
- Michael A. Beck
- Department of Physics, University of Winnipeg, Winnipeg, Manitoba, Canada
- Department of Applied Computer Science, University of Winnipeg, Winnipeg, Manitoba, Canada
| | - Chen-Yi Liu
- Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Christopher P. Bidinosti
- Department of Physics, University of Winnipeg, Winnipeg, Manitoba, Canada
- Department of Applied Computer Science, University of Winnipeg, Winnipeg, Manitoba, Canada
- Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Christopher J. Henry
- Department of Applied Computer Science, University of Winnipeg, Winnipeg, Manitoba, Canada
| | - Cara M. Godee
- Department of Biology, University of Winnipeg, Winnipeg, Manitoba, Canada
| | - Manisha Ajmani
- Department of Physics, University of Winnipeg, Winnipeg, Manitoba, Canada
- Department of Applied Computer Science, University of Winnipeg, Winnipeg, Manitoba, Canada
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Gaillard M, Miao C, Schnable JC, Benes B. Voxel carving-based 3D reconstruction of sorghum identifies genetic determinants of light interception efficiency. PLANT DIRECT 2020; 4:e00255. [PMID: 33073164 PMCID: PMC7541904 DOI: 10.1002/pld3.255] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 07/09/2020] [Indexed: 05/02/2023]
Abstract
Changes in canopy architecture traits have been shown to contribute to yield increases. Optimizing both light interception and light interception efficiency of agricultural crop canopies will be essential to meeting the growing food needs. Canopy architecture is inherently three-dimensional (3D), but many approaches to measuring canopy architecture component traits treat the canopy as a two-dimensional (2D) structure to make large scale measurement, selective breeding, and gene identification logistically feasible. We develop a high throughput voxel carving strategy to reconstruct 3D representations of sorghum from a small number of RGB photos. Our approach builds on the voxel carving algorithm to allow for fully automatic reconstruction of hundreds of plants. It was employed to generate 3D reconstructions of individual plants within a sorghum association population at the late vegetative stage of development. Light interception parameters estimated from these reconstructions enabled the identification of known and previously unreported loci controlling light interception efficiency in sorghum. The approach is generalizable and scalable, and it enables 3D reconstructions from existing plant high throughput phenotyping datasets. We also propose a set of best practices to increase 3D reconstructions' accuracy.
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Affiliation(s)
- Mathieu Gaillard
- Department of Computer Graphics TechnologyPurdue UniversityWest LafayetteINUSA
| | - Chenyong Miao
- Center for Plant Science Innovation and Department of Agronomy and HorticultureUniversity of Nebraska‐LincolnLincolnNEUSA
| | - James C. Schnable
- Center for Plant Science Innovation and Department of Agronomy and HorticultureUniversity of Nebraska‐LincolnLincolnNEUSA
| | - Bedrich Benes
- Department of Computer Graphics TechnologyPurdue UniversityWest LafayetteINUSA
- Department of Computer SciencePurdue UniversityWest LafayetteINUSA
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Abstract
Wheat was one of the first grain crops domesticated by humans and remains among the major contributors to the global calorie and protein budget. The rapidly expanding world population demands further enhancement of yield and performance of wheat. Phenotypic information has historically been instrumental in wheat breeding for improved traits. In the last two decades, a steadily growing collection of tools and imaging software have given us the ability to quantify shoot, root, and seed traits with progressively increasing accuracy and throughput. This review discusses challenges and advancements in image analysis platforms for wheat phenotyping at the organ level. Perspectives on how these collective phenotypes can inform basic research on understanding wheat physiology and breeding for wheat improvement are also provided.
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Olas JJ, Fichtner F, Apelt F. All roads lead to growth: imaging-based and biochemical methods to measure plant growth. JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:11-21. [PMID: 31613967 PMCID: PMC6913701 DOI: 10.1093/jxb/erz406] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 08/28/2019] [Indexed: 05/31/2023]
Abstract
Plant growth is a highly complex biological process that involves innumerable interconnected biochemical and signalling pathways. Many different techniques have been developed to measure growth, unravel the various processes that contribute to plant growth, and understand how a complex interaction between genotype and environment determines the growth phenotype. Despite this complexity, the term 'growth' is often simplified by researchers; depending on the method used for quantification, growth is viewed as an increase in plant or organ size, a change in cell architecture, or an increase in structural biomass. In this review, we summarise the cellular and molecular mechanisms underlying plant growth, highlight state-of-the-art imaging and non-imaging-based techniques to quantitatively measure growth, including a discussion of their advantages and drawbacks, and suggest a terminology for growth rates depending on the type of technique used.
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Affiliation(s)
- Justyna Jadwiga Olas
- University of Potsdam, Institute of Biochemistry and Biology, Karl-Liebknecht-Straße, Haus, Potsdam, Germany
| | - Franziska Fichtner
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg, Potsdam, Germany
| | - Federico Apelt
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg, Potsdam, Germany
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Betegón‐Putze I, González A, Sevillano X, Blasco‐Escámez D, Caño‐Delgado AI. MyROOT: a method and software for the semiautomatic measurement of primary root length in Arabidopsis seedlings. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 98:1145-1156. [PMID: 30809923 PMCID: PMC6618301 DOI: 10.1111/tpj.14297] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 02/18/2019] [Accepted: 02/19/2019] [Indexed: 05/05/2023]
Abstract
Root analysis is essential for both academic and agricultural research. Despite the great advances in root phenotyping and imaging, calculating root length is still performed manually and involves considerable amounts of labor and time. To overcome these limitations, we developed MyROOT, a software for the semiautomatic quantification of root growth of seedlings growing directly on agar plates. Our method automatically determines the scale from the image of the plate, and subsequently measures the root length of the individual plants. To this aim, MyROOT combines a bottom-up root tracking approach with a hypocotyl detection algorithm. At the same time as providing accurate root measurements, MyROOT also significantly minimizes the user intervention required during the process. Using Arabidopsis, we tested MyROOT with seedlings from different growth stages and experimental conditions. When comparing the data obtained from this software with that of manual root measurements, we found a high correlation between both methods (R2 = 0.997). When compared with previous developed software with similar features (BRAT and EZ-Rhizo), MyROOT offered an improved accuracy for root length measurements. Therefore, MyROOT will be of great use to the plant science community by permitting high-throughput root length measurements while saving both labor and time.
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Affiliation(s)
- Isabel Betegón‐Putze
- Department of Molecular GeneticsCenter for Research in Agricultural Genomics (CRAG)CSIC‐IRTA‐UAB‐UBCampus UAB, Bellaterra (Cerdanyola del Vallès)08193BarcelonaSpain
| | - Alejandro González
- GTM‐ Grup de Recerca en Tecnologies Mèdia, La SalleUniversitat Ramon Llull08022BarcelonaSpain
| | - Xavier Sevillano
- GTM‐ Grup de Recerca en Tecnologies Mèdia, La SalleUniversitat Ramon Llull08022BarcelonaSpain
| | - David Blasco‐Escámez
- Department of Molecular GeneticsCenter for Research in Agricultural Genomics (CRAG)CSIC‐IRTA‐UAB‐UBCampus UAB, Bellaterra (Cerdanyola del Vallès)08193BarcelonaSpain
| | - Ana I. Caño‐Delgado
- Department of Molecular GeneticsCenter for Research in Agricultural Genomics (CRAG)CSIC‐IRTA‐UAB‐UBCampus UAB, Bellaterra (Cerdanyola del Vallès)08193BarcelonaSpain
- Present address:
Department of Molecular GeneticsCentre de Recerca en Agrigenòmica (CRAG) CSIC‐IRTA‐UAB‐UBCampus UAB, Bellaterra (Cerdanyola del Vallès)E‐08193BarcelonaSpain
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Mir RR, Reynolds M, Pinto F, Khan MA, Bhat MA. High-throughput phenotyping for crop improvement in the genomics era. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2019; 282:60-72. [PMID: 31003612 DOI: 10.1016/j.plantsci.2019.01.007] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Revised: 12/10/2018] [Accepted: 01/09/2019] [Indexed: 05/24/2023]
Abstract
Tremendous progress has been made with continually expanding genomics technologies to unravel and understand crop genomes. However, the impact of genomics data on crop improvement is still far from satisfactory, in large part due to a lack of effective phenotypic data; our capacity to collect useful high quality phenotypic data lags behind the current capacity to generate high-throughput genomics data. Thus, the research bottleneck in plant sciences is shifting from genotyping to phenotyping. This article review the current status of efforts made in the last decade to systematically collect phenotypic data to alleviate this 'phenomics bottlenecks' by recording trait data through sophisticated non-invasive imaging, spectroscopy, image analysis, robotics, high-performance computing facilities and phenomics databases. These modern phenomics platforms and tools aim to record data on traits like plant development, architecture, plant photosynthesis, growth or biomass productivity, on hundreds to thousands of plants in a single day, as a phenomics revolution. It is believed that this revolution will provide plant scientists with the knowledge and tools necessary for unlocking information coded in plant genomes. Efforts have been also made to present the advances made in the last 10 years in phenomics platforms and their use in generating phenotypic data on different traits in several major crops including rice, wheat, barley, and maize. The article also highlights the need for phenomics databases and phenotypic data sharing for crop improvement. The phenomics data generated has been used to identify genes/QTL through QTL mapping, association mapping and genome-wide association studies (GWAS) for genomics-assisted breeding (GAB) for crop improvement.
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Affiliation(s)
- Reyazul Rouf Mir
- Division of Genetics & Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Wadura Campus, Sopore-193201, Kashmir, India.
| | - Mathew Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT), Mexico, D.F., Mexico
| | - Francisco Pinto
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT), Mexico, D.F., Mexico
| | - Mohd Anwar Khan
- Division of Genetics & Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Wadura Campus, Sopore-193201, Kashmir, India
| | - Mohd Ashraf Bhat
- Division of Genetics & Plant Breeding, Faculty of Agriculture (FoA), Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Wadura Campus, Sopore-193201, Kashmir, India
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Passot S, Couvreur V, Meunier F, Draye X, Javaux M, Leitner D, Pagès L, Schnepf A, Vanderborght J, Lobet G. Connecting the dots between computational tools to analyse soil-root water relations. JOURNAL OF EXPERIMENTAL BOTANY 2019; 70:2345-2357. [PMID: 30329081 DOI: 10.1093/jxb/ery361] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Accepted: 10/10/2018] [Indexed: 05/20/2023]
Abstract
In recent years, many computational tools, such as image analysis, data management, process-based simulation, and upscaling tools, have been developed to help quantify and understand water flow in the soil-root system, at multiple scales (tissue, organ, plant, and population). Several of these tools work together or at least are compatible. However, for the uninformed researcher, they might seem disconnected, forming an unclear and disorganized succession of tools. In this article, we show how different studies can be further developed by connecting them to analyse soil-root water relations in a comprehensive and structured network. This 'explicit network of soil-root computational tools' informs readers about existing tools and helps them understand how their data (past and future) might fit within the network. We also demonstrate the novel possibilities of scale-consistent parameterizations made possible by the network with a set of case studies from the literature. Finally, we discuss existing gaps in the network and how we can move forward to fill them.
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Affiliation(s)
- Sixtine Passot
- Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Valentin Couvreur
- Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Félicien Meunier
- Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
- Computational and Applied Vegetation Ecology lab, Ghent University, Gent, Belgium
- Department of Earth and Environment, Boston University, Boston, MA, USA
| | - Xavier Draye
- Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Mathieu Javaux
- Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
- Agrosphere, IBG3, Forschungszentrum Jülich, GmbH Jülich, Germany
| | | | | | - Andrea Schnepf
- Agrosphere, IBG3, Forschungszentrum Jülich, GmbH Jülich, Germany
| | - Jan Vanderborght
- Agrosphere, IBG3, Forschungszentrum Jülich, GmbH Jülich, Germany
| | - Guillaume Lobet
- Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
- Agrosphere, IBG3, Forschungszentrum Jülich, GmbH Jülich, Germany
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Marzougui A, Ma Y, Zhang C, McGee RJ, Coyne CJ, Main D, Sankaran S. Advanced Imaging for Quantitative Evaluation of Aphanomyces Root Rot Resistance in Lentil. FRONTIERS IN PLANT SCIENCE 2019; 10:383. [PMID: 31057562 PMCID: PMC6477098 DOI: 10.3389/fpls.2019.00383] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 03/13/2019] [Indexed: 05/08/2023]
Abstract
Aphanomyces root rot (ARR) is a soil-borne disease that results in severe yield losses in lentil. The development of resistant cultivars is one of the key strategies to control this pathogen. However, the evaluation of disease severity is limited to visual scores that can be subjective. This study utilized image-based phenotyping approaches to evaluate Aphanomyces euteiches resistance in lentil genotypes in greenhouse (351 genotypes from lentil single plant/LSP derived collection and 191 genotypes from recombinant inbred lines/RIL using digital Red-Green-Blue/RGB and hyperspectral imaging) and field (173 RIL genotypes using unmanned aerial system-based multispectral imaging) conditions. Moderate to strong correlations were observed between RGB, multispectral, and hyperspectral derived features extracted from lentil shoots/roots and visual scores. In general, root features extracted from RGB imaging were found to be strongly associated with disease severity. With only three root traits, elastic net regression model was able to predict disease severity across and within multiple datasets (R 2 = 0.45-0.73 and RMSE = 0.66-1.00). The selected features could represent visual disease scores. Moreover, we developed twelve normalized difference spectral indices (NDSIs) that were significantly correlated with disease scores: two NDSIs for lentil shoot section - computed from wavelengths of 1170, 1160, 1270, and 1280 nm (0.12 ≤ |r| ≤ 0.24, P < 0.05) and ten NDSIs for lentil root sections - computed from wavelengths in the range of 630-670, 700-840, and 1320-1530 nm (0.10 ≤ |r| ≤ 0.50, P < 0.05). Root-derived NDSIs were more accurate in predicting disease scores with an R 2 of 0.54 (RMSE = 0.86), especially when the model was trained and tested on LSP accessions, compared to R 2 of 0.25 (RMSE = 1.64) when LSP and RIL genotypes were used as train and test datasets, respectively. Importantly, NDSIs - computed from wavelengths of 700, 710, 730, and 790 nm - had strong positive correlations with disease scores (0.35 ≤r ≤ 0.50, P < 0.0001), which was confirmed in field phenotyping with similar correlations using vegetation index with red edge wavelength (normalized difference red edge, 0.36 ≤ |r| ≤ 0.57, P < 0.0001). The adopted image-based phenotyping approaches can help plant breeders to objectively quantify ARR resistance and reduce the subjectivity in selecting potential genotypes.
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Affiliation(s)
- Afef Marzougui
- Department of Biological Systems Engineering, Washington State University, Pullman, WA, United States
| | - Yu Ma
- Department of Horticulture, Washington State University, Pullman, WA, United States
| | - Chongyuan Zhang
- Department of Biological Systems Engineering, Washington State University, Pullman, WA, United States
| | - Rebecca J. McGee
- United States Department of Agriculture-Agricultural Research Service, Grain Legume Genetics and Physiology Research Unit, Washington State University, Pullman, WA, United States
| | - Clarice J. Coyne
- United States Department of Agriculture-Agricultural Research Service, Plant Germplasm Introduction and Testing Unit, Washington State University, Pullman, WA, United States
| | - Dorrie Main
- Department of Horticulture, Washington State University, Pullman, WA, United States
| | - Sindhuja Sankaran
- Department of Biological Systems Engineering, Washington State University, Pullman, WA, United States
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30
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Cunha BRD, Andreasen C, Rasmussen J, Nielsen J, Ritz C, Streibig JC. Assessing herbicide symptoms by using a logarithmic field sprayer. PEST MANAGEMENT SCIENCE 2019; 75:1166-1171. [PMID: 30379393 DOI: 10.1002/ps.5257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 10/26/2018] [Accepted: 10/26/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND In field experiments, assessment of herbicide selectivity and efficacy rarely takes advantage of dose-response regressions. The objective is to demonstrate that logarithmic sprayers, which automatically make a logarithmic dilution of a herbicide rate, can extract biologically relevant parameters describing the efficacy of herbicides in crops, and compare localities and time of assessment. RESULTS In a conventional and an organic field, canola, white mustard, and no crop plots were sprayed with diflufenican and beflubutamid. A mixed effect log-logistic dose-response regression, with autoregressive correlation structure, estimated ED50 and ED90 for visual and Excess Green Index symptoms at various days after treatment (DAT). For visual assessment, ED50 differed within no crop between locations for beflubutamid at 12 DAT and 26 DAT. For diflufenican, the ED50 was different within crops at the two fields at 12 DAT, but not at 26 DAT. The Excess Green Indices at ED50 were not different among herbicides, locations, and corps; ED90 differed for white mustard and canola for beflubutamid but not for diflufenican. CONCLUSION Suitable nonlinear regression models are now available for fitting dose-response data from a logarithmic sprayer in field experiments. The derived parameters (e.g. ED50 ) can compare selectivity and efficacy at numerous cropping systems. © 2018 Society of Chemical Industry.
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Affiliation(s)
- Beatriz Ribeiro da Cunha
- Department of Crop Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Brazil
| | - Christian Andreasen
- Department of Plant and Environmental Sciences, University of Copenhagen, Taastrup, Denmark
| | - Jesper Rasmussen
- Department of Plant and Environmental Sciences, University of Copenhagen, Taastrup, Denmark
| | - Jon Nielsen
- Department of Plant and Environmental Sciences, University of Copenhagen, Taastrup, Denmark
| | - Christian Ritz
- Department of Nutrition, Exercise and Sports, University of Copenhagen, Frederiksberg C, Denmark
| | - Jens Carl Streibig
- Department of Plant and Environmental Sciences, University of Copenhagen, Taastrup, Denmark
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Zubairova US, Verman PY, Oshchepkova PA, Elsukova AS, Doroshkov AV. LSM-W 2: laser scanning microscopy worker for wheat leaf surface morphology. BMC SYSTEMS BIOLOGY 2019; 13:22. [PMID: 30836965 PMCID: PMC6399813 DOI: 10.1186/s12918-019-0689-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Background Microscopic images are widely used in plant biology as an essential source of information on morphometric characteristics of the cells and the topological characteristics of cellular tissue pattern due to modern computer vision algorithms. High-resolution 3D confocal images allow extracting quantitative characteristics describing the cell structure of leaf epidermis. For some issues in the study of cereal leaves development, it is required to apply the staining techniques with fluorescent dyes and to scan rather large fragments consisting of several frames. We aimed to develop a tool for processing multi-frame multi-channel 3D images obtained from confocal laser scanning microscopy and taking into account the peculiarities of the cereal leaves staining. Results We elaborated an ImageJ-plugin LSM-W2 that allows extracting data on Leaf Surface Morphology from Laser Scanning Microscopy images. The plugin is a crucial link in a workflow for obtaining data on structural properties of leaf epidermis and morphological properties of epidermal cells. It allows converting large lsm-files (laser scanning microscopy) into segmented 2D/3D images or tables with data on cells and/or nuclei sizes. In the article, we also represent some case studies showing the plugin application for solving biological tasks. Namely the plugin is applied in the following cases: defining parameters of jigsaw-puzzle pattern for maize leaf epidermal cells, analysis of the pavement cells morphological parameters for the mature wheat leaf grown under control and water deficit conditions, initiation of cell longitudinal rows, and detection of guard mother cells emergence at the initial stages of the stomatal morphogenesis in the growth zone of a wheat leaf. Conclusion The proposed plugin is efficient for high-throughput analysis of cellular architecture for cereal leaf epidermis. The workflow implies using inexpensive and rapid sample preparation and does not require the applying of transgenesis and reporter genetic structures expanding the range of species and varieties to study. Obtained characteristics of the cell structure and patterns further could act as a basis for the development and verification for spatial models of plant tissues formation mechanisms accounting for structural features of cereal leaves. Availability The implementation of this workflow is available as an ImageJ plugin distributed as a part of the Fiji project (FijiisjustImageJ: https://fiji.sc/). The plugin is freely available at https://imagej.net/LSM_Worker, https://github.com/JmanJ/LSM_Worker
and http://pixie.bionet.nsc.ru/LSM_WORKER/. Electronic supplementary material The online version of this article (10.1186/s12918-019-0689-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ulyana S Zubairova
- Institute of Cytology and Genetics SB RAS, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia.
| | - Pavel Yu Verman
- Institute of Cytology and Genetics SB RAS, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia.,A.P. Ershov Institute of Informatics Systems SB RAS, Prospekt Lavrentyeva 6, Novosibirsk, 630090, Russia
| | | | - Alina S Elsukova
- Novosibirsk State University, Pirogova str. 1, Novosibirsk, 630090, Russia
| | - Alexey V Doroshkov
- Institute of Cytology and Genetics SB RAS, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia.,Novosibirsk State University, Pirogova str. 1, Novosibirsk, 630090, Russia
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Matzke AJ, Lin WD, Matzke M. Evidence That Ion-Based Signaling Initiating at the Cell Surface Can Potentially Influence Chromatin Dynamics and Chromatin-Bound Proteins in the Nucleus. FRONTIERS IN PLANT SCIENCE 2019; 10:1267. [PMID: 31681370 PMCID: PMC6811650 DOI: 10.3389/fpls.2019.01267] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 09/11/2019] [Indexed: 05/18/2023]
Abstract
We have developed tools and performed pilot experiments to test the hypothesis that an intracellular ion-based signaling pathway, provoked by an extracellular stimulus acting at the cell surface, can influence interphase chromosome dynamics and chromatin-bound proteins in the nucleus. The experimental system employs chromosome-specific fluorescent tags and the genome-encoded fluorescent pH sensor SEpHluorinA227D, which has been targeted to various intracellular membranes and soluble compartments in root cells of Arabidopsis thaliana. We are using this system and three-dimensional live cell imaging to visualize whether fluorescent-tagged interphase chromosome sites undergo changes in constrained motion concurrently with reductions in membrane-associated pH elicited by extracellular ATP, which is known to trigger a cascade of events in plant cells including changes in calcium ion concentrations, pH, and membrane potential. To examine possible effects of the proposed ion-based signaling pathway directly at the chromatin level, we generated a pH-sensitive fluorescent DNA-binding protein that allows pH changes to be monitored at specific genomic sites. Results obtained using these tools support the existence of a rapid, ion-based signaling pathway that initiates at the cell surface and reaches the nucleus to induce alterations in interphase chromatin mobility and the surrounding pH of chromatin-bound proteins. Such a pathway could conceivably act under natural circumstances to allow external stimuli to swiftly influence gene expression by affecting interphase chromosome movement and the structures and/or activities of chromatin-associated proteins.
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Affiliation(s)
| | | | - Marjori Matzke
- *Correspondence: Antonius J.M. Matzke, ; Marjori Matzke,
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Mochida K, Koda S, Inoue K, Hirayama T, Tanaka S, Nishii R, Melgani F. Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective. Gigascience 2019; 8:5232233. [PMID: 30520975 PMCID: PMC6312910 DOI: 10.1093/gigascience/giy153] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 09/06/2018] [Accepted: 11/24/2018] [Indexed: 11/29/2022] Open
Abstract
Employing computer vision to extract useful information from images and videos is becoming a key technique for identifying phenotypic changes in plants. Here, we review the emerging aspects of computer vision for automated plant phenotyping. Recent advances in image analysis empowered by machine learning-based techniques, including convolutional neural network-based modeling, have expanded their application to assist high-throughput plant phenotyping. Combinatorial use of multiple sensors to acquire various spectra has allowed us to noninvasively obtain a series of datasets, including those related to the development and physiological responses of plants throughout their life. Automated phenotyping platforms accelerate the elucidation of gene functions associated with traits in model plants under controlled conditions. Remote sensing techniques with image collection platforms, such as unmanned vehicles and tractors, are also emerging for large-scale field phenotyping for crop breeding and precision agriculture. Computer vision-based phenotyping will play significant roles in both the nowcasting and forecasting of plant traits through modeling of genotype/phenotype relationships.
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Affiliation(s)
- Keiichi Mochida
- Bioproductivity Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Microalgae Production Control Technology Laboratory, RIKEN Baton Zone Program, RIKEN Cluster for Science, Technology and Innovation Hub, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Institute of Plant Science and Resources, Okayama University, 2-20-1 Chuo, Kurashiki, Okayama 710-0046, Japan
- Kihara Institute for Biological Research, Yokohama City University, 641-12 Maioka-cho, Totsuka-ku, Yokohama, Kanagawa 244–0813, Japan
- Graduate School of Nanobioscience, Yokohama City University, 22-2 Seto, Kanazawa-ku, Yokohama, Kanagawa 236-0027, Japan
| | - Satoru Koda
- Graduate School of Mathematics, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
| | - Komaki Inoue
- Bioproductivity Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Takashi Hirayama
- Institute of Plant Science and Resources, Okayama University, 2-20-1 Chuo, Kurashiki, Okayama 710-0046, Japan
| | - Shojiro Tanaka
- Hiroshima University of Economics, 5-37-1, Gion, Asaminami, Hiroshima-shi Hiroshima 731-0138, Japan
| | - Ryuei Nishii
- Institute of Mathematics for Industry, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
| | - Farid Melgani
- Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, 38123 Trento, Italy
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Su Y, Wu F, Ao Z, Jin S, Qin F, Liu B, Pang S, Liu L, Guo Q. Evaluating maize phenotype dynamics under drought stress using terrestrial lidar. PLANT METHODS 2019; 15:11. [PMID: 30740137 PMCID: PMC6360786 DOI: 10.1186/s13007-019-0396-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 01/25/2019] [Indexed: 05/18/2023]
Abstract
BACKGROUND Maize (Zea mays L.) is the third most consumed grain in the world and improving maize yield is of great importance of the world food security, especially under global climate change and more frequent severe droughts. Due to the limitation of phenotyping methods, most current studies only focused on the responses of phenotypes on certain key growth stages. Although light detection and ranging (lidar) technology showed great potential in acquiring three-dimensional (3D) vegetation information, it has been rarely used in monitoring maize phenotype dynamics at an individual plant level. RESULTS In this study, we used a terrestrial laser scanner to collect lidar data at six growth stages for 20 maize varieties under drought stress. Three drought-related phenotypes, i.e., plant height, plant area index (PAI) and projected leaf area (PLA), were calculated from the lidar point clouds at the individual plant level. The results showed that terrestrial lidar data can be used to estimate plant height, PAI and PLA at an accuracy of 96%, 70% and 92%, respectively. All three phenotypes showed a pattern of first increasing and then decreasing during the growth period. The high drought tolerance group tended to keep lower plant height and PAI without losing PLA during the tasseling stage. Moreover, the high drought tolerance group inclined to have lower plant area density in the upper canopy than the low drought tolerance group. CONCLUSION The results demonstrate the feasibility of using terrestrial lidar to monitor 3D maize phenotypes under drought stress in the field and may provide new insights on identifying the key phenotypes and growth stages influenced by drought stress.
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Affiliation(s)
- Yanjun Su
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Fangfang Wu
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Zurui Ao
- Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275 China
| | - Shichao Jin
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Feng Qin
- College of Biological Sciences, China Agricultural University, Beijing, 100091 China
| | - Boxin Liu
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Shuxin Pang
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093 China
| | - Lingli Liu
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Qinghua Guo
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
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Enders TA, St. Dennis S, Oakland J, Callen ST, Gehan MA, Miller ND, Spalding EP, Springer NM, Hirsch CD. Classifying cold-stress responses of inbred maize seedlings using RGB imaging. PLANT DIRECT 2019; 3:e00104. [PMID: 31245751 PMCID: PMC6508840 DOI: 10.1002/pld3.104] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 11/12/2018] [Accepted: 12/06/2018] [Indexed: 05/05/2023]
Abstract
Increasing the tolerance of maize seedlings to low-temperature episodes could mitigate the effects of increasing climate variability on yield. To aid progress toward this goal, we established a growth chamber-based system for subjecting seedlings of 40 maize inbred genotypes to a defined, temporary cold stress while collecting digital profile images over a 9-daytime course. Image analysis performed with PlantCV software quantified shoot height, shoot area, 14 other morphological traits, and necrosis identified by color analysis. Hierarchical clustering of changes in growth rates of morphological traits and quantification of leaf necrosis over two time intervals resulted in three clusters of genotypes, which are characterized by unique responses to cold stress. For any given genotype, the set of traits with similar growth rates is unique. However, the patterns among traits are different between genotypes. Cold sensitivity was not correlated with the latitude where the inbred varieties were released suggesting potential further improvement for this trait. This work will serve as the basis for future experiments investigating the genetic basis of recovery to cold stress in maize seedlings.
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Affiliation(s)
- Tara A. Enders
- Department of Plant and Microbial BiologyUniversity of MinnesotaSt. PaulMinnesota
| | - Susan St. Dennis
- Department of Plant and Microbial BiologyUniversity of MinnesotaSt. PaulMinnesota
| | - Justin Oakland
- Department of Plant and Microbial BiologyUniversity of MinnesotaSt. PaulMinnesota
| | - Steven T. Callen
- Donald Danforth Plant Science CenterSt. LouisMissouri
- Present address:
Bayer U.S. Crop ScienceSt. LouisMissouri
| | | | - Nathan D. Miller
- Department of BotanyUniversity of Wisconsin‐MadisonMadisonWisconsin
| | | | - Nathan M. Springer
- Department of Plant and Microbial BiologyUniversity of MinnesotaSt. PaulMinnesota
| | - Cory D. Hirsch
- Department of Plant PathologyUniversity of MinnesotaSt. PaulMinnesota
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Varshney RK, Singh VK, Kumar A, Powell W, Sorrells ME. Can genomics deliver climate-change ready crops? CURRENT OPINION IN PLANT BIOLOGY 2018; 45:205-211. [PMID: 29685733 PMCID: PMC6250981 DOI: 10.1016/j.pbi.2018.03.007] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 03/25/2018] [Accepted: 03/27/2018] [Indexed: 05/20/2023]
Abstract
Development of climate resilient crops with accelerating genetic gains in crops will require integration of different disciplines/technologies, to see the impact in the farmer's field. In this review, we summarize how we are utilizing our germplasm collections to identify superior alleles/haplotypes through NGS based sequencing approaches and how genomics-enabled technologies together with precise phenotyping are being used in crop breeding. Pre-breeding and genomics-assisted breeding approaches are contributing to the more efficient development of climate-resilient crops. It is anticipated that the integration of several disciplines/technologies will result in the delivery of climate change ready crops in less time.
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Affiliation(s)
- Rajeev K Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502324, India.
| | - Vikas K Singh
- International Rice Research Institute (IRRI), IRRI South Asia Hub, ICRISAT, Patancheru 502324, India
| | - Arvind Kumar
- International Rice Research Institute (IRRI), IRRI South Asia Hub, ICRISAT, Patancheru 502324, India
| | - Wayne Powell
- SRUC (Scotland's Rural College), Peter Wilson Building, West Mains Road, Edinburgh EH9 3JG, UK
| | - Mark E Sorrells
- Department of Plant Breeding, 240 Emerson Hall, Cornell, Ithaca, NY 14853-1902, USA
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Manacorda CA, Asurmendi S. Arabidopsis phenotyping through geometric morphometrics. Gigascience 2018; 7:5039702. [PMID: 29917076 PMCID: PMC6041757 DOI: 10.1093/gigascience/giy073] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 02/07/2018] [Accepted: 06/11/2018] [Indexed: 12/18/2022] Open
Abstract
Background Recently, great technical progress has been achieved in the field of plant phenotyping. High-throughput platforms and the development of improved algorithms for rosette image segmentation make it possible to extract shape and size parameters for genetic, physiological, and environmental studies on a large scale. The development of low-cost phenotyping platforms and freeware resources make it possible to widely expand phenotypic analysis tools for Arabidopsis. However, objective descriptors of shape parameters that could be used independently of the platform and segmentation software used are still lacking, and shape descriptions still rely on ad hoc or even contradictory descriptors, which could make comparisons difficult and perhaps inaccurate. Modern geometric morphometrics is a family of methods in quantitative biology proposed to be the main source of data and analytical tools in the emerging field of phenomics studies. Based on the location of landmarks (corresponding points) over imaged specimens and by combining geometry, multivariate analysis, and powerful statistical techniques, these tools offer the possibility to reproducibly and accurately account for shape variations among groups and measure them in shape distance units. Results Here, a particular scheme of landmark placement on Arabidopsis rosette images is proposed to study shape variation in viral infection processes. Shape differences between controls and infected plants are quantified throughout the infectious process and visualized. Quantitative comparisons between two unrelated ssRNA+ viruses are shown, and reproducibility issues are assessed. Conclusions Combined with the newest automated platforms and plant segmentation procedures, geometric morphometric tools could boost phenotypic features extraction and processing in an objective, reproducible manner.
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Affiliation(s)
- Carlos A Manacorda
- Instituto de Biotecnología, CICVyA, INTA, Nicolas Repetto y de los Reseros s/n, Hurlingham, (1686) Buenos Aires, Argentina
| | - Sebastian Asurmendi
- Instituto de Biotecnología, CICVyA, INTA, Nicolas Repetto y de los Reseros s/n, Hurlingham, (1686) Buenos Aires, Argentina
- CONICET, Nicolas Repetto y de los Reseros s/n, Hurlingham, (1686) Buenos Aires, Argentina
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Thomas S, Behmann J, Steier A, Kraska T, Muller O, Rascher U, Mahlein AK. Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform. PLANT METHODS 2018; 14:45. [PMID: 29930695 PMCID: PMC5994119 DOI: 10.1186/s13007-018-0313-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Accepted: 05/31/2018] [Indexed: 05/19/2023]
Abstract
BACKGROUND Phenotyping is a bottleneck for the development of new plant cultivars. This study introduces a new hyperspectral phenotyping system, which combines the high throughput of canopy scale measurements with the advantages of high spatial resolution and a controlled measurement environment. Furthermore, the measured barley canopies were grown in large containers (called Mini-Plots), which allow plants to develop field-like phenotypes in greenhouse experiments, without being hindered by pot size. RESULTS Six barley cultivars have been investigated via hyperspectral imaging up to 30 days after inoculation with powdery mildew. With a high spatial resolution and stable measurement conditions, it was possible to automatically quantify powdery mildew symptoms through a combination of Simplex Volume Maximization and Support Vector Machines. Detection was feasible as soon as the first symptoms were visible for the human eye during manual rating. An accurate assessment of the disease severity for all cultivars at each measurement day over the course of the experiment was realized. Furthermore, powdery mildew resistance based necrosis of one cultivar was detected as well. CONCLUSION The hyperspectral phenotyping system combines the advantages of field based canopy level measurement systems (high throughput, automatization, low manual workload) with those of laboratory based leaf level measurement systems (high spatial resolution, controlled environment, stable conditions for time series measurements). This allows an accurate and objective disease severity assessment without the need for trained experts, who perform visual rating, as well as detection of disease symptoms in early stages. Therefore, it is a promising tool for plant resistance breeding.
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Affiliation(s)
- Stefan Thomas
- INRES-Plant Protection and Plant Diseases, University Bonn, Bonn, Germany
- IBG2: Plant Sciences, Forschungszentrum Jülich GMBH, Jülich, Germany
| | - Jan Behmann
- INRES-Plant Protection and Plant Diseases, University Bonn, Bonn, Germany
| | - Angelina Steier
- IBG2: Plant Sciences, Forschungszentrum Jülich GMBH, Jülich, Germany
| | - Thorsten Kraska
- Field Lab Campus Klein-Altendorf, University Bonn, Bonn, Germany
| | - Onno Muller
- IBG2: Plant Sciences, Forschungszentrum Jülich GMBH, Jülich, Germany
| | - Uwe Rascher
- IBG2: Plant Sciences, Forschungszentrum Jülich GMBH, Jülich, Germany
| | - Anne-Katrin Mahlein
- INRES-Plant Protection and Plant Diseases, University Bonn, Bonn, Germany
- Institute of Sugar Beet Research (IfZ), Göttingen, Germany
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Ganthaler A, Losso A, Mayr S. Using image analysis for quantitative assessment of needle bladder rust disease of Norway spruce. PLANT PATHOLOGY 2018; 67:1122-1130. [PMID: 29861507 PMCID: PMC5969058 DOI: 10.1111/ppa.12842] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
High elevation spruce forests of the European Alps are frequently infected by the needle rust Chrysomyxa rhododendri, a pathogen causing remarkable defoliation, reduced tree growth and limited rejuvenation. Exact quantification of the disease severity on different spatial scales is crucial for monitoring, management and resistance breeding activities. Based on the distinct yellow discolouration of attacked needles, it was investigated whether image analysis of digital photographs can be used to quantify disease severity and to improve phenotyping compared to conventional assessment in terms of time, effort and application range. The developed protocol for preprocessing and analysis of digital RGB images enabled identification of disease symptoms and healthy needle areas on images obtained in ground surveys (total number of analysed images n = 62) and by the use of a semiprofessional quadcopter (n = 13). Obtained disease severities correlated linearly with results obtained by manual counting of healthy and diseased needles for all approaches, including images of individual branches with natural background (R2 = 0.87) and with black background (R2 = 0.95), juvenile plants (R2 = 0.94), and top views and side views of entire tree crowns of adult trees (R2 = 0.98 and 0.88, respectively). Results underline that a well-defined signal related to needle bladder rust symptoms of Norway spruce can be extracted from images recorded by standard digital cameras and using drones. The presented protocol enables precise and time-efficient quantification of disease symptoms caused by C. rhododendri and provides several advantages compared to conventional assessment by manual counting or visual estimations.
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Affiliation(s)
- A. Ganthaler
- Department of BotanyUniversity InnsbruckSternwartestrasse 15InnsbruckA‐6020Austria
| | - A. Losso
- Department of BotanyUniversity InnsbruckSternwartestrasse 15InnsbruckA‐6020Austria
| | - S. Mayr
- Department of BotanyUniversity InnsbruckSternwartestrasse 15InnsbruckA‐6020Austria
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40
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Nguyen GN, Kant S. Improving nitrogen use efficiency in plants: effective phenotyping in conjunction with agronomic and genetic approaches. FUNCTIONAL PLANT BIOLOGY : FPB 2018; 45:606-619. [PMID: 32290963 DOI: 10.1071/fp17266] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 01/04/2018] [Indexed: 05/03/2023]
Abstract
For global sustainable food production and environmental benefits, there is an urgent need to improve N use efficiency (NUE) in crop plants. Excessive and inefficient use of N fertiliser results in increased crop production costs and environmental pollution. Therefore, cost-effective strategies such as proper management of the timing and quantity of N fertiliser application, and breeding for better varieties are needed to improve NUE in crops. However, for these efforts to be feasible, high-throughput and reliable phenotyping techniques would be very useful for monitoring N status in planta, as well as to facilitate faster decisions during breeding and selection processes. This review provides an insight into contemporary approaches to phenotyping NUE-related traits and associated challenges. We discuss recent and advanced, sensor- and image-based phenotyping techniques that use a variety of equipment, tools and platforms. The review also elaborates on how high-throughput phenotyping will accelerate efforts for screening large populations of diverse genotypes in controlled environment and field conditions to identify novel genotypes with improved NUE.
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Affiliation(s)
- Giao N Nguyen
- Agriculture Victoria, Grains Innovation Park, 110 Natimuk Road, Horsham, Vic. 3400, Australia
| | - Surya Kant
- Agriculture Victoria, Grains Innovation Park, 110 Natimuk Road, Horsham, Vic. 3400, Australia
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41
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Mothersill C, Abend M, Bréchignac F, Iliakis G, Impens N, Kadhim M, Møller AP, Oughton D, Powathil G, Saenen E, Seymour C, Sutcliffe J, Tang FR, Schofield PN. When a duck is not a duck; a new interdisciplinary synthesis for environmental radiation protection. ENVIRONMENTAL RESEARCH 2018; 162:318-324. [PMID: 29407763 DOI: 10.1016/j.envres.2018.01.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Revised: 01/18/2018] [Accepted: 01/19/2018] [Indexed: 06/07/2023]
Abstract
This consensus paper presents the results of a workshop held in Essen, Germany in September 2017, called to examine critically the current approach to radiological environmental protection. The meeting brought together participants from the field of low dose radiobiology and those working in radioecology. Both groups have a common aim of identifying radiation exposures and protecting populations and individuals from harmful effects of ionising radiation exposure, but rarely work closely together. A key question in radiobiology is to understand mechanisms triggered by low doses or dose rates, leading to adverse outcomes of individuals while in radioecology a key objective is to recognise when harm is occurring at the level of the ecosystem. The discussion provided a total of six strategic recommendations which would help to address these questions.
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Affiliation(s)
- Carmel Mothersill
- Department of Biology, McMaster University, Hamilton, Ontario, Canada L8S 4K1.
| | - Michael Abend
- Bundeswehr Institute of Radiobiology, Neuherbergstr. 11, 80937 Munich, Germany.
| | - François Bréchignac
- Institute for Radioprotection and Nuclear Safety (IRSN) & International Union of Radioecology (IUR), Centre du Cadarache, Bldg 229, St Paul-lez-Durance, France.
| | - George Iliakis
- Institute of Medical Radiation Biology, University of Duisburg-Essen, Medical School, Hufeland Str. 55, 45122 Essen, Germany.
| | - Nathalie Impens
- Institute of Environment, Health and Safety, Biosphere Impact Studies, SCK•CEN, Boeretang 200, 2400 Mol, Belgium.
| | - Munira Kadhim
- Department of Biological and Medical Sciences, Oxford Brookes University, Oxford, UK.
| | - Anders Pape Møller
- Ecologie Systématique Evolution, Equipe Diversité, Ecologie et Evolution Microbiennes Université Paris-Sud, CNRS, and AgroParisTech, Université Paris-Saclay, F-91405 Orsay Cedex, France.
| | - Deborah Oughton
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Campus Ås, Universitetstunet 3, 1432 Ås, Norway.
| | - Gibin Powathil
- Department of Mathematics, College of Science, Swansea University, Singleton Park, Swansea Wales SA2 8PP, UK.
| | - Eline Saenen
- Institute of Environment, Health and Safety, Biosphere Impact Studies, SCK•CEN, Boeretang 200, 2400 Mol, Belgium.
| | - Colin Seymour
- Department of Biology, McMaster University, Hamilton, Ontario, Canada L8S 4K1.
| | - Jill Sutcliffe
- Low Level Radiation and Health Group, Ingrams Farm Fittleworth Road, Wisborough Green RH14 0JA, West Sussex, UK.
| | - Fen-Ru Tang
- National University of Singapore, Radiobiology Research Laboratory, Singapore Nuclear, Research and Safety Initiative, Singapore.
| | - Paul N Schofield
- Dept of Physiology Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3EG, UK.
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Reeb C, Kaandorp J, Jansson F, Puillandre N, Dubuisson JY, Cornette R, Jabbour F, Coudert Y, Patiño J, Flot JF, Vanderpoorten A. Quantification of complex modular architecture in plants. THE NEW PHYTOLOGIST 2018; 218:859-872. [PMID: 29468683 DOI: 10.1111/nph.15045] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Accepted: 01/07/2018] [Indexed: 06/08/2023]
Abstract
Morphometrics, the assignment of quantities to biological shapes, is a powerful tool to address taxonomic, evolutionary, functional and developmental questions. We propose a novel method for shape quantification of complex modular architecture in thalloid plants, whose extremely reduced morphologies, combined with the lack of a formal framework for thallus description, have long rendered taxonomic and evolutionary studies extremely challenging. Using graph theory, thalli are described as hierarchical series of nodes and edges, allowing for accurate, homologous and repeatable measurements of widths, lengths and angles. The computer program MorphoSnake was developed to extract the skeleton and contours of a thallus and automatically acquire, at each level of organization, width, length, angle and sinuosity measurements. Through the quantification of leaf architecture in Hymenophyllum ferns (Polypodiopsida) and a fully worked example of integrative taxonomy in the taxonomically challenging thalloid liverwort genus Riccardia, we show that MorphoSnake is applicable to all ramified plants. This new possibility of acquiring large numbers of quantitative traits in plants with complex modular architectures opens new perspectives of applications, from the development of rapid species identification tools to evolutionary analyses of adaptive plasticity.
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Affiliation(s)
- Catherine Reeb
- Institut de Systématique, Évolution, Biodiversité (ISYEB - UMR7205 - Sorbonne Universités MNHN, CNRS, EPHE) Muséum national d'Histoire Naturelle, 57 rue Cuvier CP 50, 75005, Paris, France
| | - Jaap Kaandorp
- Computational Science Lab, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands
| | - Fredrik Jansson
- Computational Science Lab, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands
| | - Nicolas Puillandre
- Institut de Systématique, Évolution, Biodiversité (ISYEB - UMR7205 - Sorbonne Universités MNHN, CNRS, EPHE) Muséum national d'Histoire Naturelle, 57 rue Cuvier CP 50, 75005, Paris, France
| | - Jean-Yves Dubuisson
- Institut de Systématique, Évolution, Biodiversité (ISYEB - UMR7205 - Sorbonne Universités MNHN, CNRS, EPHE) Muséum national d'Histoire Naturelle, 57 rue Cuvier CP 50, 75005, Paris, France
| | - Raphaël Cornette
- Équipe Évolution et Développement des Variations Phénotypiques (ISYEB - UMR7205 - MNHN, CNRS, Sorbonne Universités EPHE) Muséum national d'Histoire Naturelle, Sorbonne Universités, 57 rue Cuvier CP 50, 75005, Paris, France
| | - Florian Jabbour
- Institut de Systématique, Évolution, Biodiversité (ISYEB - UMR7205 - Sorbonne Universités MNHN, CNRS, EPHE) Muséum national d'Histoire Naturelle, 57 rue Cuvier CP 50, 75005, Paris, France
| | - Yoan Coudert
- Laboratoire Reproduction et Développement des Plantes, Ecole Normale Supérieure de Lyon, CNRS, INRA, Université Claude Bernard Lyon 1, 46 Allée d'Italie, 69007, Lyon, France
| | - Jairo Patiño
- Island Ecology and Evolution Research Group, Instituto de Productos Naturales β Agrobiología (IPNA-CSIC), La Laguna, Tenerife, Spain
- Department of Environmental Science, Policy and Management, University of California, Berkeley, CA, 94720, USA
| | - Jean-François Flot
- Evolutionary Biology & Ecology, Université Libre de Bruxelles, Avenue F.D. Roosevelt 50, C.P. 160/12, 1050, Brussels, Belgium
| | - Alain Vanderpoorten
- Institute of Botany, University of Liège, B22 Sart Tilman, 4000, Liège, Belgium
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Schneider JV, Rabenstein R, Wesenberg J, Wesche K, Zizka G, Habersetzer J. Improved non-destructive 2D and 3D X-ray imaging of leaf venation. PLANT METHODS 2018; 14:7. [PMID: 29375648 PMCID: PMC5774031 DOI: 10.1186/s13007-018-0274-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Accepted: 01/09/2018] [Indexed: 05/29/2023]
Abstract
BACKGROUND Leaf venation traits are important for many research fields such as systematics and evolutionary biology, plant physiology, climate change, and paleoecology. In spite of an increasing demand for vein trait data, studies are often still data-limited because the development of methods that allow rapid generation of large sets of vein data has lagged behind. Recently, non-destructive X-ray technology has proven useful as an alternative to traditional slow and destructive chemical-based methods. Non-destructive techniques more readily allow the use of herbarium specimens, which provide an invaluable but underexploited resource of vein data and related environmental information. The utility of 2D X-ray technology and microfocus X-ray computed tomography, however, has been compromised by insufficient image resolution. Here, we advanced X-ray technology by increasing image resolution and throughput without the application of contrast agents. RESULTS For 2D contact microradiography, we developed a method which allowed us to achieve image resolutions of up to 7 µm, i.e. a 3.6-fold increase compared to the industrial standard (25 µm resolution). Vein tracing was further optimized with our image processing standards that were specifically adjusted for different types of leaf structure and the needs of higher imaging throughput. Based on a test dataset, in 91% of the samples the 7 µm approach led to a significant improvement in estimations of minor vein density compared to the industrial standard. Using microfocus X-ray computed tomography, very high-resolution images were obtained from a virtual 3D-2D transformation process, which was superior to that of 3D images. CONCLUSIONS Our 2D X-ray method with a significantly improved resolution advances rapid non-destructive bulk scanning at a quality that in many cases is sufficient to determine key venation traits. Together with our high-resolution microfocus X-ray computed tomography method, both non-destructive approaches will help in vein trait data mining from museum collections, which provide an underexploited resource of historical and recent data on environmental and evolutionary change. In spite of the significant increase in effective image resolution, a combination of high-throughput and full visibility of the vein network (including the smallest veins and their connectivity) remains challenging, however.
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Affiliation(s)
- Julio V. Schneider
- Department of Botany and Molecular Evolution, Senckenberg Research Institute and Natural History Museum Frankfurt, Senckenberganlage 25, 60325 Frankfurt, Germany
- Institute of Ecology, Evolution and Diversity, Goethe-University, Max-von-Laue-Str. 13, 60439 Frankfurt, Germany
| | - Renate Rabenstein
- Department of Messel Research and Mammalogy, Senckenberg Research Institute and Natural History Museum Frankfurt, Senckenberganlage 25, 60325 Frankfurt, Germany
| | - Jens Wesenberg
- Department of Botany, Senckenberg Museum of Natural History Görlitz, Am Museum 1, 02826 Görlitz, Germany
| | - Karsten Wesche
- Department of Botany, Senckenberg Museum of Natural History Görlitz, Am Museum 1, 02826 Görlitz, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany
- International Institute Zittau, Technische Universität Dresden, Markt 23, 02763 Zittau, Germany
| | - Georg Zizka
- Department of Botany and Molecular Evolution, Senckenberg Research Institute and Natural History Museum Frankfurt, Senckenberganlage 25, 60325 Frankfurt, Germany
- Institute of Ecology, Evolution and Diversity, Goethe-University, Max-von-Laue-Str. 13, 60439 Frankfurt, Germany
| | - Jörg Habersetzer
- Department of Messel Research and Mammalogy, Senckenberg Research Institute and Natural History Museum Frankfurt, Senckenberganlage 25, 60325 Frankfurt, Germany
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Failmezger H, Lempe J, Khadem N, Cartolano M, Tsiantis M, Tresch A. MowJoe: a method for automated-high throughput dissected leaf phenotyping. PLANT METHODS 2018; 14:27. [PMID: 29599815 PMCID: PMC5868070 DOI: 10.1186/s13007-018-0290-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Accepted: 03/13/2018] [Indexed: 05/21/2023]
Abstract
BACKGROUND Accurate and automated phenotyping of leaf images is necessary for high throughput studies of leaf form like genome-wide association analysis and other forms of quantitative trait locus mapping. Dissected leaves (also referred to as compound) that are subdivided into individual units are an attractive system to study diversification of form. However, there are only few software tools for their automated analysis. Thus, high-throughput image processing algorithms are needed that can partition these leaves in their phenotypically relevant units and calculate morphological features based on these units. RESULTS We have developed MowJoe, an image processing algorithm that dissects a dissected leaf into leaflets, petiolule, rachis and petioles. It employs image skeletonization to convert leaves into graphs, and thereafter applies algorithms operating on graph structures. This partitioning of a leaf allows the derivation of morphological features such as leaf size, or eccentricity of leaflets. Furthermore, MowJoe automatically places landmarks onto the terminal leaflet that can be used for further leaf shape analysis. It generates specific output files that can directly be imported into downstream shape analysis tools. We applied the algorithm to two accessions of Cardamine hirsuta and show that our features are able to robustly discriminate between these accessions. CONCLUSION MowJoe is a tool for the semi-automated, quantitative high throughput shape analysis of dissected leaf images. It provides the statistical power for the detection of the genetic basis of quantitative morphological variations.
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Affiliation(s)
- Henrik Failmezger
- Max Planck Institute for Plant Breeding Research, Carl-von-Linné-Weg 10, 50829 Cologne, Germany
- Department of Biology, University of Cologne, Zülpicher Str. 47, 50674 Cologne, Germany
| | - Janne Lempe
- Max Planck Institute for Plant Breeding Research, Carl-von-Linné-Weg 10, 50829 Cologne, Germany
| | - Nasim Khadem
- Department of Biology, University of Cologne, Zülpicher Str. 47, 50674 Cologne, Germany
| | - Maria Cartolano
- Max Planck Institute for Plant Breeding Research, Carl-von-Linné-Weg 10, 50829 Cologne, Germany
| | - Miltos Tsiantis
- Max Planck Institute for Plant Breeding Research, Carl-von-Linné-Weg 10, 50829 Cologne, Germany
| | - Achim Tresch
- Max Planck Institute for Plant Breeding Research, Carl-von-Linné-Weg 10, 50829 Cologne, Germany
- Institute of Medical Statistics and Computational Biology, University of Cologne, Bachemer Strasse 86, 50931 Cologne, Germany
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Blazakis KN, Kosma M, Kostelenos G, Baldoni L, Bufacchi M, Kalaitzis P. Description of olive morphological parameters by using open access software. PLANT METHODS 2017; 13:111. [PMID: 29238398 PMCID: PMC5725956 DOI: 10.1186/s13007-017-0261-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2017] [Accepted: 11/29/2017] [Indexed: 05/16/2023]
Abstract
BACKGROUND The morphological analysis of olive leaves, fruits and endocarps may represent an efficient tool for the characterization and discrimination of cultivars and the establishment of relationships among them. In recent years, much attention has been focused on the application of molecular markers, due to their high diagnostic efficiency and independence from environmental and phenological variables. RESULTS In this study, we present a semi-automatic methodology of detecting various morphological parameters. With the aid of computing and image analysis tools, we created semi-automatic algorithms applying intuitive mathematical descriptors that quantify many fruit, leaf and endocarp morphological features. In particular, we examined quantitative and qualitative characters such as size, shape, symmetry, contour roughness and presence of additional structures such as nipple, petiole, endocarp surface roughness, etc.. CONCLUSION We illustrate the performance and the applicability of our approach on Greek olive cultivars; on sets of images from fruits, leaves and endocarps. In addition, the proposed methodology was also applied for the description of other crop species morphologies such as tomato, grapevine and pear. This allows us to describe crop morphologies efficiently and robustly in a semi-automated way.
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Affiliation(s)
- Konstantinos N. Blazakis
- Department of Horticultural Genetics and Biotechnology, Mediterranean Agronomic Institute of Chania (MAICh), Alsyllio Agrokipiou, PO BOX 85, 73100 Chania-Crete, Greece
| | - Maria Kosma
- Department of Horticultural Genetics and Biotechnology, Mediterranean Agronomic Institute of Chania (MAICh), Alsyllio Agrokipiou, PO BOX 85, 73100 Chania-Crete, Greece
| | | | - Luciana Baldoni
- Italian National Research Council, Institute of Biosciences and Bio-Resources (CNR-IBBR), Via Madonna Alta, 130-06128 Perugia, Italy
| | - Marina Bufacchi
- Italian National Research Council, Institute for Agriculture and Forest Systems in the Mediterranean (CNR-ISAFOM), Via Madonna Alta, 130-06128 Perugia, Italy
| | - Panagiotis Kalaitzis
- Department of Horticultural Genetics and Biotechnology, Mediterranean Agronomic Institute of Chania (MAICh), Alsyllio Agrokipiou, PO BOX 85, 73100 Chania-Crete, Greece
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Gehan MA, Fahlgren N, Abbasi A, Berry JC, Callen ST, Chavez L, Doust AN, Feldman MJ, Gilbert KB, Hodge JG, Hoyer JS, Lin A, Liu S, Lizárraga C, Lorence A, Miller M, Platon E, Tessman M, Sax T. PlantCV v2: Image analysis software for high-throughput plant phenotyping. PeerJ 2017; 5:e4088. [PMID: 29209576 PMCID: PMC5713628 DOI: 10.7717/peerj.4088] [Citation(s) in RCA: 110] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Accepted: 11/03/2017] [Indexed: 12/11/2022] Open
Abstract
Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.
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Affiliation(s)
- Malia A. Gehan
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Noah Fahlgren
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Arash Abbasi
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Jeffrey C. Berry
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Steven T. Callen
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Current affiliation: Monsanto Company, St. Louis, MO, United States of America
| | - Leonardo Chavez
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Andrew N. Doust
- Department of Plant Biology, Ecology, and Evolution, Oklahoma State University, Stillwater, OK, United States of America
| | - Max J. Feldman
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Kerrigan B. Gilbert
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - John G. Hodge
- Department of Plant Biology, Ecology, and Evolution, Oklahoma State University, Stillwater, OK, United States of America
| | - J. Steen Hoyer
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Computational and Systems Biology Program, Washington University in St. Louis, St. Louis, MO, United States of America
| | - Andy Lin
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Current affiliation: Unidev, St. Louis, MO, United States of America
| | - Suxing Liu
- Arkansas Biosciences Institute, Arkansas State University, Jonesboro, AR, United States of America
- Current affiliation: Department of Plant Biology, University of Georgia, Athens, GA, United States of America
| | - César Lizárraga
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Current affiliation: CiBO Technologies, Cambridge, MA, United States of America
| | - Argelia Lorence
- Arkansas Biosciences Institute, Department of Chemistry and Physics, Arkansas State University, Jonesboro, AR, United States of America
| | - Michael Miller
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Current affiliation: Department of Agronomy and Horticulture, Center for Plant Science Innovation, Beadle Center for Biotechnology, University of Nebraska - Lincoln, Lincoln, NE, United States of America
| | | | - Monica Tessman
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Department of Plant Biology, Ecology, and Evolution, Oklahoma State University, Stillwater, OK, United States of America
| | - Tony Sax
- Missouri University of Science and Technology, Rolla, MO, United States of America
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Dobrescu A, Scorza LCT, Tsaftaris SA, McCormick AJ. A "Do-It-Yourself" phenotyping system: measuring growth and morphology throughout the diel cycle in rosette shaped plants. PLANT METHODS 2017; 13:95. [PMID: 29151842 PMCID: PMC5678596 DOI: 10.1186/s13007-017-0247-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Accepted: 10/26/2017] [Indexed: 05/19/2023]
Abstract
BACKGROUND Improvements in high-throughput phenotyping technologies are rapidly expanding the scope and capacity of plant biology studies to measure growth traits. Nevertheless, the costs of commercial phenotyping equipment and infrastructure remain prohibitively expensive for wide-scale uptake, while academic solutions can require significant local expertise. Here we present a low-cost methodology for plant biologists to build their own phenotyping system for quantifying growth rates and phenotypic characteristics of Arabidopsis thaliana rosettes throughout the diel cycle. RESULTS We constructed an image capture system consisting of a near infra-red (NIR, 940 nm) LED panel with a mounted Raspberry Pi NoIR camera and developed a MatLab-based software module (iDIEL Plant) to characterise rosette expansion. Our software was able to accurately segment and characterise multiple rosettes within an image, regardless of plant arrangement or genotype, and batch process image sets. To further validate our system, wild-type Arabidopsis plants (Col-0) and two mutant lines with reduced Rubisco contents, pale leaves and slow growth phenotypes (1a3b and 1a2b) were grown on a single plant tray. Plants were imaged from 9 to 24 days after germination every 20 min throughout the 24 h light-dark growth cycle (i.e. the diel cycle). The resulting dataset provided a dynamic and uninterrupted characterisation of differences in rosette growth and expansion rates over time for the three lines tested. CONCLUSION Our methodology offers a straightforward solution for setting up automated, scalable and low-cost phenotyping facilities in a wide range of lab environments that could greatly increase the processing power and scalability of Arabidopsis soil growth experiments.
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Affiliation(s)
- Andrei Dobrescu
- Institute of Digital Communications, School of Engineering, University of Edinburgh, Edinburgh, EH9 3FB UK
- Daniel Rutherford Building, SynthSys and Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, The King’s Buildings, Edinburgh, EH9 3BF UK
| | - Livia C. T. Scorza
- Daniel Rutherford Building, SynthSys and Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, The King’s Buildings, Edinburgh, EH9 3BF UK
| | - Sotirios A. Tsaftaris
- Institute of Digital Communications, School of Engineering, University of Edinburgh, Edinburgh, EH9 3FB UK
| | - Alistair J. McCormick
- Daniel Rutherford Building, SynthSys and Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, The King’s Buildings, Edinburgh, EH9 3BF UK
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