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Casavecchia S, Giannelli F, Giovannotti M, Trucchi E, Carducci F, Quattrini G, Lucchetti L, Barucca M, Canapa A, Biscotti MA, Aquilanti L, Pesaresi S. Morphological and Genomic Differences in the Italian Populations of Onopordum tauricum Willd.-A New Source of Vegetable Rennet. PLANTS (BASEL, SWITZERLAND) 2024; 13:654. [PMID: 38475500 DOI: 10.3390/plants13050654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/19/2024] [Accepted: 02/21/2024] [Indexed: 03/14/2024]
Abstract
Onopordum tauricum Willd., a species distributed in Eastern Europe, has been the subject of various research endeavors aimed at assessing its suitability for extracting vegetable rennet for use in the production of local cheeses as a substitute for animal-derived rennet. In Italy, the species has an extremely fragmented and localized distribution in six locations scattered across the central-northern Apennines and some areas of southern Italy. In this study, both the morphology and genetic diversity of the six known Italian populations were investigated to detect putative ecotypes. To this end, 33 morphological traits were considered for morphometric measurements, while genetic analysis was conducted on the entire genome using the ddRAD-Seq method. Both analyses revealed significant differences among the Apennine populations (SOL, COL, and VIS) and those from southern Italy (ROT, PES, and LEC). Specifically, the southern Italian populations appear to deviate significantly in some characteristics from the typical form of the species. Therefore, its attribution to O. tauricum is currently uncertain, and further genetic and morphological analyses are underway to ascertain its systematic placement within the genus Onopordum.
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Affiliation(s)
- Simona Casavecchia
- Department of Agriculture, Food and Environmental Sciences, Marche Polytechnic University, Via Brecce Bianche, 60131 Ancona, Italy
| | - Francesco Giannelli
- Department of Life and Environmental Sciences, Marche Polytechnic University, Via Brecce Bianche, 60131 Ancona, Italy
| | - Massimo Giovannotti
- Department of Life and Environmental Sciences, Marche Polytechnic University, Via Brecce Bianche, 60131 Ancona, Italy
| | - Emiliano Trucchi
- Department of Life and Environmental Sciences, Marche Polytechnic University, Via Brecce Bianche, 60131 Ancona, Italy
| | - Federica Carducci
- Department of Life and Environmental Sciences, Marche Polytechnic University, Via Brecce Bianche, 60131 Ancona, Italy
| | - Giacomo Quattrini
- Department of Agriculture, Food and Environmental Sciences, Marche Polytechnic University, Via Brecce Bianche, 60131 Ancona, Italy
| | - Lara Lucchetti
- Department of Agriculture, Food and Environmental Sciences, Marche Polytechnic University, Via Brecce Bianche, 60131 Ancona, Italy
| | - Marco Barucca
- Department of Life and Environmental Sciences, Marche Polytechnic University, Via Brecce Bianche, 60131 Ancona, Italy
| | - Adriana Canapa
- Department of Life and Environmental Sciences, Marche Polytechnic University, Via Brecce Bianche, 60131 Ancona, Italy
| | - Maria Assunta Biscotti
- Department of Life and Environmental Sciences, Marche Polytechnic University, Via Brecce Bianche, 60131 Ancona, Italy
| | - Lucia Aquilanti
- Department of Agriculture, Food and Environmental Sciences, Marche Polytechnic University, Via Brecce Bianche, 60131 Ancona, Italy
| | - Simone Pesaresi
- Department of Agriculture, Food and Environmental Sciences, Marche Polytechnic University, Via Brecce Bianche, 60131 Ancona, Italy
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Zhang Y, Zhang N, Chai X, Sun T. Machine learning for image-based multi-omics analysis of leaf veins. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:4928-4941. [PMID: 37410807 DOI: 10.1093/jxb/erad251] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 06/29/2023] [Indexed: 07/08/2023]
Abstract
Veins are a critical component of the plant growth and development system, playing an integral role in supporting and protecting leaves, as well as transporting water, nutrients, and photosynthetic products. A comprehensive understanding of the form and function of veins requires a dual approach that combines plant physiology with cutting-edge image recognition technology. The latest advancements in computer vision and machine learning have facilitated the creation of algorithms that can identify vein networks and explore their developmental progression. Here, we review the functional, environmental, and genetic factors associated with vein networks, along with the current status of research on image analysis. In addition, we discuss the methods of venous phenotype extraction and multi-omics association analysis using machine learning technology, which could provide a theoretical basis for improving crop productivity by optimizing the vein network architecture.
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Affiliation(s)
- Yubin Zhang
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St, Beijing 100081, China
| | - Ning Zhang
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St, Beijing 100081, China
| | - Xiujuan Chai
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St, Beijing 100081, China
| | - Tan Sun
- Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing, China
- Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St, Beijing 100081, China
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Zhang W, Wu S, Wen W, Lu X, Wang C, Gou W, Li Y, Guo X, Zhao C. Three-dimensional branch segmentation and phenotype extraction of maize tassel based on deep learning. PLANT METHODS 2023; 19:76. [PMID: 37528454 PMCID: PMC10394845 DOI: 10.1186/s13007-023-01051-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 07/10/2023] [Indexed: 08/03/2023]
Abstract
BACKGROUND The morphological structure phenotype of maize tassel plays an important role in plant growth, reproduction, and yield formation. It is an important step in the distinctness, uniformity, and stability (DUS) testing to obtain maize tassel phenotype traits. Plant organ segmentation can be achieved with high-precision and automated acquisition of maize tassel phenotype traits because of the advances in the point cloud deep learning method. However, this method requires a large number of data sets and is not robust to automatic segmentation of highly adherent organ components; thus, it should be combined with point cloud processing technology. RESULTS An innovative method of incomplete annotation of point cloud data was proposed for easy development of the dataset of maize tassels,and an automatic maize tassel phenotype analysis system: MaizeTasselSeg was developed. The tip feature of point cloud is trained and learned based on PointNet + + network, and the tip point cloud of tassel branch was automatically segmented. Complete branch segmentation was realized based on the shortest path algorithm. The Intersection over Union (IoU), precision, and recall of the segmentation results were 96.29, 96.36, and 93.01, respectively. Six phenotypic traits related to morphological structure (branch count, branch length, branch angle, branch curvature, tassel volume, and dispersion) were automatically extracted from the segmentation point cloud. The squared correlation coefficients (R2) for branch length, branch angle, and branch count were 0.9897, 0.9317, and 0.9587, respectively. The root mean squared error (RMSE) for branch length, branch angle, and branch count were 0.529 cm, 4.516, and 0.875, respectively. CONCLUSION The proposed method provides an efficient scheme for high-throughput organ segmentation of maize tassels and can be used for the automatic extraction of phenotypic traits of maize tassel. In addition, the incomplete annotation approach provides a new idea for morphology-based plant segmentation.
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Affiliation(s)
- Wenqi Zhang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
- College of Information Technology, Shanghai Ocean University, Shanghai, 201306, China
| | - Sheng Wu
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
| | - Weiliang Wen
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
| | - Xianju Lu
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
| | - Chuanyu Wang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
| | - Wenbo Gou
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
| | - Yuankun Li
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
| | - Xinyu Guo
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China.
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.
| | - Chunjiang Zhao
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China.
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.
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Abebe AM, Kim Y, Kim J, Kim SL, Baek J. Image-Based High-Throughput Phenotyping in Horticultural Crops. PLANTS (BASEL, SWITZERLAND) 2023; 12:2061. [PMID: 37653978 PMCID: PMC10222289 DOI: 10.3390/plants12102061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/12/2023] [Accepted: 05/18/2023] [Indexed: 09/02/2023]
Abstract
Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the development of various sensors and imaging platforms for rapid and efficient quantitative measurement of plant traits has become the mainstream approach in plant phenotyping studies. Here, we reviewed the trends of image-based high-throughput phenotyping methods applied to horticultural crops. High-throughput phenotyping is carried out using various types of imaging platforms developed for indoor or field conditions. We highlighted the applications of different imaging platforms in the horticulture sector with their advantages and limitations. Furthermore, the principles and applications of commonly used imaging techniques, visible light (RGB) imaging, thermal imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging for high-throughput plant phenotyping, are discussed. High-throughput phenotyping has been widely used for phenotyping various horticultural traits, which can be morphological, physiological, biochemical, yield, biotic, and abiotic stress responses. Moreover, the ability of high-throughput phenotyping with the help of various optical sensors will lead to the discovery of new phenotypic traits which need to be explored in the future. We summarized the applications of image analysis for the quantitative evaluation of various traits with several examples of horticultural crops in the literature. Finally, we summarized the current trend of high-throughput phenotyping in horticultural crops and highlighted future perspectives.
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Affiliation(s)
| | | | | | | | - Jeongho Baek
- Department of Agricultural Biotechnology, National Institute of Agricultural Science, Rural Development Administration, Jeonju 54874, Republic of Korea
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Zhang M, Liu B, Fei Y, Yang X, Zhao L, Shi C, Zhang Y, Lu N, Wu C, Ma W, Wang J. Genetic architecture of leaf morphology revealed by integrated trait module in Catalpa bungei. HORTICULTURE RESEARCH 2023; 10:uhad032. [PMID: 37090097 PMCID: PMC10120837 DOI: 10.1093/hr/uhad032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 02/14/2023] [Indexed: 05/03/2023]
Abstract
Leaves are crucial for maintaining plant growth and development via photosynthesis, and their function is simultaneously regulated by a suite of phenotypic traits. Although much is known about the genetic architecture of individual leaf traits, unraveling the genetic basis of complex leaf morphology remains a challenge. Based on the functional correlation and coordination of multi-traits, we divided 15 leaf morphological traits into three modules, comprising size (area, length, width, and perimeter), shape (leaf lobes, aspect ratio, circularity, rectangularity, and the relevant ratios), and color (red, green, and blue) for an ornamental tree species, Catalpa bungei. A total of 189 significant single-nucleotide polymorphisms were identified in the leaves of C. bungei: 35, 82, and 76 in the size, shape, and color modules, respectively. Four quantitative trait loci were common between the size and shape modules, which were closely related according to phenotype correlation, genetic mapping, and mRNA analysis. The color module was independent of them. Synergistic changes in the aspect ratio, leaf lobe, and circularity suggest that these traits could be the core indicators of the leaf shape module. The LAS and SRK genes, associated with leaf lobe and circularity, were found to function in plant defense mechanisms and the growth of leaves. The associations between the SRK and CRK2 genes and the leaf lobe and circularity traits were further verified by RT-qPCR. Our findings demonstrate the importance of integrating multi-trait modules to characterize leaf morphology and facilitate a holistic understanding of the genetic architecture of intraspecific leaf morphology diversity.
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Affiliation(s)
| | | | - Yue Fei
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Xiaowei Yang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Linjiao Zhao
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Chaozhong Shi
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Yueying Zhang
- Academy of Forest and Grassland Inventory and Planning, National Forestry and Grassland Administration, Beijing 100714, China
| | - Nan Lu
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Chuangye Wu
- Wenxian Forestry Science Research Institute, Jiaozuo 454850, China
| | - Wenjun Ma
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
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Automatic non-destructive multiple lettuce traits prediction based on DeepLabV3 +. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01660-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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