Study of the distribution of phenotypic characteristics of sunflower seeds in a head of different genotypes
Elchyn Aliyev
, Katerina Vedmedeva
, Tatiana Machova
, Stanislav Vedmedev
Abstract: Nowadays, sunflower breeding needs new approaches and methods. Using computer-aided image analysis techniques combined with other data on the phenotyping subject creates a sound basis for selection. Selection work on the size and weight of sunflower seeds causes many complications related to the variety of seeds, even in one head. To solve this problem, field experiments, mathematical modelling, and computer processing of photographs were involved. As a result of combining the results of actual measurement and evaluation of photographic images, a method of measurement and determination of patterns of distribution of phenotypic characteristics of sunflower seeds in a head was developed. The methodology includes a developed mathematical model of the location of sunflower seeds in the head and a methodology for determining the geometric dimensions of the seeds from the image. The distribution patterns of geometric (length L, width W, thickness T) and mass (seed mass Ms, seed kernel mass Mk) parameters of seeds in the head were studied. The variability of the seed phenotype in the head was established depending on its location based on the material of four lines and the sunflower variety. A complex index of phenotypic characteristics of sunflower seeds I is introduced, defined as the product of the ranks of individual phenotypic parameters of seeds (L, W, T, Ms, Mk). A general pattern was determined, like three tiers with different phenotypic characteristics in each head. The possibilities of visualizing the phenotype of seeds by their location in the head have been revealed.
Keywords: complex index; distribution pattern; location in the head; phenotypic characteristics; seed weight; sunflower seeds
Citation: Aliyev, E., Vedmedeva, K., Machova, T., & Vedmedev, S. (2024). Study of the distribution of phenotypic characteristics of sunflower seeds in a head of different genotypes. Bulgarian Journal of Crop Science, 61(4), 73-89.
References: (click to open/close) | Abràmoff, M. D., Magalhães, P. J., & Ram, S. J. (2004). Image processing with ImageJ. Biophotonics Internat, 11 (7): 36–42. Afonnikov, D. A., Genayev, M. A., Doroshkov, A. V., Komyshev, Ye. G., & Pshenichnikova, T. A. (2016). Methods for high-throughput plant phenotyping for mass breeding and genetic experiments. Genetika, 52 (7): 788–803. (Ru) Aliyev, E. B. (2022). Automatic phenotyping of seed pod material of sunflower: monograph. Kyyiv: Ahrarna nauka. 104-s. ISBN 978-966-540-540-5 (Ukr). Aliyev, E. B., & Vedmedyeva, K. V. (2023). Mathematical model of placement of sunflower seeds in a head. Naukovo-tekhnichnyy byuletenʹ Instytutu oliynykh kulʹtur NAAN, 34: 15–23. DOI: 10.36710/IOC-2023-34-02 (Ukr). Allen, R. D., Trelease, R. N., & Thomas, T. L. (1988). Regulation of isocitrate lyase gene expression in sunflower. Plant Physiol. 86(2): 527-532. DOI: 10.1104/pp.86.2.527 Arakelyan, G. (2014). Mathematics and the history of the golden section. Logos, 404 p. ISBN 978-5-98704-663-0. Barrio-Conde, M., Zanella, M. A., Aguiar-Perez, J. M., Ruiz-Gonzalez, R., & Gomez-Gil, J. A. (2023). Deep Learning Image System for Classifying High Oleic Sunflower Seed Varieties. Sensors, 23, 2471. DOI: 10.3390/s23052471 Borda, A., & Bowen, J. P. (2020). Turing's Sunflowers: Public research and the role of museums. Published by BCS Learning and Development Ltd. Proceedings of EVA London. 8 p. DOI: 10.31235/osf.io/mvjhf Bradski, G., & Kaehler, A. (2008). Learning OpenCV: Computer vision with the OpenCV library. P.: O’Reilly Media, Inc. 543 p. Burenko, K. S., Vedmedeva, K. V., & Pershyn, A. F. (2012) He studied sunflower collections for the constituents of the sign of large-fruitedness. Naukovo-tekhnichnyy byulletenʹ Instytutu oliynykh kulʹtur NAAN, 17: 42–47. http://bulletin.imk.zp.ua/pdf/2012/17/Burenko_17.pdf (Ukr). Cast, C. S., Lobet, G., Cabrera-Bosquet, L., Couvreur, V., Pradal, C., Tardieu, F. & Draye, X. (2022). Connecting plant phenotyping and modelling communities: lessons from science mapping and operational perspectives. In silico Plants, 4 (1): 1–13 DOI: 10.1093/insilicoplants/diac005 Cvejić, S., Jocić, S., Mladenov, V., Banjac, B., Radeka, I., Jocković, M., & Miklič, V. (2019). Selection of sunflower hybrids based on stability across environments. Genetika, 51(1), 81–92. DOI: 10.2298/GENSR1901081C Daviet, B., Fernandez, R., Cabrera-Bosquet, L., Pradal, C., & Fournier, C. (2022). PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time. Plant Methods. 18, 130. DOI: 10.1186/s13007-022-00961-4 Hladni, N., & Miladinović, D. (2019). Confectionery sunflower breeding and supply chain in Eastern Europe. OCL, 26, 29: 1–9. DOI: 10.1051/ocl/2019019 Hladni, N., Škorić, D., Kraljević-Balalić, M., Sakač, Z. & Jovanović, D. (2006) Combining ability for oil content and its correlations with other yield components in sunflower (Helianthus annuus L). HELIA, 29 (44), 101–110. DOI: 10.2298/ hel0644101h International Plant Phenotyping Network – IPPN (2024) https://www.plant-phenotyping.org/ Copyright IPPN Korkodola, M. M., & Maklyak, K. M. (2021). The effectiveness of the applied elements of the technology of growing sunflower for the confectionery direction of use. Naukovo-tekhnichnyy byuletenʹ Instytutu oliynykh kulʹtur NAAN, 31: 88-97. DOI: 10.36710/ioc-2021-31-08 (Ukr). Lazar, D., Niu, Y., & Nedbal, L. (2022). Insights on the regulation of photosynthesis in pea leaves exposed to oscillating light. Journal of Experimental Botany, 73 (18): 6380–6393. DOI: 10.1093/jxb/erac283 Lei, T., & Sun, D.W. (2023). Introducing the THz time domain CT system for evaluating kernel weight and plumpness of sunflower seed. Journal of Food Measurement and Characterization, 17: 3616–3624. DOI: 10.1007/s11694-023-01882-z Li, L., Zhang, Q., & Huang, D. (2014). A review of imaging techniques for plant phenotyping. Sensors (Basel), 14 (11): 20078-111. DOI: 10.3390/s141120078 Nosalʹ, O. O., Vedmedyeva, K. V., & Shkolova, S. V. (2017). Donor properties of the sunflower line based on large fertility. Naukovo-tekhnichnyy byuletenʹ Instytutu oliynykh kulʹtur NAAN, 24: 110–121. http://bulletin.imk.zp.ua/pdf/2017/24/Nosal_24.pdf (Ukr). Pérez-Vich, B., Aguirre, M. R., Guta, B., Fernández-Martínez, J. M., & Velasco, L. (2018). Genetic diversity of a germplasm collection of confectionery sunflower landraces from Spain. Crop Science, 58 (5): 1972–1981. DOI: 10.2135/cropsci2018.02.0108 Phillips, R. L. (2010). Mobilizing science to break yield barriers. Crop Sci, 50, 99–108. DOI: 10.2135/cropsci2009.09.0525 Polyakov, O. I., Boyko, K. A., & Novoshynsʹka, N. O. (2011). The yield of conditioned seeds of parental forms of steppe hybrid sunflower depending on the sowing scheme. Naukovo-tekhnichnyy byuletenʹ Instytutu oliynykh kulʹtur NAAN, 16: 87–90. http://bulletin.imk.zp.ua/pdf/2011/16/ Poliakov_16.pdf (Ukr). Polyakov, O. I., Nikitenko, O. V., & Soroka, A. I. (2022). Productivity of sunflower hybrids depending on the density of plant standing at different sowing times. Naukovo-tekhnichnyy byuletenʹ Instytutu oliynykh kulʹtur NAAN, 32: 99–111. DOI: 10.36710/IOC-2022-32-10 (Ukr). Schurr, U. (2015). Phenotyping, bioeconomy ... and beyond. Addressing grand challenges by integrated approaches. Presentation. Forschungszentrum Jülich, Germany, 63 p. Swinton, J. (2004). Watching the Daisies Grow: Turing and Fibonacci Phyllotaxis. Alan Turing: Life and Legacy of a Great Thinker. 477–498. DOI: 10.1007/978-3-662-05642-4_20. Swinton, J., & Ochu, E. (2016). The MSI Turing’s Sunflower Consortium. Novel Fibonacci and non-Fibonacci structure in the sunflower: results of a citizen science experiment. R. Soc. open sci., 3: 160091. DOI: 10.1098/rsos.160091 Tigay, K. I., & Tereshchenko, G. A. (2017). Dependence of economically valuable traits of confectionery sunflower seeds on plant density. Politematicheskiy setevoy elektronnyy nauchnyy zhurnal Kubanskogo gosudarstvennogo agrarnogo universiteta, 128: 1052–1060 (Ru). Tishkov, N. M., & Borodin S. G. (2009). Productivity of confectionery sunflower varieties depending on plant density. Maslichnyye kul'tury: Nauch.-tekhn. byull. VNIIMK, 1 (140): 57–64 (Ru). Vasil'yeva, T., Boyko, Y. U., Khatit, A., & Ilyuk, G. (2012). Dependence of the size of sunflower achenes on competition between them within the head. Maslichnyye kul'tury, 1(150): 34–41 (Ru). Vedmedeva, K. V., & Nosal, O. O. (2018). Inheritance of the trait of large-fruited sunflower seeds (Hellianthus annuus l.) in the combination of crossing L12B x KP11B. Naukovo-tekhnichnyy byulletenʹ Instytutu oliynykh kulʹtur NAAN, 26: 21–29, DOI: 10.36710/ioc-2018-26-03 (Ukr). Vedmedeva, K. V., & Nosalʹ, O. O. (2020). Evaluation of large-fruited sunflower lines by kilʹkisnymy characteristics of morphological features.Naukovo-tekhnichnyy byuletenʹ Instytutu oliynykh kulʹtur NAAN, 29: 46–55. DOI: 10.36710/ioc-2020-29-05 (Ukr). Vedmedeva, K., Nosal, O., Poliakova, I. & Machova, T. (2023) Correlations of confectionary seed traits in different head zones sunflower. Helia, 46(79): 215-231. https://doi.org/10.1515/helia-2023-0012. Vedmedyev, S. R., & Tereshchenko, E. V. (2022). Creating a digital model of a sunflower. Modern problems and achievements in the field of radio engineering, telecommunications and information technologies: Tezy dopovidey KHI Mizhnarodnoyi naukovo-praktychnoyi konferentsiyi (12-14 hrudnya 2022 r., m. Zaporizhzhya). [Elektronnyy resurs] / Elektron. dani. –Zaporizhzhya: NU «Zaporizʹka politekhnika». 136–137. (Ukr). Vedmedyeva, K. V., Makhova, T. V., & Yakubenko, O. V. (2022). Justification of individual identification features of sunflower according to UPOV and VOS description methods. Naukovo-tekhnichnyy byuletenʹ Instytutu oliynykh kulʹtur NAAN., 33, 6–18. DOI: 10.36710/ioc-2022-33-01 (Ukr). Yang, S., Zheng, L., He, P., Wu, T., Sun, S. & Wang, M. (2021). High-throughput soybean seeds phenotyping with convolutional neural networks and transfer learning. Plant Methods 17, 50. DOI: 10.1186/s13007-021-00749-y. Yue, B., Vick, B. A., Cai, X. & Hu, J. (2010). Genetic mapping for the Rf1 (fertility restoration) gene in sunflower (Helianthus annuus L.) by SSR and TRAP markers. Plant Breeding, 129 (1), 24–28. DOI: 10.1111/j.1439-0523.2009.01661.x
|
|
| Date published: 2024-08-29
Download full text