vavilovВавиловский журнал генетики и селекцииVavilov Journal of Genetics and Breeding2500-3259Institute of Cytology and Genetics of Siberian Branch of the RAS10.18699/vjgb-26-37vavilov-5050Research ArticleЦИФРОВОЕ ФЕНОТИПИРОВАНИЕDIGITAL PHENOTYPINGОписание морфологических характеристик колосьев пшеницы в базе данных SpikeDroidDB в виде цифрового паспортаDescription of morphological characteristics of wheat spike as a digital certificate in the SpikeDroidDB databaseКомышевЕ. Г.KomyshevE. G.

Новосибирск

Novosibirsk

КручининаЮ. В.KruchininaYu. V.

Новосибирск

Novosibirsk

КовальВ. С.KovalV. S.

Новосибирск

Novosibirsk

ПотешкинаА. А.PoteshkinaA. A.

Новосибирск;

р. п. Краснообск, Новосибирская область

Novosibirsk;

Krasnoobsk, Novosibirsk region

ПетрашН. В.PetrashN. V.

Новосибирск;

р. п. Краснообск, Новосибирская область

Novosibirsk;

Krasnoobsk, Novosibirsk region

ПискаревВ. В.PiskarevV. V.

Новосибирск;

р. п. Краснообск, Новосибирская область

Novosibirsk;

Krasnoobsk, Novosibirsk region

ГончаровН. П.GoncharovN. P.

Новосибирск

Novosibirsk

АфонниковД. А.AfonnikovD. A.

Новосибирск

Novosibirsk

Федеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наук; Курчатовский геномный центр ИЦиГ СО РАНРоссияInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Kurchatov Genomic Center of ICG SB RASRussian FederationФедеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наук; Сибирский научно-исследовательский институт растениеводства и селекции – филиал Федерального исследовательского центра Институт цитологии и генетики Сибирского отделения Российской академии наукРоссияInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Siberian Research Institute of Plant Production and Breeding – Branch of the Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of SciencesRussian FederationФедеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наукРоссияInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Kurchatov Genomic Center of ICG SB RASRussian Federation202606042026302330338Copyright © Комышев Е.Г., Кручинина Ю.В., Коваль В.С., Потешкина А.А., Петраш Н.В., Пискарев В.В., Гончаров Н.П., Афонников Д.А., 20262026Комышев Е.Г., Кручинина Ю.В., Коваль В.С., Потешкина А.А., Петраш Н.В., Пискарев В.В., Гончаров Н.П., Афонников Д.А.Komyshev E.G., Kruchinina Y.V., Koval V.S., Poteshkina A.A., Petrash N.V., Piskarev V.V., Goncharov N.P., Afonnikov D.A.This work is licensed under a Creative Commons Attribution 4.0 License.https://vavilov.elpub.ru/jour/article/view/5050

При анализе структуры урожая пшеницы неоднократно показано, что его выраженность зависит от продуктивности колоса. Основные характеристики колоса, которые связаны с продуктивностью, это прежде всего масса 1000 зерен, число зерен и колосков в колосе, его размеры, наличие/отсутствие остей и другие. В современных генетических исследованиях для идентификации локусов, контролирующих признаки продуктивности колоса, требуется морфометрия сотен и тысяч колосьев. С другой стороны, современные коллекции генетических ресурсов содержат тысячи образцов, которые также требуют своего детального описания. Все это обусловливает необходимость развития цифровых технологий описания признака колоса пшеницы, которые могут быть достигнуты на основе методов анализа изображений. Эти методы позволяют автоматически получать значения набора признаков, которые могут служить основой формирования цифровых коллекций растений. В настоящей работе предложено цифровое описание колоса пшеницы на основе характеристик, полученных как вручную, так и на основе анализа цифровых изображений, а также характеристик растений, у которых был взят колос. Эти данные положены в основу обновленной версии базы данных SpikeDroidDB (http://spikedroid.biores.cytogen.ru/). Цифровое описание колоса состоит из двух блоков. Блок загружаемых данных содержит описание растения и включает 5  таблиц: коллекция, сортообразец (год выращивания (вегетация), посевной номер, таксономическую информацию и др.), место выращивания, характеристики колоса растений, оцененные экспертом вручную (длину, ширину фронтальной и боковой проекций, тип и цвет колоса и др.). Блок извлекаемых характеристик включает признаки колоса, полученные в результате цифрового фенотипирования, и состоит из шести таблиц: характеристики контура колоса на изображении; характеристики модели четырехугольников, компоненты цвета колоса, доминантные цвета колоса, текстурные характеристики колоса на изображении. Было проведено выделение наиболее наглядных и информативных характеристик колоса, которые позволили сформировать цифровой паспорт колоса, включающий признаки размера, формы и цвета, определенные на основе анализа цифровых изображений. Проведено сравнение признаков, формирующих цифровой паспорт, у двух видов пшеницы, T. aethiopicum и T. carthlicum. Показано, что признаки цифрового паспорта позволяют наглядно представить модель колоса, а также выявить достоверно различающиеся параметры (цвет колоса и остей, округлость фронтальной проекции колоса). В интерфейс базы данных также добавлена возможность пакетной загрузки данных о характеристиках растения и колоса, а также их изображений.

It has been repeatedly shown that spike productivity is the main component of wheat yield. The main spike parameters related to productivity are size, the number of grains and spikelets per spike, and the presence or absence of awns. In modern genetic research, morphometric analysis of hundreds and thousands of spikes is required to determine the loci that control spike productivity traits. On the other hand, thousands of accessions in modern collections of wheat genetic resources need detailed description. These considerations motivate the development of digital technologies for describing spike traits in wheat, which can be achieved through image analysis methods. These methods allow for automated acquisition of trait values that can serve as the basis for digital plant collections. Here we propose an extended set of spike characteristics obtained both manually and through digital image analysis and present plant characterization. These data form the basis of the updated version of the SpikeDroidDB database (http://spikedroid.biores.cytogen.ru/). The digital description of the spike consists of two blocks. The block of uploaded data includes a description of the plant and contains five tables: collection; variety sample (year of cultivation (vegetation), sowing identifier, taxonomic information, etc.), planting site, and characteristics of the spike determined manually (length, width of frontal and lateral views, type and color of the spike, etc.) The block of extracted features includes spike characteristics obtained by digital phenotyping and contains six tables: characteristics of the spike outline in the image; characteristics of the quadrangle model, values of the color components of the spike, dominant colors of the spike, and texture characteristics of the spike in the image. The most illustrative and significant features of the spike have been identified, allowing for the formation of the spike digital certificate, which includes size, shape, and color features derived from the digital images. The features forming the digital certificate have been compared between two wheat species, T. aethiopicum and T. carthlicum. It is shown that the features of the digital certificate allow for a clear representation of the spike model and the identification of distinct parameters: colors of the spike and awns and roundness of the frontal view of the spike. The database interface has been supplemented with the ability to upload data on plant and spike characteristics, as well as their images, in the batch mode.

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The authors declare that there are no conflicts of interest present.