vavilovВавиловский журнал генетики и селекцииVavilov Journal of Genetics and Breeding2500-3259Institute of Cytology and Genetics of Siberian Branch of the RAS10.18699/vjgb-24-50vavilov-4187Research ArticleКОМПЬЮТЕРНАЯ БИОЛОГИЯ РАСТЕНИЙCOMPUTATIONAL PLANT BIOLOGYКонвейер обработки гиперспектральных изображений на примере исследования зерен ячменя, содержащих меланинA pipeline for processing hyperspectral images, with a case of melanin-containing barley grains as an exampleБусовИ. Д.BusovI. D.

Новосибирск

Novosibirsk

i.busov@g.nsu.ruГенаевМ. А.GenaevM. A.

Новосибирск

Novosibirsk

КомышевЕ. Г.KomyshevE. G.

Новосибирск

Novosibirsk

КовальВ. С.KovalV. S.

Новосибирск

Novosibirsk

ЗыковаТ. E.ZykovaT. E.

Новосибирск

Novosibirsk

ГлаголеваА. Ю.GlagolevaA. Y.

Новосибирск

Novosibirsk

https://orcid.org/0000-0001-9738-1409АфонниковД. А.AfonnikovD. A.

Новосибирск

Novosibirsk

Федеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наук ; Новосибирский государственный университетРоссияInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences ; Novosibirsk State UniversityRussian 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 SciencesRussian Federation202411072024284443455Copyright © Бусов И.Д., Генаев М.А., Комышев Е.Г., Коваль В.С., Зыкова Т.E., Глаголева А.Ю., Афонников Д.А., 20242024Бусов И.Д., Генаев М.А., Комышев Е.Г., Коваль В.С., Зыкова Т.E., Глаголева А.Ю., Афонников Д.А.Busov I.D., Genaev M.A., Komyshev E.G., Koval V.S., Zykova T.E., Glagoleva A.Y., Afonnikov D.A.This work is licensed under a Creative Commons Attribution 4.0 License.https://vavilov.elpub.ru/jour/article/view/4187

Анализ гиперспектральных изображений представляет большой интерес при изучении растений. В настоящее время такой анализ используется все более широко, поэтому создание методов обработки гиперспектральных изображений является актуальной задачей. В статье представлен конвейер для работы с гиперспектральными изображениями, который включает: предварительную обработку, базовый статистический анализ, визуализацию многоканального гиперспектрального изображения, а также решение задач классификации и кластеризации с применением методов машинного обучения. В текущей версии пакета программ реализованы следующие методы: построение доверительного интервала произвольного уровня для разницы выборочных средних; проверка сходства распределений интенсивности линий спектра для двух наборов гиперспектральных изображений на основе U-критерия Манна–Уитни и критерия согласия Пирсона; визуализация в двухмерном пространстве с применением методов понижения размерности PCA, ISOMAP и UMAP; классификация с использованием линейной или гребневой регрессии, случайного леса и градиентного бустинга; кластеризация образцов с помощью EM-алгоритма. Программный конвейер реализован на языке Python с использованием библиотек Pandas, NumPy, OpenCV, SciPy, Sklearn, Umap, CatBoost и Plotly. Исходный код доступен по адресу: https://github.com/igor2704/Hyperspectral_images. Данный конвейер был применен для идентификации пигмента меланина в оболочке зерен ячменя на базе гиперспектральных данных. Визуализация на основе методов PCA, UMAP и ISOMAP, а также использование алгоритмов кластеризации показали, что на базе гиперспектральных данных с высокой точностью можно провести линейное разделение образцов зерен с пигментацией и без нее. Анализ выявил статистически значимые различия в распределении медиан интенсивности для выборок изображений зерен с пигментом и без него. Таким образом, продемонстрировано, что с помощью гиперспектральных изображений с большой точностью можно определить наличие или отсутствие меланина в зернах ячменя. Созданный в данной работе гибкий и удобный инструмент позволит существенно повысить эффективность анализа гиперспектральных изображений.

Analysis of hyperspectral images is of great interest in plant studies. Nowadays, this analysis is used more and more widely, so the development of hyperspectral image processing methods is an urgent task. This paper presents a hyperspectral image processing pipeline that includes: preprocessing, basic statistical analysis, visualization of a multichannel hyperspectral image, and solving classification and clustering problems using machine learning methods. The current version of the package implements the following methods: construction of a confidence interval of an arbitrary level for the difference of sample averages; verification of the similarity of intensity distributions of spectral lines for two sets of hyperspectral images on the basis of the Mann–Whitney U-criterion and Pearson’s criterion of agreement; visualization in two-dimensional space using dimensionality reduction methods PCA, ISOMAP and UMAP; classification using linear or ridge regression, random forest and catboost; clustering of samples using the EM-algorithm. The software pipeline is implemented in Python using the Pandas, NumPy, OpenCV, SciPy, Sklearn, Umap, CatBoost and Plotly libraries. The source code is available at: https://github.com/igor2704/Hyperspectral_images. The pipeline was applied to identify melanin pigment in the shell of barley grains based on hyperspectral data. Visualization based on PCA, UMAP and ISOMAP methods, as well as the use of clustering algorithms, showed that a linear separation of grain samples with and without pigmentation could be performed with high accuracy based on hyperspectral data. The analysis revealed statistically significant differences in the distribution of median intensities for samples of images of grains with and without pigmentation. Thus, it was demonstrated that hyperspectral images can be used to determine the presence or absence of melanin in barley grains with great accuracy. The flexible and convenient tool created in this work will significantly increase the efficiency of hyperspectral image analysis.

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