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M A Genaev

Showing results (1-10 of 8) with videos related to

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Vavilovskii Zhurnal Genetiki I Selektsii|March 4, 2021
Analysis of color and texture characteristics of cereals on digital imagesE G Komyshev, M A Genaev, D A Afonnikov
Genetika|January 26, 2018
[Methods of high-throughput plant phenotyping for large-scale breeding and genetic experiments]D A Afonnikov, M A Genaev, A V Doroshkov, et al.
Vavilovskii Zhurnal Genetiki I Selektsii|April 13, 2026
Wheat spikelet detection on RGB images using deep machine learningM A Genaev, I D Busov, Yu V Kruchinina, et al.
Vavilovskii Zhurnal Genetiki I Selektsii|October 23, 2024
A new leaf pubescence gene, Hl1th , introgressed into bread wheat from Thinopyrum ponticum and its phenotypic manifestation under homoeologous chromosomal substitutionsA V Simonov, E I Gordeeva, M A Genaev, et al.
Vavilovskii Zhurnal Genetiki I Selektsii|June 8, 2026
Genetic analysis of wheat ear architecture in F2 hybrid of tetraploid wheats Triticum aethiopicum and T. carthlicum and its computer phenotypingYu V Kruchinina, E G Komyshev, M A Genaev, et al.
Vavilovskii Zhurnal Genetiki I Selektsii|July 23, 2024
A pipeline for processing hyperspectral images, with a case of melanin-containing barley grains as an exampleI D Busov, M A Genaev, E G Komyshev, et al.
Vavilovskii Zhurnal Genetiki I Selektsii|March 19, 2024
Determination of the melanin and anthocyanin content in barley grains by digital image analysis using machine learning methodsE G Komyshev, M A Genaev, I D Busov, et al.
Vavilovskii Zhurnal Genetiki I Selektsii|December 13, 2021
Automatic morphology phenotyping of tetra- and hexaploid wheat spike using computer vision methodsA Yu Pronozin, A A Paulish, E A Zavarzin, et al.
Pageof 1

Showing results (1-10 of 8) with videos related to

Sort By:
Pageof 1
Vavilovskii Zhurnal Genetiki I Selektsii|March 4, 2021
Analysis of color and texture characteristics of cereals on digital imagesE G Komyshev, M A Genaev, D A Afonnikov
Genetika|January 26, 2018
[Methods of high-throughput plant phenotyping for large-scale breeding and genetic experiments]D A Afonnikov, M A Genaev, A V Doroshkov, et al.
Vavilovskii Zhurnal Genetiki I Selektsii|April 13, 2026
Wheat spikelet detection on RGB images using deep machine learningM A Genaev, I D Busov, Yu V Kruchinina, et al.
Vavilovskii Zhurnal Genetiki I Selektsii|October 23, 2024
A new leaf pubescence gene, Hl1th , introgressed into bread wheat from Thinopyrum ponticum and its phenotypic manifestation under homoeologous chromosomal substitutionsA V Simonov, E I Gordeeva, M A Genaev, et al.
Vavilovskii Zhurnal Genetiki I Selektsii|June 8, 2026
Genetic analysis of wheat ear architecture in F2 hybrid of tetraploid wheats Triticum aethiopicum and T. carthlicum and its computer phenotypingYu V Kruchinina, E G Komyshev, M A Genaev, et al.
Vavilovskii Zhurnal Genetiki I Selektsii|July 23, 2024
A pipeline for processing hyperspectral images, with a case of melanin-containing barley grains as an exampleI D Busov, M A Genaev, E G Komyshev, et al.
Vavilovskii Zhurnal Genetiki I Selektsii|March 19, 2024
Determination of the melanin and anthocyanin content in barley grains by digital image analysis using machine learning methodsE G Komyshev, M A Genaev, I D Busov, et al.
Vavilovskii Zhurnal Genetiki I Selektsii|December 13, 2021
Automatic morphology phenotyping of tetra- and hexaploid wheat spike using computer vision methodsA Yu Pronozin, A A Paulish, E A Zavarzin, et al.
Pageof 1