Triticale field phenotyping using RGB camera for ear counting
and yield estimation

J 2024

Triticale field phenotyping using RGB camera for ear counting and yield estimation

STEFAŃSKI, Piotr, Sajid ULLAH, Przemysław MATYSIK and Krystyna RYBKA

Basic information

Original name

Triticale field phenotyping using RGB camera for ear counting and yield estimation

Authors

STEFAŃSKI, Piotr, Sajid ULLAH (586 Pakistan, belonging to the institution), Przemysław MATYSIK and Krystyna RYBKA

Edition

Journal of Applied Genetics, Springer, 2024, 1234-1983

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10611 Plant sciences, botany

Country of publisher

Germany

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 2.400 in 2022

Organization unit

Faculty of Science

DOI

http://dx.doi.org/10.1007/s13353-024-00835-6

UT WoS

001161689400002

Keywords in English

plant breeding; yield potential; ear detection; deep learning; field imaging; statistical analysis

Abstract

V originále

Triticale (X Triticosecale Wittmack), a wheat-rye small grain crop hybrid, combines wheat and rye attributes in one hexaploid genome. It is characterized by high adaptability to adverse environmental conditions: drought, soil acidity, salinity and heavy metal ions, poorer soil quality, and waterlogging. So that its cultivation is prospective in a changing climate. Here, we describe RGB on-ground phenotyping of field-grown eighteen triticale market-available cultivars, made in naturally changing light conditions, in two consecutive winter cereals growing seasons: 2018–2019 and 2019–2020. The number of ears was counted on top-down images with an accuracy of 95% and mean average precision (mAP) of 0.71 using advanced object detection algorithm YOLOv4, with ensemble modeling of field imaging captured in two different illumination conditions. A correlation between the number of ears and yield was achieved at the statistical importance of 0.16 for data from 2019. Results are discussed from the perspective of modern breeding and phenotyping bottleneck.

Links

EF16_026/0008446, research and development project

Name: Integrace signálu a epigenetické reprogramování pro produktivitu rostlin

Citovat

STEFAŃSKI, Piotr, Sajid ULLAH, Przemysław MATYSIK and Krystyna RYBKA. Triticale field phenotyping using RGB camera for ear counting and yield estimation. Journal of Applied Genetics. Springer, 2024, vol. 65, No 2, p. 271-281. ISSN 1234-1983. Available from: https://dx.doi.org/10.1007/s13353-024-00835-6.

@article{2363177,
   author = {Stefański, Piotr and Ullah, Sajid and Matysik, Przemysław and Rybka, Krystyna and Rybka, Krystyna},
   article_number = {2},
   doi = {http://dx.doi.org/10.1007/s13353-024-00835-6},
   keywords = {plant breeding; yield potential; ear detection; deep learning; field imaging; statistical analysis},
   language = {eng},
   issn = {1234-1983},
   journal = {Journal of Applied Genetics},
   title = {Triticale field phenotyping using RGB camera for ear counting and yield estimation},
   url = {https://link.springer.com/article/10.1007/s13353-024-00835-6},
   volume = {65},
   year = {2024}
}

TY  - JOUR
ID  - 2363177
AU  - Stefański, Piotr - Ullah, Sajid - Matysik, Przemysław - Rybka, Krystyna - Rybka, Krystyna
PY  - 2024
TI  - Triticale field phenotyping using RGB camera for ear counting and yield estimation
JF  - Journal of Applied Genetics
VL  - 65
IS  - 2
SP  - 271-281
EP  - 271-281
PB  - Springer
SN  - 12341983
KW  - plant breeding
KW  - yield potential
KW  - ear detection
KW  - deep learning
KW  - field imaging
KW  - statistical analysis
UR  - https://link.springer.com/article/10.1007/s13353-024-00835-6
N2  - Triticale (X Triticosecale Wittmack), a wheat-rye small grain crop hybrid, combines wheat and rye attributes in one hexaploid genome. It is characterized by high adaptability to adverse environmental conditions: drought, soil acidity, salinity and heavy metal ions, poorer soil quality, and waterlogging. So that its cultivation is prospective in a changing climate. Here, we describe RGB on-ground phenotyping of field-grown eighteen triticale market-available cultivars, made in naturally changing light conditions, in two consecutive winter cereals growing seasons: 2018–2019 and 2019–2020. The number of ears was counted on top-down images with an accuracy of 95% and mean average precision (mAP) of 0.71 using advanced object detection algorithm YOLOv4, with ensemble modeling of field imaging captured in two different illumination conditions. A correlation between the number of ears and yield was achieved at the statistical importance of 0.16 for data from 2019. Results are discussed from the perspective of modern breeding and phenotyping bottleneck.
ER  -

STEFA$\backslash$'NSKI, Piotr, Sajid ULLAH, Przemysław MATYSIK and Krystyna RYBKA. Triticale field phenotyping using RGB camera for ear counting and yield estimation. \textit{Journal of Applied Genetics}. Springer, 2024, vol.~65, No~2, p.~271-281. ISSN~1234-1983. Available from: https://dx.doi.org/10.1007/s13353-024-00835-6.

Detailed Information on Publication Record