Geometric parameters of wheat grain determined by image analysis and FEM approach

Firatligil-Durmus, Ebru; Sýkorová, A.; Šárka, Evžen; Bubník, Z.; Schejbal, M.; Prihoda, J.

doi:10.1556/crc.38.2010.1.13

Cited by 7 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accordingly, wheat grain has been well-explored visually in the last decades, either using uncomplicated methods and 2D indices (Braadbaart & van Bergen., 2005;Firatligil-Durmuş et al, 2010;Gegas et al, 2010;Zapotoczny, 2011;Williams et al, 2013;and Whan et al, 2014) or employing more complex techniques of 3D reconstruction (Mabille & Abecassis, 2003;Strange et al, 2015;and Le et al, 2019). The techniques utilized for this purpose can be categorized under the term of high-throughput phenotyping (HTP), which has been emerged as an efficient paradigm in response to the need for keeping the feasibility of investigations in the current complex and largescale breeding programs.…”

Section: Introductionmentioning

confidence: 99%

Wheat grain width: A clue for re-exploring visual indicators of grain weight

Haghshenas

Emam

Jafarizadeh

2021

Preprint

View full text Add to dashboard Cite

Mean grain weight (MGW) is among the most frequently measured parameters in wheat breeding and physiology. Although in the recent decades, various wheat grain analyses (e.g. counting, and determining the size, color, or shape features) have been facilitated thanks to the automated image processing systems, MGW estimations has been limited to using few number of image-derived indices; i.e. mainly the linear or power models developed based on the projected area (Area). Following a preliminary observation which indicated the potential of grain width in improving the predictions, the present study was conducted to explore potentially more efficient indices for increasing the precision of image-based MGW estimations. For this purpose, an image archive of the grains was processed, which was harvested from a two-year field experiment carried out with 3 replicates under two irrigation conditions and included 15 cultivar mixture treatments (so the archive was consisted of 180 images taken from an overall number of more than 72000 grains). It was observed that among the more than 30 evaluated indices of grain size and shape, indicators of grain width (i.e. Minor & MinFeret) along with 8 other empirical indices had a higher correlation with MGW, compared with Area. The most precise MGW predictions were obtained using the Area*Circularity, Perimeter*Circularity, and Area/Perimeter indices. In general, two main common factors were detected in the structure of the major indices, i.e. either grain width or the Area/Perimeter ratio. Moreover, comparative efficiency of the superior indices almost remained stable across the 4 environmental conditions. Eventually, using the selected indices, ten simple linear models were developed and validated for MGW prediction, which indicated a relatively higher precision than the current Area-based models. The considerable effect of enhancing image resolution on the precision of the models has been also evidenced. It is expected that the findings of the present study improve the precision of the image-based MGW estimations, and consequently facilitate wheat breeding and physiological assessments.

show abstract