Weight of individual wheat grains estimated from high-throughput digital images of grain area

Kim, Jinwook; Savin, Roxana; Slafer, Gustavo A.

doi:10.1016/j.eja.2021.126237

Cited by 8 publications

(7 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, considering that each of the 180 samples was consisted of more than 400 grains, it is expected that both types of estimations (MGW and individual grain weight) should be highly correlated. As an evidence for this fact, it was observed that similar to the study of Kim et al (2021), Kim index provided a more precise grain weight estimation than Area. More importantly, slopes of the corresponding linear models calculated in both studies were almost similar (see Table 3); despite the differences in the genotypes, treatments, imaging systems, lighting, and probably the image processing algorithms: Besides the technical advantageous for developing phenotyping platforms, findings of the present study might also be readily used in wheat physiology and breeding approaches.…”

Section: Cross-validation Indicessupporting

confidence: 54%

“…Kim index (i.e. Area 1.32 ) was derived from the study of Kim et al, 2021 Analysis of variance (ANOVA; data not shown) also indicated that the effects of year, mixture treatments, and water stress were very significant on the values of MGW, as well as the two control and 10 selected indices (the total 12 evaluated indices; P<0.0001). As it was expected according to the high correlations between MGW and the image-derived indices, the variation of the indices followed completely the changes in MGW; i.e.…”

Section: Resultsmentioning

confidence: 99%

“…Linear correlations of MGW with the visual indices were compared with those of the two control criteria i.e. 𝐴𝑟𝑒𝑎 and Kim index (𝐴𝑟𝑒𝑎 1.32 ; taken from the paper of Kim et al, 2021), and the indices with a higher correlations than the controls were selected as the final indicators of MGW. Using each selected index, a linear model for prediction of MGW was developed and evaluated.…”

Section: Image Processingmentioning

confidence: 99%

“…Accordingly, studying the relationship between the area and weight of individual grains, Kim et al (2021) introduced a single power model equation for estimating wheat grain weight, (i.e. 𝑤𝑒𝑖𝑔ℎ𝑡 = 𝑎𝑟𝑒𝑎 1.32 ), which provided a higher precision compared with the linear model.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Section: Cross-validation Indicessupporting

confidence: 54%

Section: Resultsmentioning

confidence: 99%

Section: Image Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

show abstract

“…grain width or length). Accordingly, studying the relationship between the area and weight of individual grains, Kim et al [ 20 ] introduced a single power model equation for estimating wheat grain weight, (i.e. ), which provided a higher precision compared with the linear model.…”

Section: Introductionmentioning

confidence: 99%

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

2022

View full text Add to dashboard Cite

Background 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 have 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 more efficient indices for increasing the precision of image-based MGW estimations. For this purpose, an image archive of the grains was processed, which were harvested from a 2-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 including more than 72,000 grains). Results 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. Furthermore, it was found that (i) grain width and the Area/Perimeter ratio were the common factors in the structure of the superior predictive indices; and (ii) the superior indices had the highest correlation with grain width, rather than with their mathematical components. 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. Conclusions It is expected that the findings of the present study, along with the simple predictive linear models developed and validated using new image-derived indices, could improve the precision of the image-based MGW estimations, and consequently facilitate wheat breeding and physiological assessments.

show abstract