Matches in SemOpenAlex for { <https://semopenalex.org/work/W3207003666> ?p ?o ?g. }
Showing items 1 to 62 of
62
with 100 items per page.
- W3207003666 abstract "Abstract 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 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). 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. 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. Conclusions It is expected that the findings of the present study, along with the simple predictive linear models developed and validated using the new image-derived indices, could improve the precision of the image-based MGW estimations, and consequently facilitate wheat breeding and physiological assessments." @default.
- W3207003666 created "2021-10-25" @default.
- W3207003666 creator A5002033083 @default.
- W3207003666 creator A5042277043 @default.
- W3207003666 creator A5079167590 @default.
- W3207003666 date "2021-11-29" @default.
- W3207003666 modified "2023-10-14" @default.
- W3207003666 title "Wheat Grain Width: A Clue for Re-Exploring Visual Indicators of Grain Weight" @default.
- W3207003666 cites W1968498907 @default.
- W3207003666 cites W1984191586 @default.
- W3207003666 cites W1992662597 @default.
- W3207003666 cites W2029442236 @default.
- W3207003666 cites W2030548132 @default.
- W3207003666 cites W2034103034 @default.
- W3207003666 cites W2056318090 @default.
- W3207003666 cites W2063427034 @default.
- W3207003666 cites W2075063973 @default.
- W3207003666 cites W2109795852 @default.
- W3207003666 cites W2144795185 @default.
- W3207003666 cites W2167279371 @default.
- W3207003666 cites W2620951361 @default.
- W3207003666 cites W2789833233 @default.
- W3207003666 cites W2965802655 @default.
- W3207003666 cites W3004545209 @default.
- W3207003666 cites W3094852424 @default.
- W3207003666 cites W3128524621 @default.
- W3207003666 doi "https://doi.org/10.21203/rs.3.rs-1098292/v1" @default.
- W3207003666 hasPublicationYear "2021" @default.
- W3207003666 type Work @default.
- W3207003666 sameAs 3207003666 @default.
- W3207003666 citedByCount "0" @default.
- W3207003666 crossrefType "posted-content" @default.
- W3207003666 hasAuthorship W3207003666A5002033083 @default.
- W3207003666 hasAuthorship W3207003666A5042277043 @default.
- W3207003666 hasAuthorship W3207003666A5079167590 @default.
- W3207003666 hasBestOaLocation W32070036661 @default.
- W3207003666 hasConcept C105795698 @default.
- W3207003666 hasConcept C2524010 @default.
- W3207003666 hasConcept C33923547 @default.
- W3207003666 hasConcept C98503990 @default.
- W3207003666 hasConceptScore W3207003666C105795698 @default.
- W3207003666 hasConceptScore W3207003666C2524010 @default.
- W3207003666 hasConceptScore W3207003666C33923547 @default.
- W3207003666 hasConceptScore W3207003666C98503990 @default.
- W3207003666 hasLocation W32070036661 @default.
- W3207003666 hasLocation W32070036662 @default.
- W3207003666 hasOpenAccess W3207003666 @default.
- W3207003666 hasPrimaryLocation W32070036661 @default.
- W3207003666 hasRelatedWork W1592683135 @default.
- W3207003666 hasRelatedWork W2040248213 @default.
- W3207003666 hasRelatedWork W2043099224 @default.
- W3207003666 hasRelatedWork W2051221975 @default.
- W3207003666 hasRelatedWork W2119158312 @default.
- W3207003666 hasRelatedWork W2330407128 @default.
- W3207003666 hasRelatedWork W2969635709 @default.
- W3207003666 hasRelatedWork W3037784022 @default.
- W3207003666 hasRelatedWork W4229963900 @default.
- W3207003666 hasRelatedWork W4234996786 @default.
- W3207003666 isParatext "false" @default.
- W3207003666 isRetracted "false" @default.
- W3207003666 magId "3207003666" @default.
- W3207003666 workType "article" @default.