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

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 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.

Response of Bread Wheat Cultivars to Terminal Water Stress and Cytokinin Application from a Grain Phenotyping Perspective

“…Images were taken using an exclusively designed laboratory system (Visual Grain Analyzer, VGA; see Haghshenas et al (2022) [23], which was equipped with a glass table with a 60 × 60 cm flicker-free white LED panel light source from underneath. Images with dimensions of 6960 × 4640 pixels (about 33 MP) were captured in natural lighting conditions, from 50 cm above the samples (measured from the lens to the table).…”

“…The image processing and calculations described in the present study were carried out using the Visual Grain Analyzer (VGA v. 1.0.1), an easy-to-use ImageJ macro developed by Haghshenas et al ( 2022) [23]. The macro is available on GitHub at: https://github.com/ haqueshenas/Visual-Grain-Analyzer (accessed on 23 October 2023).…”

“…For a better understanding of the sources of variation in potential GW determination and related aspects to improve yield capacity [5,17,18], numerous imaging approaches, from straightforward 2D indices to sophisticated 3D reconstruction methods, have been used in the study of wheat grain during the past few decades [19][20][21][22][23][24][25][26]. Although 3D images can offer detailed geometric information on wheat grain, the process is laborious, costly, and requires specialized equipment.…”

“…In contrast, 2D grain analysis using an ordinary digital image is a quick and low-cost procedure that can be performed with various equipment, including consumer-grade cameras, scanners, and automated imaging systems. Using this method, a massive number of grains can be evaluated in real-time or almost realtime for scientific and industrial purposes [23]. Grain image analysis is an aspect of high-throughput phenotyping (HTP), which has grown as a powerful tool for analyzing extensive breeding programs.…”

Determination the Wheat Weight and Volume: Mathematical Approach

“…Some wheat grains were spherical whereas others were oval. One of the most often examined factors in wheat breeding and physiology is mean grain weight (MGW) [4]. The examination of wheat grain size and shape is an important indicator of yield [5].…”