Predicting Wheat Grain Yield Through Morphometric Analysis of Seed Dimensions Using Computational Imaging Techniques

Abstract

Triticum aestivum L. (Bread wheat) is a vital global staple, necessitating innovative approaches for accurate grain yield prediction. Grain weight, a critical determinant of crop yield, is influenced by genetic and environmental factors as well as agronomic practices and seed morphometric traits. This study aimed to explore the relationship between seed morphometric traits and grain weight in wheat using digital image analysis and statistical methods. Seed traits such as length, breadth, thickness, and area were measured in 122 genotypes, and their impact on grain weight was assessed through multi-linear regression analysis and structural equation modeling. Our results indicated that seed length and thickness were significant predictors of seed weight, with mean length showing the highest standardized effect. Additionally, grain volume, width, and perimeter were essential factors influencing thousand-grain weight, accounting for over 99% of the variation in TGW. Horizontal seed area and perimeter were strong predictors of seed length, while vertical seed area and perimeter predicted seed width. Furthermore, vertical seed circularity, area, and perimeter were significant predictors of thickness. The structural equation model revealed that these factors strongly influence seed weight, with thickness and seed length being the most influential. Digital imaging proved to be an effective, non-destructive, and cost-efficient method for evaluating seed morphology, although challenges like the reliance on external features and the limitations of 2D imaging were noted. To address these issues, the study suggests integrating advanced techniques such as near-infrared spectroscopy and 3D imaging for a more comprehensive analysis. Overall, this research highlights the potential of seed morphometry in wheat breeding programs, emphasizing the importance of size and shape traits for improving grain quality and yield.

Plant phenotyping relevance

小麦種子の形態形質をデジタル画像解析で取得・推定し、収量関連形質との関係を評価することが中心であり、植物フェノタイピング手法の実質的な適用研究に該当する。

Code and data availability

公開状態または取得可能な本文経路を確認できませんでした。

No evidence-backed public reproduction asset is currently recorded.