Physiology-informed high-throughput phenotyping of grain moisture dynamics provides enhanced insights into rice grain weight formation
Clicks: 7
ID: 321563
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
1.8
/100
7 views
4 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #325 of 521 articles by views in Plant physiology and biochemistry : PPB
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 521 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Abstract Grain filling is the decisive period for rice grain weight formation. However, traditional static traits fail to capture its complex, non-linear dynamics, while direct panicle weighing is hindered by canopy occlusion. Given the intrinsic synchronization between grain filling and dehydration from anthesis to physiological maturity, monitoring grain moisture content (GMC) dynamics serves as a robust proxy for characterizing the filling process. Here, we propose a high-throughput, physiology-informed phenotyping framework to monitor dehydration. Leveraging a four-year dataset across 135 cultivar-environment combinations, we demonstrate that the GMC threshold for physiological maturity is relatively stable (≈25%). Concurrently, we developed two image-based models for GMC estimation, achieving high accuracies (R2 = 0.82 and 0.86). Integrating this physiological threshold with GMC estimation models enabled the successful reconstruction of the dehydration process. Validation on 26 independent cultivars across two sowing dates predicted physiological maturity with an RMSE of 2.4 to 3.3 days. Traits extracted from these dehydration profiles accounted for 42% of the variance in grain weight, doubling the explanatory power of traditional traits. These gains are largely attributed to a new integrated trait, the moisture maintenance index (MMI), which showed a higher and more stable correlation with thousand-grain weight (r = 0.6). This framework offers a scalable approach for monitoring large-scale dehydration dynamics to deepen our understanding of grain weight formation, facilitating the genetic improvement of the filling process to enhance crop yield.
| Reference Key |
openalex_W7169654204
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | X Wang, Feng Xiao, Zejun Li, Zhenghui Liu, Frédéric Baret, Yanfeng Ding, Chen Zhu, Shouyang Liu |
| Journal | Plant physiology and biochemistry : PPB |
| Year | 2026 |
| DOI |
10.1093/plphys/kiag526
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.