Quantitative evaluation and genome-wide association studies of chrysanthemum flower color DOI

Wenyang Wan,

Feifei Jia, Ziyuan Liu

et al.

Scientia Horticulturae, Journal Year: 2024, Volume and Issue: 338, P. 113561 - 113561

Published: Aug. 18, 2024

Language: Английский

Phenotypic variation and inheritance of leaf weight in cut chrysanthemum DOI

Jialu Sun,

Chao Wen

Euphytica, Journal Year: 2025, Volume and Issue: 221(3)

Published: Feb. 21, 2025

Language: Английский

Citations

0
GWAS reveals the genetic basis and genomic regions underlying four active compounds in chrysanthemum DOI Creative Commons
Xuefeng Zhang,

Xinyi Ning,

Yuhua He

et al.

Horticultural Plant Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

0
Elucidation of the genetic basis of variation in flowering time in Brassica napus via genome-wide association studies and gene coexpression analysis DOI Creative Commons

Hongli Dong,

Shucheng Qi,

Qi Shen

et al.

BMC Plant Biology, Journal Year: 2025, Volume and Issue: 25(1)

Published: March 18, 2025

Language: Английский

Citations

0
Auto-LIA: The Automated Vision-Based Leaf Inclination Angle Measurement System Improves Monitoring of Plant Physiology DOI Creative Commons

Sijun Jiang,

Xingcai Wu,

Qi Wang

et al.

Plant Phenomics, Journal Year: 2024, Volume and Issue: 6

Published: Jan. 1, 2024

Plant sensors are commonly used in agricultural production, landscaping, and other fields to monitor plant growth environmental parameters. As an important basic parameter monitoring, leaf inclination angle (LIA) not only influences light absorption pesticide loss but also contributes genetic analysis phenotypic data collection. The measurements of LIA provide a basis for crop research as well management, such water loss, absorption, illumination radiation. On the one hand, existing efficient solutions, represented by detection ranging (LiDAR), can average distribution plot. labor-intensive schemes hand show high accuracy. However, methods suffer from low automation weak leaf–plant correlation, limiting application individual phenotypes. To improve efficiency measurement correlation between plant, we design image-phenotype-based noninvasive optical sensor system, which combines multi-processes implemented via computer vision technologies RGB images collected physical sensing devices. Specifically, utilize object associate leaves with plants adopt 3-dimensional reconstruction techniques recover spatial information computational space. Then, propose continuity-based segmentation algorithm combined graphical operation implement extraction key points. Finally, seek connection space actual put forward method transformation realize localization recovery Overall, our solution is characterized noninvasiveness, full-process automation, strong enables at cost. In this study, validate Auto-LIA practicality compare accuracy best that acquired expensive invasive LiDAR device. Our demonstrates its competitiveness usability much lower equipment cost, 2. 5° less than widely LiDAR. intelligent processing system signals, provides fully automated LIA, improving monitoring physiological protection. We make code publicly available http://autolia.samlab.cn .

Language: Английский

Citations

3
Multi-locus genome-wide association studies reveal the dynamic genetic architecture of flowering time in chrysanthemum DOI
Jiangshuo Su,

Junwei Zeng,

Siyue Wang

et al.

Plant Cell Reports, Journal Year: 2024, Volume and Issue: 43(4)

Published: March 6, 2024

Language: Английский

Citations

2
Quantitative evaluation and genome-wide association studies of chrysanthemum flower color DOI

Wenyang Wan,

Feifei Jia, Ziyuan Liu

et al.

Scientia Horticulturae, Journal Year: 2024, Volume and Issue: 338, P. 113561 - 113561

Published: Aug. 18, 2024

Language: Английский

Citations

2