Tang Haibao, Kong Wenqian, Nabukalu Pheonah, Lomas Johnathan S, Moser Michel, Zhang Jisen, Jiang Mengwei, Zhang Xingtan, Paterson Andrew H, Yim Won Cheol
Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Haixia Institute of Science and Technology and College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China.
Plant Genome Mapping Laboratory, University of Georgia, Athens, GA, 30605, USA.
Plant Methods. 2024 Sep 12;20(1):140. doi: 10.1186/s13007-024-01268-2.
Phenotyping of plant traits presents a significant bottleneck in Quantitative Trait Loci (QTL) mapping and genome-wide association studies (GWAS). Computerized phenotyping using digital images promises rapid, robust, and reproducible measurements of dimension, shape, and color traits of plant organs, including grain, leaf, and floral traits.
We introduce GRABSEEDS, which is specifically tailored to extract a comprehensive set of features from plant images based on state-of-the-art computer vision and deep learning methods. This command-line enabled tool, which is adept at managing varying light conditions, background disturbances, and overlapping objects, uses digital images to measure plant organ characteristics accurately and efficiently. GRABSEED has advanced features including label recognition and color correction in a batch setting.
GRABSEEDS streamlines the plant phenotyping process and is effective in a variety of seed, floral and leaf trait studies for association with agronomic traits and stress conditions. Source code and documentations for GRABSEEDS are available at: https://github.com/tanghaibao/jcvi/wiki/GRABSEEDS .
植物性状的表型分析是数量性状基因座（QTL）定位和全基因组关联研究（GWAS）中的一个重大瓶颈。使用数字图像进行计算机化表型分析有望对植物器官（包括籽粒、叶片和花部性状）的尺寸、形状和颜色性状进行快速、可靠且可重复的测量。
我们引入了GRABSEEDS，它基于先进的计算机视觉和深度学习方法，专门用于从植物图像中提取一套全面的特征。这个支持命令行的工具擅长处理不同的光照条件、背景干扰和重叠物体，利用数字图像准确高效地测量植物器官特征。GRABSEED具有先进的功能，包括批量设置中的标签识别和颜色校正。
GRABSEEDS简化了植物表型分析过程，在各种种子、花部和叶片性状研究中对于关联农艺性状和胁迫条件是有效的。GRABSEEDS的源代码和文档可在以下网址获取：https://github.com/tanghaibao/jcvi/wiki/GRABSEEDS 。
Zotero 插件
