Cotton ( spp.), an economically and strategically significant crop in China, faces challenges such as rising cultivation costs and conflicts between grain and cotton cultivation. These challenges underscore the need for enhancing yields per unit area. In response, this study employs deep learning techniques, combined with high-throughput angle detection, to conduct genome-wide association studies (GWAS) on 355 upland cotton accessions, identify key SNPs and candidate genes for plant architecture by fruit branch angle (FBA) influences planting density, yield, and mechanized harvesting. A convolutional neural network (CNN)-based software was developed for rapid and accurate branch angle detection, showing high correlation with both AutoCAD and manual measurements. Significant phenotypic variation in FBA was observed across various cotton planting regions, with the Northwest Inland Region (NIR) exhibiting notably smaller angles. In total, 107 significant Single Nucleotide Polymorphisms (SNPs) were detected across 45 quantitative trait loci (QTL), and three potential candidate genes (, , and ) were identified, providing insights into the genetic basis of FBA and presenting valuable genetic resources for cotton breeding programs.

中文翻译：

---

结合高通量深度学习表型分析和GWAS揭示陆地棉果枝角度遗传变异


棉花作为我国重要的经济和战略作物，面临着种植成本上升、粮棉矛盾等挑战。这些挑战凸显了提高单位面积产量的必要性。为此，本研究采用深度学习技术，结合高通量角度检测，对355个陆地棉种质进行全基因组关联研究（GWAS），通过果枝角度（FBA）识别植物结构的关键SNP和候选基因影响种植密度、产量和机械化收获。开发了基于卷积神经网络 (CNN) 的软件，用于快速、准确地检测分支角度，与 AutoCAD 和手动测量均显示出高度相关性。在不同棉花种植区观察到 FBA 显着的表型变异，其中西北内陆地区 (NIR) 的角度明显较小。总共在 45 个数量性状位点 (QTL) 中检测到 107 个显着单核苷酸多态性 (SNP)，并鉴定了 3 个潜在候选基因 (、、和)，为了解 FBA 的遗传基础提供了见解，并为棉花提供了宝贵的遗传资源。育种计划。